163 lines
708 KiB
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163 lines
708 KiB
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<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/"><channel><title>My Customized Papers</title><link>https://github.com/your_username/your_repo</link><description>Aggregated research papers</description><language>en-US</language><lastBuildDate>Wed, 14 Jan 2026 06:34:01 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[cond-mat updates on arXiv.org] Chiral Two-Body Bound States from Berry Curvature and Chiral Superconductivity</title><link>https://arxiv.org/abs/2601.08055</link><description>arXiv:2601.08055v1 Announce Type: new
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Abstract: Motivated by the discovery of exotic superconductivity in rhombohedral graphene, we study the two-body problem in electronic bands endowed with Berry curvature and show that it supports chiral, non-$s$-wave bound states with nonzero angular momentum. In the presence of a Fermi sea, these interactions give rise to a chiral pairing problem featuring multiple superconducting phases that break time-reversal symmetry. These phases form a cascade of chiral topological states with different angular momenta, where the order-parameter phase winds by $2\pi m$ around the Fermi surface, with $m = 1,3,5,\ldots$, and the succession of phases is governed by the Berry-curvature flux through the Fermi surface area, $\Phi = b k_F^2/2$. As $\Phi$ increases, the system undergoes a sequence of first-order phase transitions between distinct chiral phases, occurring whenever $\Phi$ crosses integer values. This realizes a quantum-geometry analog of the Little--Parks effect -- oscillations in $T_c$ that provide a clear and experimentally accessible hallmark of chiral superconducting order.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 14 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.08055v1</guid></item><item><title>[cond-mat updates on arXiv.org] Symmetry-aware Conditional Generation of Crystal Structures Using Diffusion Models</title><link>https://arxiv.org/abs/2601.08115</link><description>arXiv:2601.08115v1 Announce Type: new
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Abstract: The application of generative models in crystal structure prediction (CSP) has gained significant attention. Conditional generation--particularly the generation of crystal structures with specified stability or other physical properties has been actively researched for material discovery purposes. Meanwhile, the generative models capable of symmetry-aware generation are also under active development, because space group symmetry has a strong relationship with the physical properties of materials. In this study, we demonstrate that the symmetry control in the previous conditional crystal generation model may not be sufficiently effective when space group constraints are applied as a condition. To address this problem, we propose the WyckoffDiff-Adaptor, which embeds conditional generation within a WyckoffDiff architecture that effectively diffuses Wyckoff positions to achieve precise symmetry control. We successfully generated formation energy phase diagrams while specifying stable structures of particular combination of elements, such as Li--O and Ti--O systems, while simultaneously preserving the symmetry of the input conditions. The proposed method with symmetry-aware conditional generation demonstrates promising results as an effective approach to achieving the discovery of novel materials with targeted physical properties.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 14 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.08115v1</guid></item><item><title>[cond-mat updates on arXiv.org] A microscopic origin for the breakdown of the Stokes Einstein relation in ion transport</title><link>https://arxiv.org/abs/2601.08309</link><description>arXiv:2601.08309v1 Announce Type: new
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Abstract: Ion transport underlies the operation of biological ion channels and governs the performance of electrochemical energy-storage devices. A long-standing anomaly is that smaller alkali metal ions, such as Li$^+$, migrate more slowly in water than larger ions, in apparent violation of the Stokes-Einstein relation. This breakdown is conventionally attributed to dielectric friction, a collective drag force arising from electrostatic interactions between a drifting ion and its surrounding solvent. Here, combining nanopore transport measurements over electric fields spanning several orders of magnitude with molecular dynamics simulations, we show that the time-averaged electrostatic force on a migrating ion is not a drag force but a net driving force. By contrasting charged ions with neutral particles, we reveal that ionic charge introduces additional Lorentzian peaks in the frequency-dependent friction coefficient. These peaks originate predominantly from short-range Lennard-Jones (LJ) interactions within the first hydration layer and represent additional channels for energy dissipation, strongest for Li$^+$ and progressively weaker for Na$^+$ and K$^+$. Our results demonstrate that electrostatic interactions primarily act to tighten the local hydration structure, thereby amplifying short-range LJ interactions rather than directly opposing ion motion. This microscopic mechanism provides a unified physical explanation for the breakdown of the Stokes-Einstein relation in aqueous ion transport.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 14 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.08309v1</guid></item><item><title>[cond-mat updates on arXiv.org] DataScribe: An AI-Native, Policy-Aligned Web Platform for Multi-Objective Materials Design and Discovery</title><link>https://arxiv.org/abs/2601.07966</link><description>arXiv:2601.07966v1 Announce Type: cross
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Abstract: The acceleration of materials discovery requires digital platforms that go beyond data repositories to embed learning, optimization, and decision-making directly into research workflows. We introduce DataScribe, an AI-native, cloud-based materials discovery platform that unifies heterogeneous experimental and computational data through ontology-backed ingestion and machine-actionable knowledge graphs. The platform integrates FAIR-compliant metadata capture, schema and unit harmonization, uncertainty-aware surrogate modeling, and native multi-objective multi-fidelity Bayesian optimization, enabling closed-loop propose-measure-learn workflows across experimental and computational pipelines. DataScribe functions as an application-layer intelligence stack, coupling data governance, optimization, and explainability rather than treating them as downstream add-ons. We validate the platform through case studies in electrochemical materials and high-entropy alloys, demonstrating end-to-end data fusion, real-time optimization, and reproducible exploration of multi-objective trade spaces. By embedding optimization engines, machine learning, and unified access to public and private scientific data directly within the data infrastructure, and by supporting open, free use for academic and non-profit researchers, DataScribe functions as a general-purpose application-layer backbone for laboratories of any scale, including self-driving laboratories and geographically distributed materials acceleration platforms, with built-in support for performance, sustainability, and supply-chain-aware objectives.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 14 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.07966v1</guid></item><item><title>[cond-mat updates on arXiv.org] Kinetic Flux Equations for Ion Exchange in Silicate Glasses</title><link>https://arxiv.org/abs/2601.03207</link><description>arXiv:2601.03207v4 Announce Type: replace
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Abstract: Ion exchange kinetic flux equations have been extensively investigated since the mid-twentieth century and continue to provide a fundamental framework for describing mass transport phenomena in solid materials. Despite the maturity of this field, inconsistencies remain in the literature concerning the definition, dimensional consistency, and physical interpretation of the parameters involved. A rigorous and unified treatment of these equations is therefore essential to ensure the reproducibility and comparability of theoretical and experimental studies. The present study aims to establish a coherent and systematic development of ion exchange kinetic flux equations, with particular emphasis on the consistent definition and dimensional formulation of the relevant physical quantities. Beyond refining the theoretical foundations, this study extends the classical formulation by incorporating the influence of mechanical stress on ion transport and considering cross-term interactions within the framework of linear irreversible thermodynamics. This study investigates ion-exchange kinetics within silicate glasses, operating under the Nernst-Planck binary interdiffusion regime. These developments provide a more comprehensive description of ion exchange kinetics, particularly as applied to silicate glasses, where coupling between chemical and mechanical effects plays a crucial role in determining transport behavior and performance.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 14 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.03207v4</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Comparative LCA of energy and environmental impacts in sulfide-based all-solid-state battery manufacturing: Wet vs. dry processes</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X26000708?dgcid=rss_sd_all</link><description><p>Publication date: 10 March 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 150</p><p>Author(s): Jiachen Xu, Tao Feng, Wei Guo, Jun Wu, Liurong Shi, Lin Hua, Ziwei Wang</p></description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 13 Jan 2026 18:30:44 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X26000708</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Multiscale modeling for all-solid-state batteries: An investigation on electro-chemo-thermo-mechanical degradation</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25050091?dgcid=rss_sd_all</link><description><p>Publication date: 10 March 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 150</p><p>Author(s): Kejie Wang, Zhipeng Chen, Fenghui Wang, Xiang Zhao</p></description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 13 Jan 2026 18:30:44 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25050091</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Self-assembled non-flammable poly(arylene ether sulfone)-grafted poly(ethylene glycol) solid electrolyte with improved lithium-ion transport for lithium–sulfur batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25050492?dgcid=rss_sd_all</link><description><p>Publication date: 10 March 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 150</p><p>Author(s): Anh Le Mong, Thi Cam Thach To, Thuy An Trinh, Dukjoon Kim</p></description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 13 Jan 2026 18:30:44 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25050492</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Solution-processed poly(vinylidene difluoride)-cellulose acetate/Na<sub>1+x</sub>Al<sub>x</sub>Ti<sub>2-x</sub>(PO<sub>4</sub>)<sub>3</sub> composite quasi-solid electrolyte for safe and high-performance quasi-solid-state sodium-ion batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X26000757?dgcid=rss_sd_all</link><description><p>Publication date: 10 March 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 150</p><p>Author(s): Yi-Hung Liu, Pei-Xuan Chen, Yen-Shen Kuo, Yi-Yu Chiang, Meng-Lun Lee, Torng Jinn Lee</p></description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 13 Jan 2026 18:30:44 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X26000757</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Computational insights into the superionic behavior of amorphous lithium oxyhalide 1.6Li<sub>2</sub>O-TaCl<sub>5</sub> solid electrolyte</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25050455?dgcid=rss_sd_all</link><description><p>Publication date: 10 March 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 150</p><p>Author(s): Adil Saleem, Junquan Ou, Leon L. Shaw, Bushra Jabar, Mehwish Khalid Butt</p></description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 13 Jan 2026 18:30:44 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25050455</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Enhancement of ion transport in Li<sub>3</sub>InCl<sub>6</sub> solid electrolyte by in-rich strategy</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X26000770?dgcid=rss_sd_all</link><description><p>Publication date: 10 March 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 150</p><p>Author(s): Bo Li, Lei Xian, Fu-Jie Zhao, Zu-Tao Pan, Ling-Bin Kong</p></description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 13 Jan 2026 18:30:44 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X26000770</guid></item><item><title>[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Anharmonicity-Driven Modulation of Carrier Lifetime and Mobility in BF4-Doped All-Inorganic CsPbX3 (X = I, Br) Perovskites</title><link>http://dx.doi.org/10.1021/acs.jpclett.5c03817</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpclett.5c03817/asset/images/medium/jz5c03817_0006.gif" /></p><div><cite>The Journal of Physical Chemistry Letters</cite></div><div>DOI: 10.1021/acs.jpclett.5c03817</div></description><author>The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)</author><pubDate>Tue, 13 Jan 2026 13:12:44 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpclett.5c03817</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Machine Learning Driven Window Blinds Inspired Porous Carbon‐Based Flake for Ultra‐Broadband Electromagnetic Wave Absorption</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202521130?af=R</link><description>Advanced Science, EarlyView.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Tue, 13 Jan 2026 08:11:16 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202521130</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Toward Robust Ionic Conductivity Determination of Sulfide‐Based Solid Electrolytes for Solid‐State Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202509479?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 4, 12 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Tue, 13 Jan 2026 07:18:05 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202509479</guid></item><item><title>[cond-mat updates on arXiv.org] Mechanisms of alkali ionic transport in amorphous oxyhalides solid state conductors</title><link>https://arxiv.org/abs/2601.06384</link><description>arXiv:2601.06384v1 Announce Type: new
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Abstract: Amorphous oxyhalides have attracted significant attention due to their relatively high ionic conductivity ($>$1 mS cm$^{-1}$), excellent chemical stability, mechanical softness, and facile synthesis routes via standard solid-state reactions. These materials exhibit an ionic conductivity that is almost independent of the underlying chemistry, in stark contrast to what occurs in crystalline conductors. In this work, we employ an accurately fine-tuned machine learning interatomic potential to construct large-scale molecular dynamics trajectories encompassing hundreds of nanoseconds to obtain statistically converged transport properties. We find that the amorphous state consists of chain fragments of metal-anion tetrahedra of various lenght. By analyzing the residence time of alkali cations migrating around tetrahedrally-coordinated trivalent metal ions, we find that oxygen anions on the metal-anion tetrahedra limit alkali diffusion. By computing the full Einstein expression of the ionic conductivity, we demonstrate that the alkali transference number of these materials is strongly influenced by distinct-particles correlations, while at the same time they are characterized by an alkali Haven ratio close to one, implying that ionic transport is largely dictated by uncorrelated self-diffusion. Finally, by extending this analysis to chemical compositions $AMX_{2.5}\textsf{O}_{0.75}$, spanning different alkaline ($A$ = Li, Na, K), metallic ($M$ = Al, Ga, In), and halogen ($X$ = Cl, Br, I) species, we clarify why the diffusion properties of these materials remain largely insensitive to variations in atomic chemistry.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.06384v1</guid></item><item><title>[cond-mat updates on arXiv.org] Beyond Predicted ZT: Machine Learning Strategies for the Experimental Discovery of Thermoelectric Materials</title><link>https://arxiv.org/abs/2601.06571</link><description>arXiv:2601.06571v1 Announce Type: new
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Abstract: The discovery of high-performance thermoelectric (TE) materials for advancing green energy harvesting from waste heat is an urgent need in the context of looming energy crisis and climate change. The rapid advancement of machine learning (ML) has accelerated the design of thermoelectric (TE) materials, yet a persistent "gap" remains between high-accuracy computational predictions and their successful experimental validation. While ML models frequently report impressive test scores (R^2 values of 0.90-0.98) for complex TE properties (zT, power factor, and electrical/thermal conductivity), only a handful of these predictions have culminated in the experimental discovery of new high-zT materials. In this review, we identify and discuss that the primary obstacles are poor model generalizability-stemming from the "small-data" problem, sampling biases in cross-validation, and inadequate structural representation-alongside the critical challenge of thermodynamic phase stability. Moreover, we argue that standard randomized validation often overestimates model performance by ignoring "hidden hierarchies" and clustering within chemical families. Finally, to bridge this gap between ML-predictions and experimental realization, we advocate for advanced validation strategies like PCA-based sampling and a synergetic active learning loop that integrates ML "fast filters" for stability (e.g., GNoME) with high-throughput combinatorial thin-film synthesis to rapidly map stable, high-zT compositional spaces.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.06571v1</guid></item><item><title>[cond-mat updates on arXiv.org] Altermagnetism-driven FFLO superconductivity in finite-filling 2D lattices</title><link>https://arxiv.org/abs/2601.06735</link><description>arXiv:2601.06735v1 Announce Type: new
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Abstract: We systematically investigate the emergence of finite-momentum Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) superconductivity in a square lattice Hubbard model with finite filling, driven by either $d_{xy}$-wave or $d_{x^{2}-y^{2}}$-wave altermagnetic order in the presence of on-site $s$-wave attractive interactions. Our study combines mean-field calculation in the superconducting phase with pairing instability analysis of the normal state, incorporating the next-nearest-neighbor hopping in the single-particle dispersion relation. We demonstrate that the two types of altermagnetism have markedly different impacts on the stabilization of FFLO states. Specifically, $d_{xy}$-wave altermagnetism supports FFLO superconductivity over a broad parameter regime at low fillings, whereas $d_{x^{2}-y^{2}}$-wave altermagnetism only induces FFLO pairing in a narrow range at high fillings. Furthermore, we find that the presence of a Van Hove singularity in the density of states tends to suppress FFLO superconductivity. These findings may provide guidance for experimental exploration of altermagnetism-induced FFLO states in real materials with more complex electronic structures.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.06735v1</guid></item><item><title>[cond-mat updates on arXiv.org] Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery</title><link>https://arxiv.org/abs/2601.06820</link><description>arXiv:2601.06820v1 Announce Type: new
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Abstract: Efficient exploration of vast compositional and processing spaces is essential for accelerated materials discovery. Bayesian optimization (BO) provides a principled strategy for identifying optimal materials with minimal experiments, yet its adoption in materials science is hindered by implementation complexity and limited domain-specific tools. Here, we present Bgolearn, a comprehensive Python framework that makes BO accessible and practical for materials research through an intuitive interface, robust algorithms, and materials-oriented workflows. Bgolearn supports both single-objective and multi-objective Bayesian optimization with multiple acquisition functions (e.g., expected improvement, upper confidence bound, probability of improvement, and expected hypervolume improvement etc.), diverse surrogate models (including Gaussian processes, random forests, and gradient boosting etc.), and bootstrap-based uncertainty quantification. Benchmark studies show that Bgolearn reduces the number of required experiments by 40-60% compared with random search, grid search, and genetic algorithms, while maintaining comparable or superior solution quality. Its effectiveness is demonstrated not only through the studies presented in this paper, such as the identification of maximum-elastic-modulus triply periodic minimal surface structures, ultra-high-hardness high-entropy alloys, and high-strength, high-ductility medium-Mn steels, but also by numerous publications that have proven its impact in material discovery. With a modular architecture that integrates seamlessly into existing materials workflows and a graphical user interface (BgoFace) that removes programming barriers, Bgolearn establishes a practical and reliable platform for Bayesian optimization in materials science, and is openly available at https://github.com/Bin-Cao/Bgolearn.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.06820v1</guid></item><item><title>[cond-mat updates on arXiv.org] Nanoindentation induced plasticity in equiatomic MoTaW alloys by experimentally guided machine learning molecular dynamics simulations</title><link>https://arxiv.org/abs/2601.06846</link><description>arXiv:2601.06846v1 Announce Type: new
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Abstract: Refractory complex concentrated alloys (RCCA) exhibit exceptional strength and thermal stability, yet their plastic deformation mechanisms under complex contact loading remain insufficiently understood. Here, the nanoindentation response of an equiatomic MoTaW alloy is investigated through a combined experimental and atomistically resolved modeling approach. Spherical nanoindentation experiments are coupled with large scale molecular dynamics simulations employing a tabulated low dimensional Gaussian Approximation Potential (tabGAP), enabling near DFT accuracy. A physics based similarity criterion, implemented via PCA of load-displacement curves, is used to identify mechanically representative experimental responses for quantitative comparison with simulations. Indentation stress-strain curves are constructed yielding excellent agreement in the elastic regime between experiment and simulation, with reduced Young's moduli of approximately 270 GPa. Generalized stacking fault energy calculations reveal elevated unstable stacking- and twinning-fault energies in MoTaW relative to pure refractory elements, indicating suppressed localized shear and a preference for dislocation-mediated plasticity. Atomistic analyses demonstrate a strong crystallographic dependence of plastic deformation, with symmetric {110}<111> slip activation and four-fold rosette pile-ups for the [001] orientation, and anisotropic slip, strain localization, and enhanced junction formation for [011]. Local entropy and polyhedral template matching analyses further elucidate dislocation network evolution and deformation-induced local structural transformations. Overall, this study establishes a direct mechanistic link between fault energetics, orientation-dependent dislocation activity, and experimentally observed nanoindentation behavior in RCCA.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.06846v1</guid></item><item><title>[cond-mat updates on arXiv.org] A survey of active learning in materials science: Data-driven paradigm for accelerating the research pipeline</title><link>https://arxiv.org/abs/2601.06971</link><description>arXiv:2601.06971v1 Announce Type: new
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Abstract: The exploration of materials composition, structure, and processing spaces is constrained by high dimensionality and the cost of data acquisition. While machine learning has supported property prediction and design, its effectiveness depends on labeled data, which remains expensive to generate via experiments or high-fidelity simulations. Improving data efficiency is thus a central concern in materials informatics.
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Active learning (AL) addresses this by coupling model training with adaptive data acquisition. Instead of static datasets, AL iteratively prioritizes candidates based on uncertainty, diversity, or task-specific objectives. By guiding data collection under limited budgets, AL offers a structured approach to decision-making, complementing physical insight with quantitative measures of informativeness.
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Recently, AL has been applied to computational simulation, structure optimization, and autonomous experimentation. However, the diversity of AL formulations has led to fragmented methodologies and inconsistent assessments. This Review provides a concise overview of AL methods in materials science, focusing on their role in improving data efficiency under realistic constraints. We summarize key methodological principles, representative applications, and persistent challenges, aiming to clarify the scope and limitations of AL as a practical tool within contemporary materials informatics.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.06971v1</guid></item><item><title>[cond-mat updates on arXiv.org] Case study of an exploratory high voltage NASICON-based Na$_4$NiCr(PO$_4$)$_3$ cathode material for sodium-ion batteries</title><link>https://arxiv.org/abs/2601.07012</link><description>arXiv:2601.07012v1 Announce Type: new
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Abstract: We examine a new NASICON-type Na$_4$NiCr(PO$_4$)$_3$ material designed for high-voltage and multi-electron reactions for the sodium-ion batteries (SIBs). The Rietveld refinement of the X-ray diffraction pattern, using the R$\bar{3}$c space group, confirmed the stabilization of the rhombohedral NASICON framework. Furthermore, the Raman and Fourier transform infrared spectroscopy are employed to probe the structure and chemical bonding. The core-level photoemission analysis reveals the Cr$^{3+}$ and mixed Ni$^{2+}$/Ni$^{3+}$ oxidation states in the sample. Moreover, the bond valence energy landscape (BVEL) analysis, based on the refined structure, revealed a three-dimensional network of well-connected sodium sites with a migration energy barrier of 0.468 eV. The material delivered a good charge capacity at around 4.5 V, but showed no sodium-ion intercalation during discharge, resulting in negligible discharge capacity. The post-mortem analysis confirmed that the crystal structure remained intact. The calculated energy barrier values indicated a reversal in sodium site stability after cycling, though the barriers can still permit feasible ion migration. This suggests that ion transport alone cannot explain the lack of reversibility, which likely arises from intrinsically poor electronic conductivity. These findings highlight key challenges in achieving stable, reversible capacity in this system and underscore the need for doping, structural modification, and electrolyte optimization to realize its full potential as a high-voltage SIB cathode.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.07012v1</guid></item><item><title>[cond-mat updates on arXiv.org] Observation of Time-Reversal Symmetry Breaking in the Type-I Superconductor YbSb$_2$</title><link>https://arxiv.org/abs/2601.07460</link><description>arXiv:2601.07460v1 Announce Type: new
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Abstract: The spontaneous breaking of time-reversal symmetry is a hallmark of unconventional superconductivity, typically observed in type-II superconductors. Here, we report evidence of time-reversal symmetry breaking in the type-I superconductor YbSb$_2$. Zero-field $\mu$SR measurements reveal spontaneous internal magnetic fields emerging just below the superconducting transition, while transverse-field $\mu$SR confirms a fully gapped type-I superconducting state. Our first-principles calculations identify YbSb$_2$ as a ${\mathbb Z}_2$ topological metal hosting a Dirac nodal line near the Fermi level. Symmetry analysis within the Ginzburg Landau framework indicates an internally antisymmetric nonunitary triplet (INT) state as the most probable superconducting ground state. Calculations based on an effective low-energy model further demonstrate that this INT state hosts gapless Majorana surface modes, establishing YbSb$_2$ as a topological superconductor. Our results highlight YbSb$_2$ as a unique material platform where type-I superconductivity coexists with triplet-pairing and nontrivial topology.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.07460v1</guid></item><item><title>[cond-mat updates on arXiv.org] Machine learning nonequilibrium phase transitions in charge-density wave insulators</title><link>https://arxiv.org/abs/2601.07583</link><description>arXiv:2601.07583v1 Announce Type: new
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Abstract: Nonequilibrium electronic forces play a central role in voltage-driven phase transitions but are notoriously expensive to evaluate in dynamical simulations. Here we develop a machine learning framework for adiabatic lattice dynamics coupled to nonequilibrium electrons, and demonstrate it for a gating induced insulator to metal transition out of a charge density wave state in the Holstein model. Although exact electronic forces can be obtained from nonequilibrium Green's function (NEGF) calculations, their high computational cost renders long time dynamical simulations prohibitively expensive. By exploiting the locality of the electronic response, we train a neural network to directly predict instantaneous local electronic forces from the lattice configuration, thereby bypassing repeated NEGF calculations during time evolution. When combined with Brownian dynamics, the resulting machine learning force field quantitatively reproduces domain wall motion and nonequilibrium phase transition dynamics obtained from full NEGF simulations, while achieving orders of magnitude gains in computational efficiency. Our results establish direct force learning as an efficient and accurate approach for simulating nonequilibrium lattice dynamics in driven quantum materials.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.07583v1</guid></item><item><title>[cond-mat updates on arXiv.org] PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials</title><link>https://arxiv.org/abs/2601.07742</link><description>arXiv:2601.07742v1 Announce Type: new
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Abstract: Many materials properties depend on higher-order derivatives of the potential energy surface, yet machine learned interatomic potentials (MLIPs) trained with standard a standard loss on energy, force, and stress errors can exhibit error in curvature, degrading the prediction of vibrational properties. We introduce phonon fine-tuning (PFT), which directly supervises second-order force constants of materials by matching MLIP energy Hessians to DFT-computed force constants from finite displacement phonon calculations. To scale to large supercells, PFT stochastically samples Hessian columns and computes the loss with a single Hessian-vector product. We also use a simple co-training scheme to incorporate upstream data to mitigate catastrophic forgetting. On the MDR Phonon benchmark, PFT improves Nequix MP (trained on Materials Project) by 55% on average across phonon thermodynamic properties and achieves state-of-the-art performance among models trained on Materials Project trajectories. PFT also generalizes to improve properties beyond second-derivatives, improving thermal conductivity predictions that rely on third-order derivatives of the potential energy.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.07742v1</guid></item><item><title>[cond-mat updates on arXiv.org] Quantum algorithm for dephasing of coupled systems: decoupling and IQP duality</title><link>https://arxiv.org/abs/2601.06298</link><description>arXiv:2601.06298v1 Announce Type: cross
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Abstract: Noise and decoherence are ubiquitous in the dynamics of quantum systems coupled to an external environment. In the regime where environmental correlations decay rapidly, the evolution of a subsytem is well described by a Lindblad quantum master equation. In this work, we introduce a quantum algorithm for simulating unital Lindbladian dynamics by sampling unitary quantum channels without extra ancillas. Using ancillary qubits we show that this algorithm allows approximating general Lindbladians as well. For interacting dephasing Lindbladians coupling two subsystems, we develop a decoupling scheme that reduces the circuit complexity of the simulation. This is achieved by sampling from a time-correlated probability distribution - determined by the evolution of one subsystem, which specifies the stochastic circuit implemented on the complementary subsystem. We demonstrate our approach by studying a model of bosons coupled to fermions via dephasing, which naturally arises from anharmonic effects in an electron-phonon system coupled to a bath. Our method enables tracing out the bosonic degrees of freedom, reducing part of the dynamics to sampling an instantaneous quantum polynomial (IQP) circuit. The sampled bitstrings then define a corresponding fermionic problem, which in the non-interacting case can be solved efficiently classically. We comment on the computational complexity of this class of dissipative problems, using the known fact that sampling from IQP circuits is believed to be difficult classically.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.06298v1</guid></item><item><title>[cond-mat updates on arXiv.org] Physics-Informed Tree Search for High-Dimensional Computational Design</title><link>https://arxiv.org/abs/2601.06444</link><description>arXiv:2601.06444v1 Announce Type: cross
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Abstract: High-dimensional design spaces underpin a wide range of physics-based modeling and computational design tasks in science and engineering. These problems are commonly formulated as constrained black-box searches over rugged objective landscapes, where function evaluations are expensive, and gradients are unavailable or unreliable. Conventional global search engines and optimizers struggle in such settings due to the exponential scaling of design spaces, the presence of multiple local basins, and the absence of physical guidance in sampling. We present a physics-informed Monte Carlo Tree Search (MCTS) framework that extends policy-driven tree-based reinforcement concepts to continuous, high-dimensional scientific optimization. Our method integrates population-level decision trees with surrogate-guided directional sampling, reward shaping, and hierarchical switching between global exploration and local exploitation. These ingredients allow efficient traversal of non-convex, multimodal landscapes where physically meaningful optima are sparse. We benchmark our approach against standard global optimization baselines on a suite of canonical test functions, demonstrating superior or comparable performance in terms of convergence, robustness, and generalization. Beyond synthetic tests, we demonstrate physics-consistent applicability to (i) crystal structure optimization from clusters to bulk, (ii) fitting of classical interatomic potentials, and (iii) constrained engineering design problems. Across all cases, the method converges with high fidelity and evaluation efficiency while preserving physical constraints. Overall, our work establishes physics-informed tree search as a scalable and interpretable paradigm for computational design and high-dimensional scientific optimization, bridging discrete decision-making frameworks with continuous search in scientific design workflows.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.06444v1</guid></item><item><title>[cond-mat updates on arXiv.org] Cation Dominated but Negatively Charged Na2SO4,aq-Graphene Interfaces</title><link>https://arxiv.org/abs/2601.06995</link><description>arXiv:2601.06995v1 Announce Type: cross
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Abstract: The distribution of ions and their impact on the structure of electrolyte interfaces plays an important role in many applications. Interestingly, recent experimental studies have suggested the preferential accumulation of $SO_4^{2-}$ ions at the $Na_2SO_{4,aq}$-graphene interface in disagreement with the generally known tendency of cations to accumulate at graphene-electrolyte interfaces. Herein, we resolve the atomistic structure of the $Na_2SO_{4,aq}$-graphene interfaces in the 0.1-2.0 M concentration range using machine learning interatomic potential-based simulations and simulated sum frequency generation (SFG) spectra to reveal the molecular origins of the conundrum. Our results show that Na+ ions accumulate between the outermost and second water layers whereas $SO_4^{2-}$ ions accumulate within the second interfacial water layer indicating cation dominated interfaces. We find that the interfacial region (within ~10 ${\AA}$ of the graphene sheet) is negatively charged due to sub-stoichiometric $Na^+$/$SO_4^{2-}$ ratio at the interface. Our simulated SFG spectra show enhancement and a red-shift of the spectra in the hydrogen bonded region as a function of $Na_2SO_4$ concentration similar to measurements due to $SO_4^{2-}$-induced changes in the orientational order of water molecules in the second interfacial layer. Our study demonstrates that ion stratification and ion-induced water reorganization are key elements of understanding the electrolyte-graphene interface.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.06995v1</guid></item><item><title>[cond-mat updates on arXiv.org] The Impact of Ionic Anharmonicity on Superconductivity in Metal-Stuffed B-C Clathrates</title><link>https://arxiv.org/abs/2501.12068</link><description>arXiv:2501.12068v2 Announce Type: replace
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Abstract: Metal-stuffed B$-$C compounds with sodalite clathrate structure have captured increasing attention due to their predicted exceptional superconductivity above liquid nitrogen temperature at ambient pressure. However, by neglecting the quantum lattice anharmonicity, the existing studies may result in an incomplete understanding of such a lightweight system. Here, using state-of-the-art ab initio methods incorporating quantum effects and machine learning potentials, we revisit the properties of a series of $XY$$\text{B}_{6}\text{C}_{6}$ clathrates where $X$ and $Y$ are metals. Our findings show that ionic quantum and anharmonic effects can harden the $E_g$ and $E_u$ vibrational modes, enabling the dynamical stability of 15 materials previously considered unstable in the harmonic approximation, including materials with previously unreported ($XY$)$^{1+}$ state, which is demonstrated here to be crucial to reach high critical temperatures. Further calculations based on the anisotropic Migdal-Eliashberg equation demonstrate that the $T_\text{c}$ values for KRb$\text{B}_{6}\text{C}_{6}$ and Rb$\text{B}_{3}\text{C}_{3}$ among these stabilized compounds are 102 and 115 K at 0 and 15 GPa, respectively, both being higher than $T_\text{c}$ of 92 K of KPb$\text{B}_{6}\text{C}_{6}$ at the anharmonic level. These record-high $T_\text{c}$ values, surpassing liquid nitrogen temperatures, emphasize the importance of anharmonic effects in stabilizing B-C clathrates with large electron-phonon coupling strength and advancing the search for high-$T_\text{c}$ superconductivity at (near) ambient pressure.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2501.12068v2</guid></item><item><title>[cond-mat updates on arXiv.org] Microscopic theory of phonon polaritons and long wavelength dielectric response</title><link>https://arxiv.org/abs/2505.03915</link><description>arXiv:2505.03915v2 Announce Type: replace
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Abstract: We present a first-principles approach for calculating phonon-polariton dispersion relations. In this approach, phonon-photon interaction is described by quantization of a Hamiltonian that describes harmonic lattice vibrations coupled with the electromagnetic field inside the material. All Hamiltonian parameters are obtained from first-principles calculations, with diagonalization leading to non-interacting polariton quasiparticles. This method naturally includes retardation effects and resolves non-analytical behavior and ambiguities in phonon frequencies at the Brillouin zone center, especially in non-cubic and optically anisotropic materials. Furthermore, by incorporating higher-order terms in the Hamiltonian, we also account for quasiparticle interactions and spectral broadening. Specifically, we show how anharmonic effects in phonon polaritons lead to a dielectric response that challenges traditional models. The accuracy and consequences of the approach are demonstrated on GaP and GaN as harmonic test systems and PbTe and $\beta$-Ga$_2$O$_3$ as anharmonic test systems.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2505.03915v2</guid></item><item><title>[cond-mat updates on arXiv.org] Machine Learning Interatomic Potentials Enable Molecular Dynamics Simulations of Doped MoS2</title><link>https://arxiv.org/abs/2510.05339</link><description>arXiv:2510.05339v2 Announce Type: replace
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Abstract: We present the first computational framework for molecular dynamics simulation of MoS2 doped with 25 elements spanning metals, non-metals, and transition metals using Meta's Universal Model for Atoms machine learning interatomic potential (MLIP). Benchmarking against density functional theory calculations demonstrates the accuracy of the MLIP for simulating doped-MoS2 systems and highlights opportunities for improvement. Using the MLIP, we perform heating-cooling simulations of doped-MoS2 supercells. The simulations capture complex phenomena including dopant clustering, MoS2 layer fracturing, interlayer diffusion, and chemical compound formation at orders-of-magnitude reduced computational cost compared to density functional theory. This work provides an open-source computational workflow for application-oriented design of doped-MoS2, enabling high-throughput screening of dopant candidates and optimization of compositions for targeted tribological, electronic, and optoelectronic performance. The MLIP bridges the accuracy-efficiency gap between first-principles methods and empirical potentials, and the framework offers unprecedented opportunities for large-scale materials discovery in two-dimensional doped material systems.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2510.05339v2</guid></item><item><title>[cond-mat updates on arXiv.org] Kinetic Flux Equations for Ion Exchange in Silicate Glasses</title><link>https://arxiv.org/abs/2601.03207</link><description>arXiv:2601.03207v3 Announce Type: replace
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Abstract: Ion exchange kinetic flux equations have been extensively investigated since the mid-twentieth century and continue to provide a fundamental framework for describing mass transport phenomena in solid materials. Despite the maturity of this field, inconsistencies remain in the literature concerning the definition, dimensional consistency, and physical interpretation of the parameters involved. A rigorous and unified treatment of these equations is therefore essential to ensure the reproducibility and comparability of theoretical and experimental studies. The present study aims to establish a coherent and systematic development of ion exchange kinetic flux equations, with particular emphasis on the consistent definition and dimensional formulation of the relevant physical quantities. Beyond refining the theoretical foundations, this study extends the classical formulation by incorporating the influence of mechanical stress on ion transport and considering cross-term interactions within the framework of linear irreversible thermodynamics. This study investigates ion-exchange kinetics within silicate glasses, operating under the Nernst-Planck binary interdiffusion regime. These developments provide a more comprehensive description of ion exchange kinetics, particularly as applied to silicate glasses, where coupling between chemical and mechanical effects plays a crucial role in determining transport behavior and performance.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.03207v3</guid></item><item><title>[cond-mat updates on arXiv.org] Scalable Dielectric Tensor Predictions for Inorganic Materials using Equivariant Graph Neural Networks</title><link>https://arxiv.org/abs/2601.04755</link><description>arXiv:2601.04755v2 Announce Type: replace
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Abstract: Accurate prediction of dielectric tensors is essential for accelerating the discovery of next-generation inorganic dielectric materials. Existing machine learning approaches, such as equivariant graph neural networks, typically rely on specially-designed network architectures to enforce O(3) equivariance. However, to preserve equivariance, these specially-designed models restrict the update of equivariant features during message passing to linear transformations or gated equivariant nonlinearities. The inability to implicitly characterize more complex nonlinear structures may reduce the predictive accuracy of the model. In this study, we introduce a frame-averaging-based approach to achieve equivariant dielectric tensor prediction. We propose GoeCTP, an O(3)-equivariant framework that predicts dielectric tensors without imposing any structural restrictions on the backbone network. We benchmark its performance against several state-of-the-art models and further employ it for large-scale virtual screening of thermodynamically stable materials from the Materials Project database. GoeCTP successfully identifies various promising candidates, such as Zr(InBr$_3$)$_2$ (band gap $E_g = 2.41$ eV, dielectric constant $\overline{\varepsilon} = 194.72$) and SeI$_2$ (anisotropy ratio $\alpha_r = 96.763$), demonstrating its accuracy and efficiency in accelerating the discovery of advanced inorganic dielectric materials.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.04755v2</guid></item><item><title>[cond-mat updates on arXiv.org] An information-matching approach to optimal experimental design and active learning</title><link>https://arxiv.org/abs/2411.02740</link><description>arXiv:2411.02740v4 Announce Type: replace-cross
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Abstract: The efficacy of mathematical models heavily depends on the quality of the training data, yet collecting sufficient data is often expensive and challenging. Many modeling applications require inferring parameters only as a means to predict other quantities of interest (QoI). Because models often contain many unidentifiable (sloppy) parameters, QoIs often depend on a relatively small number of parameter combinations. Therefore, we introduce an information-matching criterion based on the Fisher Information Matrix to select the most informative training data from a candidate pool. This method ensures that the selected data contain sufficient information to learn only those parameters that are needed to constrain downstream QoIs. It is formulated as a convex optimization problem, making it scalable to large models and datasets. We demonstrate the effectiveness of this approach across various modeling problems in diverse scientific fields, including power systems and underwater acoustics. Finally, we use information-matching as a query function within an Active Learning loop for material science applications. In all these applications, we find that a relatively small set of optimal training data can provide the necessary information for achieving precise predictions. These results are encouraging for diverse future applications, particularly active learning in large machine learning models.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2411.02740v4</guid></item><item><title>[cond-mat updates on arXiv.org] Berezinskii--Kosterlitz--Thouless transition in a context-sensitive random language model</title><link>https://arxiv.org/abs/2412.01212</link><description>arXiv:2412.01212v2 Announce Type: replace-cross
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Abstract: Several power-law critical properties involving different statistics in natural languages -- reminiscent of scaling properties of physical systems at or near phase transitions -- have been documented for decades. The recent rise of large language models has added further evidence and excitement by providing intriguing similarities with notions in physics such as scaling laws and emergent abilities. However, specific instances of classes of generative language models that exhibit phase transitions, as understood by the statistical physics community, are lacking. In this work, inspired by the one-dimensional Potts model in statistical physics, we construct a simple probabilistic language model that falls under the class of context-sensitive grammars, which we call the context-sensitive random language model, and numerically demonstrate an unambiguous phase transition in the framework of a natural language model. We explicitly show that a precisely defined order parameter -- that captures symbol frequency biases in the sentences generated by the language model -- changes from strictly zero to a strictly nonzero value (in the infinite-length limit of sentences), implying a mathematical singularity arising when tuning the parameter of the stochastic language model we consider. Furthermore, we identify the phase transition as a variant of the Berezinskii--Kosterlitz--Thouless (BKT) transition, which is known to exhibit critical properties not only at the transition point but also in the entire phase. This finding leads to the possibility that critical properties in natural languages may not require careful fine-tuning nor self-organized criticality, but are generically explained by the underlying connection between language structures and the BKT phases.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2412.01212v2</guid></item><item><title>[cond-mat updates on arXiv.org] Refractive Index, Its Chromatic Dispersion, and Thermal Coefficients of Four Less Common Glycols</title><link>https://arxiv.org/abs/2504.11819</link><description>arXiv:2504.11819v2 Announce Type: replace-cross
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Abstract: We report comprehensive measurements of the refractive index as a function of wavelength and temperature for four less commonly studied glycols: pentaethylene glycol, hexaethylene glycol, dipropylene glycol (mixture of isomers), and tripropylene glycol. The measurements cover the spectral range of 0.39-1.07 ${\mu}$m and temperatures from 1${\deg}$C to 45${\deg}$C. The data were modeled using a two-pole Sellmeier equation, with temperature dependence expressed through wavelength-dependent thermal coefficients. Four fitting models (Sellmeier and Cauchy) with different numbers of parameters were tested. For pentaethylene glycol, results from all models are shown; for the remaining glycols, only the two-pole Sellmeier fits are presented in tabular form. Thermal coefficient values for six wavelengths of practical importance are also tabulated. Experimental uncertainties in refractive index, wavelength, and temperature were rigorously evaluated and incorporated into the analysis. The influence of sample purity, including residual water content and manufacturer-reported impurities, was assessed and accounted for in the uncertainty estimates. To our knowledge, this is the first dataset to systematically characterize both chromatic dispersion and thermal variation of the refractive index for these glycols over such a broad spectral and temperature range. The validated fitting equations and parameters are suitable for use in optical modeling, materials characterisation, and related applications. All raw data are available in a publicly accessible repository.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2504.11819v2</guid></item><item><title>[cond-mat updates on arXiv.org] TBPLaS 2.0: a Tight-Binding Package for Large-scale Simulation</title><link>https://arxiv.org/abs/2509.26309</link><description>arXiv:2509.26309v2 Announce Type: replace-cross
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Abstract: The common exact diagonalization-based techniques to solving tight-binding models suffer from O(N^2) and O(N^3) scaling with respect to model size in memory and CPU time, hindering their applications in large tight-binding models. On the contrary, the tight-binding propagation method (TBPM) can achieve linear scaling in both memory and CPU time, and is capable of handling large tight-binding models with billions of orbitals. In this paper, we introduce version 2.0 of TBPLaS, a package for large-scale simulation based on TBPM. This new version brings significant improvements with many new features. Existing Python/Cython modeling tools have been thoroughly optimized, and a compatible C++ implementation of the modeling tools is now available, offering efficiency enhancement of several orders. The solvers have been rewritten in C++ from scratch, with the efficiency enhanced by several times or even by an order of magnitude. The workflow of utilizing solvers has also been unified into a more comprehensive and consistent manner. New features include spin texture, Berry curvature and Chern number calculation, partial diagonalization for specific eigenvalues and eigenstates, analytical Hamiltonian, and GPU computing support. The documentation and tutorials have also been updated to the new version. In this paper, we discuss the revisions with respect to version 1.3 and demonstrate the new features. Benchmarks on modeling tools and solvers are also provided.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2509.26309v2</guid></item><item><title>[cond-mat updates on arXiv.org] Symbolic regression for defect interactions in 2D materials</title><link>https://arxiv.org/abs/2512.20785</link><description>arXiv:2512.20785v2 Announce Type: replace-cross
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Abstract: Machine learning models have become firmly established across all scientific fields. Extracting features from data and making inferences based on them with neural network models often yields high accuracy; however, this approach has several drawbacks. Symbolic regression is a powerful technique for discovering analytical equations that describe data, providing interpretable and generalizable models capable of predicting unseen data. Symbolic regression methods have gained new momentum with the advancement of neural network technologies and offer several advantages, the main one being the interpretability of results. In this work, we examined the application of the deep symbolic regression algorithm SEGVAE to determine the properties of two-dimensional materials with defects. Comparing the results with state-of-the-art graph neural network-based methods shows comparable or, in some cases, even identical outcomes. We also discuss the applicability of this class of methods in natural sciences.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.20785v2</guid></item><item><title>[cond-mat updates on arXiv.org] MiST: Understanding the Role of Mid-Stage Scientific Training in Developing Chemical Reasoning Models</title><link>https://arxiv.org/abs/2512.21231</link><description>arXiv:2512.21231v2 Announce Type: replace-cross
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Abstract: Large Language Models can develop reasoning capabilities through online fine-tuning with rule-based rewards. However, recent studies reveal a critical constraint: reinforcement learning succeeds only when the base model already assigns non-negligible probability to correct answers -- a property we term 'latent solvability'. This work investigates the emergence of chemical reasoning capabilities and what these prerequisites mean for chemistry. We identify two necessary conditions for RL-based chemical reasoning: 1) Symbolic competence, and 2) Latent chemical knowledge. We propose mid-stage scientific training (MiST): a set of mid-stage training techniques to satisfy these, including data-mixing with SMILES/CIF-aware pre-processing, continued pre-training on 2.9B tokens, and supervised fine-tuning on 1B tokens. These steps raise the latent-solvability score on 3B and 7B models by up to 1.8x, and enable RL to lift top-1 accuracy from 10.9 to 63.9% on organic reaction naming, and from 40.6 to 67.4% on inorganic material generation. Similar results are observed for other challenging chemical tasks, while producing interpretable reasoning traces. Our results define clear prerequisites for chemical reasoning training and highlight the broader role of mid-stage training in unlocking reasoning capabilities.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.21231v2</guid></item><item><title>[cond-mat updates on arXiv.org] Exact Multimode Quantization of Superconducting Circuits via Boundary Admittance</title><link>https://arxiv.org/abs/2601.04407</link><description>arXiv:2601.04407v2 Announce Type: replace-cross
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Abstract: We show that the Schur complement of the nodal admittance matrix, which reduces a multiport electromagnetic environment to the driving-point admittance $Y_{\mathrm{in}}(s)$ at the Josephson junction, naturally leads to an eigenvalue-dependent boundary condition determining the dressed mode spectrum. This identification provides a four-step quantization procedure: (i) compute or measure $Y_{\mathrm{in}}(s)$, (ii) solve the boundary condition $sY_{\mathrm{in}}(s) + 1/L_J = 0$ for dressed frequencies, (iii) synthesize an equivalent passive network, (iv) quantize with the full cosine nonlinearity retained. Within passive lumped-element circuit theory, we prove that junction participation decays as $O(\omega_n^{-1})$ at high frequencies when the junction port has finite shunt capacitance, ensuring ultraviolet convergence of perturbative sums without imposed cutoffs. The standard circuit QED parameters, coupling strength $g$, anharmonicity $\alpha$, and dispersive shift $\chi$, emerge as controlled limits with explicit validity conditions.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 13 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.04407v2</guid></item><item><title>[ChemRxiv] A data-efficient reactive machine learning potential to accelerate automated exploration of complex reaction networks</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-sm7f3-v2?rft_dat=source%3Ddrss</link><description>Reactive machine learning potentials (MLPs) significantly benefits high-throughput exploration of complex reaction networks. However, the development of such MLPs is currently constrained by the scarcity of datasets generated through efficient sampling methods that adequately capture elusive non-equilibrium configurations. To address this, we introduce an integrated molecular dynamics/coordinate driving-active learning (MD/CD-AL) framework that strategically samples reactive configurations, yielding a compact but comprehensive dataset, MDCD20, with ~1.4 million neutral and radical H/C/N/O structures. The MDCD-NN MLP trained on MDCD20 surpasses existing available MLPs trained on much larger datasets in reconstructing 181 elementary reactions with widespread types. It also accelerates automatic reaction network exploration by 10^4-fold in real-world systems relative to its reference quantum chemistry (QM) calculations, tackling complex scenarios such as multiple reaction centers, enantioselectivity and dynamic free-energy landscapes that are beyond the reach of traditional QM methods. This work establishes an efficient paradigm for constructing reactive datasets, enabling the training of reliable MLPs for complex reactive systems at an affordable cost.</description><author>ChemRxiv</author><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-sm7f3-v2?rft_dat=source%3Ddrss</guid></item><item><title>[Nature Communications] A unified time-frequency foundation model for sleep decoding</title><link>https://www.nature.com/articles/s41467-025-67970-4</link><description><p>Nature Communications, Published online: 13 January 2026; <a href="https://www.nature.com/articles/s41467-025-67970-4">doi:10.1038/s41467-025-67970-4</a></p>SleepGPT is a time-frequency foundation model for sleep decoding, built on a generative pretrained transformer, achieving superior performance in various downstream tasks across datasets.</description><author>Nature Communications</author><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41467-025-67970-4</guid></item><item><title>[npj Computational Materials] Physically interpretable interatomic potentials via symbolic regression and reinforcement learning</title><link>https://www.nature.com/articles/s41524-025-01952-4</link><description><p>npj Computational Materials, Published online: 13 January 2026; <a href="https://www.nature.com/articles/s41524-025-01952-4">doi:10.1038/s41524-025-01952-4</a></p>Physically interpretable interatomic potentials via symbolic regression and reinforcement learning</description><author>npj Computational Materials</author><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41524-025-01952-4</guid></item><item><title>[ChemRxiv] Can simple exchange heuristics guide us in predicting magnetic properties of solids?</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-xj84d-v2?rft_dat=source%3Ddrss</link><description>The Kanamori-Goodenough-Anderson rules are a textbook heuristic for predicting magnetism. They connect bond angles to magnetic ordering for some transition metal compounds. Such domain knowledge is of high importance for building predictive machine learning models in scenarios with scarce data. Yet, there has been no statistical, large-scale evaluation of the heuristic. Here, we evaluate this heuristic on an experimental database of magnetic structures. We observe that the heuristic is largely satisfied, and we discuss the exceptions. We then demonstrate how integrating this heuristic into machine learning models for predicting magnetic ordering enhances prediction quality. Notably, these magnetism models are also capable of predicting if non-collinear magnetic ordering might occur. Furthermore, the heuristic provides a useful benchmark for evaluating theoretical methods that calculate magnetic properties.</description><author>ChemRxiv</author><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-xj84d-v2?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] AIQM-PBSA: Integrating Machine Learning Interatomic
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Potentials with MMPBSA for Accurate Protein–Ligand Binding Free Energy Calculations</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-h0kn1?rft_dat=source%3Ddrss</link><description>End-point binding free energy (BFE) methods, such as molecular mechanics Poisson–Boltzmann surface area (MMPBSA), are widely used to estimate protein–ligand binding affinity due to their favorable balance between accuracy and computational efficiency. Their reliability, however, is often limited by approximations in intramolecular interactions and solvation effects. Given the critical role of force field quality in determining accuracy, we developed a hybrid framework named AIQM-PBSA, which integrates the ONIOM scheme with the PBSA model. Within this framework, the AIQM3 machine learning interatomic potential (MLIP) —an advanced Δ-learning quantum mechanical (QM) model—is employed to refine the molecular mechanics (MM) energy term, while polar and non polar solvation contributions are evaluated under the PBSA formalism. Extensive validation across diverse protein–ligand systems demonstrates that AIQM-PBSA significantly improves the correlation with experimental binding affinities compared to MMPBSA based on classical force fields and other MLIPs, with a representative benchmark showing up to 31% higher Pearson correlation relative to the MMPBSA baseline and 16% higher than ANI-2x. Furthermore, incorporating entropic contributions can further provide modest, target-dependent improvements. In summary, AIQM-PBSA offers a robust and transferable framework that combines QM level accuracy with MM level efficiency, substantially enhancing the reliability of endpoint free energy calculations for biomolecular recognition.</description><author>ChemRxiv</author><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-h0kn1?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] To Be or Not to Be: The Elusive Nature of Wheland-type Intermediates in Zeolite-Catalyzed Aromatic Alkylation Revealed by CCSD(T)-quality Metadynamics</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-2krfd?rft_dat=source%3Ddrss</link><description>The sustainable utilization of biomass feedstocks to produce value-added chemicals is a central challenge in heterogeneous catalysis. Cyclic alcohols constitute a major fraction of biomass-derived compounds, and their catalytic upgrading via zeolite-catalyzed alkylation provides an efficient route toward fuels and fine chemicals. In particular, benzene alkylation enables the synthesis of industrially relevant alkylated aromatics, while phenol alkylation is crucial for the valorization of lignin-derived feedstocks. Here, we employ machine-learning interatomic potentials (MLIPs) combined with well-tempered Metadynamics (WTMetaD) to investigate the alkylation of benzene and phenol using cyclohexene---the dehydrated form of cyclohexanol---as the alkylating agent within a zeolite framework. Free-energy surfaces (FES) obtained from enhanced sampling simulations are refined beyond standard generalized gradient approximation (GGA) density functional theory (DFT) using free-energy perturbation (FEP) to achieve MP2 and CCSD(T) accuracy. Our results reveal that it is essential to move beyond standard GGA-based DFT to accurately assess the stability of charged intermediates. The arenium ion (or Wheland intermediate), a key $\sigma$-complex in electrophilic aromatic substitution, appears relatively stable at the GGA level of theory. However, higher-level CCSD(T) calculations show that it corresponds to only a weakly stabilized, shallow minimum, indicating a highly transient character. The presence of an activating group such as the hydroxyl substituent in phenol significantly stabilizes both the arenium intermediate and the corresponding transition state, thereby lowering the overall alkylation activation barrier.</description><author>ChemRxiv</author><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-2krfd?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Retrieval-Augmented Large Language Models for Chemistry: A Comprehensive Survey</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-dtw9b?rft_dat=source%3Ddrss</link><description>The rapid proliferation of Large Language Models (LLMs) has heralded a new era in artificial intelligence, demonstrating remarkable capabilities in understanding, generating, and reasoning with human language. Their potential to revolutionize scientific discovery, particularly in chemistry, is immense. However, standalone LLMs are inherently limited by their reliance on static pre-training data, leading to issues such as factual hallucination, outdated knowledge, and a lack of transparency in their reasoning processes. Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm to mitigate these limitations by grounding LLM responses in external, up-to-date, and verifiable knowledge sources. This survey provides a comprehensive overview of the intersection of RAG and LLMs within the chemical sciences. We delve into the foundational concepts of LLMs and RAG, detail the unique architectures and methodologies required for handling diverse chemical data, and systematically review their applications across drug discovery, materials science, reaction prediction, and chemical literature mining. Furthermore, we critically examine the existing challenges, limitations, and ethical considerations inherent in deploying RAG-LLMs in chemistry. Finally, we discuss promising future directions, emphasizing the need for robust evaluation benchmarks and advanced multimodal RAG systems to unlock the full potential of these transformative technologies in accelerating chemical innovation.</description><author>ChemRxiv</author><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-dtw9b?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] A Construction of Arbitrary Order Internal Coordinate Transformations to Improve Studies of Large Amplitude Motions</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-bzmpw?rft_dat=source%3Ddrss</link><description>Internal coordinates and their derivatives underpin the efficient treatment of geometry optimizations, high-resolution spectroscopic simulation, and the fitting of potential surfaces in quantum chemistry. Existing descriptions of the construction of internal coordinate derivatives generally either lack simplicity or generality. In this paper, we provide a simple framework for evaluating any internal coordinate derivative to any order and an automatic approach to obtain the corresponding inverse transformation. Through further extension to transformations between internal coordinate systems, this approach provides a complete, generic method for handling a wide variety of molecular problems. The utility of this construction is demonstrated by investigations into the behavior of internal coordinate interpolations for studying isomerizations, quantifying the coupling between carbonyl stretches and a complex stretch coordinate in an organometallic system, and analysis of the performance of a machine learned interatomic potential in computing anharmonic frequencies as a function of low-frequency coordinate distortions. This approach is shown to be numerically efficient as well as general, and a reference implementation is provided.</description><author>ChemRxiv</author><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-bzmpw?rft_dat=source%3Ddrss</guid></item><item><title>[RSC - Chem. Sci. latest articles] Mitigation strategies for Li2CO3 contamination in garnet-type solid-state electrolytes: Formation mechanisms and interfacial engineering</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC09699E</link><description><div><i><b>Chem. Sci.</b></i>, 2026, Accepted Manuscript<br /><b>DOI</b>: 10.1039/D5SC09699E, Review Article</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Bin Hao, Qiushi Wang, Fangyuan Zhao, Jialong Wu, Weiheng Chen, Zhong-Jie Jiang, Zhongqing Jiang<br />Garnet-type solid-state electrolytes (SSEs) are promising candidates for next-generation solid-state batteries (SSBs) owing to their high ionic conductivity, robust mechanical strength, and broad electrochemical stability window. However, exposure to ambient...<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Chem. Sci. latest articles</author><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC09699E</guid></item><item><title>[ChemRxiv] Navigating high-dimensional fabrication-parameter space in organic photovoltaics device optimization by a multi-tier machine learning framework</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-bhjz6-v2?rft_dat=source%3Ddrss</link><description>Organic photovoltaics (OPV) have achieved significant advances over recent decades, driven by synergistic innovations in molecular design and device engineering. However, precise morphological control within bulk-heterojunction active layers remains a crucial barrier to commercial viability, primarily due to the high-dimensional parameter spaces and complex interdependencies among processing variables. To overcome this challenge, we established a standardized materials-processing-performance database integrating donor/acceptor pairs, nine key active layer processing parameters, and device efficiencies. This database, curated from over a decade of experimental results, resolves critical data heterogeneity issues and provides the field's most comprehensive optimization resource. Leveraging this resource, we developed a novel three-tiered machine learning framework employing gradient boosting regression trees to progressively decode active layer processing complexities. Our strategy initiates with single-parameter models for targeted optimization, advances through stage-combined models revealing intra-process synergies (e.g., solvent-additive interplay), and culminates in a global optimization tier. Remarkably, this final tier achieves unprecedented performance, demonstrating >0.9 overall Pearson correlations, and >80% success rates in identifying optimal nine-dimensional configurations. Experimental validation on 78 novel systems confirms exceptional generalization, yielding >75% accuracy in predicting either optimal or secondary parameters across eight active layer processing conditions. This work establishes a robust framework for navigating processing complexity in high-dimensional spaces, enabling accelerated optimization of OPV photoactive layers and providing a transferable data-driven paradigm for rational process design in emerging photovoltaic technologies.</description><author>ChemRxiv</author><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-bhjz6-v2?rft_dat=source%3Ddrss</guid></item><item><title>[RSC - Chem. Sci. latest articles] The interlocking process in molecular machines explained by a combined approach: the nudged elastic band method and a machine learning potential</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC08303F</link><description><div><i><b>Chem. Sci.</b></i>, 2026, Accepted Manuscript<br /><b>DOI</b>: 10.1039/D5SC08303F, Edge Article</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Lucio Peña-Zarate, Alberto Vela, Jorge Tiburcio<br />Engineering molecular machines requires a precise knowledge of the mechanisms involved in programmed motions. Among artificial molecular machines, rotaxanes have emerged as a noteworthy model due to their ability to...<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Chem. Sci. latest articles</author><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC08303F</guid></item><item><title>[RSC - Digital Discovery latest articles] LivePyxel: Accelerating image annotations with a Python-integrated webcam live streaming</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00421G</link><description><div><i><b>Digital Discovery</b></i>, 2025, Accepted Manuscript<br /><b>DOI</b>: 10.1039/D5DD00421G, Paper</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Uriel Garcilazo-Cruz, Joseph O. Okeme, Rodrigo Vargas-Hernandez<br />The lack of flexible annotation tools has hindered the deployment of AI models in some scientific areas. Most existing image annotation software requires users to upload a precollected dataset, which...<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Digital Discovery latest articles</author><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00421G</guid></item><item><title>[iScience] Machine Learning Identifies Proteomic Risk Factors Across 23 Diseases</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(26)00062-3?rss=yes</link><description>Achieving minimally invasive and rapid detection is a crucial goal in modern medicine. The comprehensive characterization of the blood proteome holds great promise in advancing our understanding of disease etiology, facilitating early diagnosis, risk stratification, and improved monitoring across various diseases and their subtypes. In this study, we collected plasma proteomes from over 3000 patients, representing 23 distinct diseases, encompassing a total of 1462 proteins. Based on histological knowledge, we developed a two-stage hierarchical multi-disease classifier and applied it to perform multi-disease classification on the collected proteomic data.</description><author>iScience</author><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(26)00062-3?rss=yes</guid></item><item><title>[iScience] Gut fungal landscape in colorectal cancer and its cross-kingdom interplay with gut microbial ecology</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(26)00039-8?rss=yes</link><description>The gut microbiota is a key hallmark of colorectal cancer (CRC), yet gut fungi remain understudied. We characterized the gut fungal landscape and its associations with bacteria, metabolites, and trace elements in CRC using fecal samples from healthy controls (n = 401), colorectal polyp patients (n = 162), and CRC patients (n = 253). Fungal annotation was performed using genomic data from NCBI (PRJNA833221) as reference. Fungal diversity increased in CRC patients, with seven genera showing differential abundance.</description><author>iScience</author><pubDate>Tue, 13 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(26)00039-8?rss=yes</guid></item><item><title>[Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)] [ASAP] Multiscale Modeling of Solid Electrolyte Interphase Formation on Oxygen-Functionalized Graphite Anodes for Lithium-Ion Batteries</title><link>http://dx.doi.org/10.1021/acs.jctc.5c01561</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jctc.5c01561/asset/images/medium/ct5c01561_0011.gif" /></p><div><cite>Journal of Chemical Theory and Computation</cite></div><div>DOI: 10.1021/acs.jctc.5c01561</div></description><author>Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)</author><pubDate>Mon, 12 Jan 2026 20:10:43 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jctc.5c01561</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Ultrahigh Ionic Conductivity in Halide Electrolytes Enabled by Anion Framework Flexibility Engineering</title><link>http://dx.doi.org/10.1021/jacs.5c15937</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c15937/asset/images/medium/ja5c15937_0007.gif" /></p><div><cite>Journal of the American Chemical Society</cite></div><div>DOI: 10.1021/jacs.5c15937</div></description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Mon, 12 Jan 2026 17:43:24 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c15937</guid></item><item><title>[ACS Materials Letters: Latest Articles (ACS Publications)] [ASAP] Mechanochemistry-Driven Optimization of Halide-Based Solid-State Electrolytes via Orthogonal Design of Experiments and Regression Modeling</title><link>http://dx.doi.org/10.1021/acsmaterialslett.5c01492</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsmaterialslett.5c01492/asset/images/medium/tz5c01492_0005.gif" /></p><div><cite>ACS Materials Letters</cite></div><div>DOI: 10.1021/acsmaterialslett.5c01492</div></description><author>ACS Materials Letters: Latest Articles (ACS Publications)</author><pubDate>Mon, 12 Jan 2026 16:07:51 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsmaterialslett.5c01492</guid></item><item><title>[Wiley: Small: Table of Contents] Multifunctional Cellulose Derivative Enables Efficient and Stable Wide‐Bandgap Perovskite Solar Cells by Inhibiting Ion Migration</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202512469?af=R</link><description>Small, EarlyView.</description><author>Wiley: Small: Table of Contents</author><pubDate>Mon, 12 Jan 2026 15:04:35 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202512469</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] “Ionic Tug‐of‐War” Effect Decoupling Li+‐Coordination Enables High Ion Conductivity and Interface Stability for Solid‐State Electrolytes</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202505982?af=R</link><description>Advanced Energy Materials, EarlyView.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Mon, 12 Jan 2026 14:21:25 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202505982</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Machine learning guided prediction of solute segregation at coherent and semi-coherent metal/oxide interfaces</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625008237?dgcid=rss_sd_all</link><description><p>Publication date: October 2026</p><p><b>Source:</b> Computational Materials Science, Volume 264</p><p>Author(s): Yizhou Lu, Blas Pedro Uberuaga, Samrat Choudhury</p></description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Mon, 12 Jan 2026 12:46:02 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625008237</guid></item><item><title>[cond-mat updates on arXiv.org] The effect of normal stress on stacking fault energy in face-centered cubic metals</title><link>https://arxiv.org/abs/2601.05453</link><description>arXiv:2601.05453v1 Announce Type: new
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Abstract: Plastic deformation and fracture of FCC metals involve the formation of stable or unstable stacking faults (SFs) on (111) plane. Examples include dislocation cross-slip and dislocation nucleation at interfaces and near crack tips. The stress component normal to (111) plane can strongly affect the SF energy when the stress magnitude reaches several to tens of GPa. We conduct a series of DFT calculations of SF energies in six FCC metals: Al, Ni, Cu, Ag, Au, and Pt. The results show that normal compression significantly increases the stable and unstable SF energies in all six metals, while normal tension decreases them. The SF formation is accompanied by inelastic expansion in the normal direction. The DFT calculations are compared with predictions of several representative classical and machine-learning interatomic potentials. Many potentials fail to capture the correct stress effect on the SF energy, often predicting trends opposite to the DFT calculations. Possible ways to improve the ability of potentials to represent the stress effect on SF energy are discussed.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 12 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.05453v1</guid></item><item><title>[cond-mat updates on arXiv.org] Autonomous Discovery of the Ising Model's Critical Parameters with Reinforcement Learning</title><link>https://arxiv.org/abs/2601.05577</link><description>arXiv:2601.05577v1 Announce Type: new
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Abstract: Traditional methods for determining critical parameters are often influenced by human factors. This research introduces a physics-inspired adaptive reinforcement learning framework that enables agents to autonomously interact with physical environments, simultaneously identifying both the critical temperature and various types of critical exponents in the Ising model with precision. Interestingly, our algorithm exhibits search behavior reminiscent of phase transitions, efficiently converging to target parameters regardless of initial conditions. Experimental results demonstrate that this method significantly outperforms traditional approaches, particularly in environments with strong perturbations. This study not only incorporates physical concepts into machine learning to enhance algorithm interpretability but also establishes a new paradigm for scientific exploration, transitioning from manual analysis to autonomous AI discovery.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 12 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.05577v1</guid></item><item><title>[cond-mat updates on arXiv.org] Phase Frustration Induced Intrinsic Bose Glass in the Kitaev-Bose-Hubbard Model</title><link>https://arxiv.org/abs/2601.05781</link><description>arXiv:2601.05781v1 Announce Type: new
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Abstract: We report an intrinsic "Bubble Phase" in the two-dimensional Kitaev-Bose-Hubbard model, driven purely by phase frustration between complex hopping and anisotropic pairing. By combining Inhomogeneous Gutzwiller Mean-Field Theory with a Bogoliubov-de Gennes stability analysis augmented by a novel Energy Penalty Method, we demonstrate that this phase spontaneously fragments into coherent islands, exhibiting the hallmark Bose glass signature of finite compressibility without global superfluidity. Notably, we propose a unified framework linking disorder-driven localization to deterministic phase frustration, identifying the Bubble Phase as a pristine, disorder-free archetype of the Bose glass. Our results provide a theoretical blueprint for realizing glassy dynamics in clean quantum simulators.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 12 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.05781v1</guid></item><item><title>[cond-mat updates on arXiv.org] A Critical Examination of Active Learning Workflows in Materials Science</title><link>https://arxiv.org/abs/2601.05946</link><description>arXiv:2601.05946v1 Announce Type: new
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Abstract: Active learning (AL) plays a critical role in materials science, enabling applications such as the construction of machine-learning interatomic potentials for atomistic simulations and the operation of self-driving laboratories. Despite its widespread use, the reliability and effectiveness of AL workflows depend on implicit design assumptions that are rarely examined systematically. Here, we critically assess AL workflows deployed in materials science and investigate how key design choices, such as surrogate models, sampling strategies, uncertainty quantification and evaluation metrics, relate to their performance. By identifying common pitfalls and discussing practical mitigation strategies, we provide guidance to practitioners for the efficient design, assessment, and interpretation of AL workflows in materials science.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 12 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.05946v1</guid></item><item><title>[cond-mat updates on arXiv.org] Autonomous Sampling and SHAP Interpretation of Deposition-Rates in Bipolar HiPIMS</title><link>https://arxiv.org/abs/2601.05287</link><description>arXiv:2601.05287v1 Announce Type: cross
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Abstract: High-power impulse magnetron sputtering (HiPIMS) offers considerable control over ion energy and flux, making it invaluable for tailoring the microstructure and properties of advanced functional coatings. However, compared to conventional sputtering techniques, HiPIMS suffers from reduced deposition rates. Many groups have begun to evaluate complex pulsing schemes to improve upon this, leveraging multi-pulse schemes (e.g. pre-ionization or bipolar pulses). Unfortunately, the increased complexity of these pulsing schemes has led to high-dimensionality parameter spaces that are prohibitive to classic design of experi-ments. In this work we evaluate bipolar HiPIMS pulses for improving deposition rates of Al and Ti sputter tar-gets. Over 3000 process conditions were collected via autonomous Bayesian sampling over a 6-dimensional parameter space. These process conditions were then interpreted using Shapley Additive Explanations (SHAP), to deconvolute complex process influences on deposition rates. This allows us to link observed var-iations in deposition rate to physical mechanisms such as back-attraction and plasma ignition. Insights gained from this approach were then used to target specific processes where the positive pulse components were expected to have the highest impact on deposition rates. However, in practice, only minimal improve-ments in deposition rate were achieved. In most cases, the positive pulse appears to be detrimental when placed immediately after the neg. pulse which we hypothesize relates to quenching of the afterglow plasma. The proposed workflow combining autonomous experimentation and interpretable machine learning is broad-ly applicable to the discovery and optimization of complex plasma processes, paving the way for physics-informed, data-driven advancements in coating technologies.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 12 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.05287v1</guid></item><item><title>[cond-mat updates on arXiv.org] VacHopPy: A Python package for vacancy hopping analysis based on molecular dynamics simulations</title><link>https://arxiv.org/abs/2503.23467</link><description>arXiv:2503.23467v2 Announce Type: replace
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Abstract: Multiscale modeling, which integrates material properties from ab initio calculations into continuum-scale simulations, is a promising strategy for optimizing semiconductor devices. However, a key challenge remains: while ab initio methods provide diffusion parameters specific to individual migration paths, continuum equations require a single effective set of parameters that captures the overall diffusion behavior. To address this issue, we present VacHopPy, an open-source Python package for vacancy hopping analysis based on molecular dynamics (MD). VacHopPy extracts an effective set of hopping parameters, including hopping distance, hopping barrier, number of effective paths, correlation factor, and attempt frequency, by statistically integrating energetic, kinetic, and geometric contributions across all paths. It also includes tools for tracking vacancy trajectories and for detecting phase transitions during MD simulations. The applicability of VacHopPy is demonstrated in three representative materials: face-centered cubic Al, rutile TiO2, and monoclinic HfO2. The extracted effective parameters reproduce temperature-dependent diffusion behavior and are in good agreement with previous experimental data. Provided in a simplified form, these parameters are well suited for continuum-scale models and remain valid over a wide temperature range spanning several hundred kelvins. Furthermore, VacHopPy inherently accounts for anisotropy in thermal vibrations, a factor often overlooked, making it suitable for simulating diffusion in complex crystals. Overall, VacHopPy establishes a robust bridge between atomic- and continuum-scale models, enabling more reliable multiscale simulation</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 12 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2503.23467v2</guid></item><item><title>[cond-mat updates on arXiv.org] Machine Learning Framework for Characterizing Processing-Structure Relationship in Block Copolymer Thin Films</title><link>https://arxiv.org/abs/2505.23064</link><description>arXiv:2505.23064v2 Announce Type: replace
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Abstract: The morphology of block copolymers (BCPs) critically influences material properties and applications. This work introduces a machine learning (ML)-enabled, high-throughput framework for analyzing grazing incidence small-angle X-ray scattering (GISAXS) data and atomic force microscopy (AFM) images to characterize BCP thin film morphology. A convolutional neural network was trained to classify AFM images by morphology type, achieving 97% testing accuracy. Classified images were then analyzed to extract 2D grain size measurements from the samples in a high-throughput manner. ML models were developed to predict morphological features based on processing parameters such as solvent ratio, additive type, and additive ratio. GISAXS-based properties were predicted with strong performances ($R^2$ > 0.75), while AFM-based property predictions were less accurate ($R^2$ < 0.60), likely due to the localized nature of AFM measurements compared to the bulk information captured by GISAXS. Beyond model performance, interpretability was addressed using Shapley Additive exPlanations (SHAP). SHAP analysis revealed that the additive ratio had the largest impact on morphological predictions, where additive provides the BCP chains with increased volume to rearrange into thermodynamically favorable morphologies. This interpretability helps validate model predictions and offers insight into parameter importance. Altogether, the presented framework combining high-throughput characterization and interpretable ML offers an approach to exploring and optimizing BCP thin film morphology across a broad processing landscape.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 12 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2505.23064v2</guid></item><item><title>[cond-mat updates on arXiv.org] Efficient Band Structure Unfolding with Atom-centered Orbitals: General Theory and Application</title><link>https://arxiv.org/abs/2506.21089</link><description>arXiv:2506.21089v2 Announce Type: replace
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Abstract: Band structure unfolding is a key technique for analyzing and simplifying the electronic band structure of large, internally distorted supercells that break the primitive cell's translational symmetry. In this work, we present an efficient band unfolding method for atomic orbital (AO) basis sets that explicitly accounts for both the non-orthogonality of atomic orbitals and their atom-centered nature. Unlike existing approaches that typically rely on a plane-wave representation of the (semi-)valence states, we here derive analytical expressions that recasts the primitive cell translational operator and the associated Bloch-functions in the supercell AO basis. In turn, this enables the accurate and efficient unfolding of conduction, valence, and core states in all-electron codes, as demonstrated by our implementation in the all-electron ab initio simulation package FHI-aims, which employs numeric atom-centered orbitals. We explicitly demonstrate the capability of running large-scale unfolding calculations for systems with thousands of atoms and showcase the importance of this technique for computing temperature-dependent spectral functions in strongly anharmonic materials using CuI as example.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 12 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2506.21089v2</guid></item><item><title>[cond-mat updates on arXiv.org] Disordered Dynamics in High Dimensions: Connections to Random Matrices and Machine Learning</title><link>https://arxiv.org/abs/2601.01010</link><description>arXiv:2601.01010v2 Announce Type: replace
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Abstract: We provide an overview of high dimensional dynamical systems driven by random matrices, focusing on applications to simple models of learning and generalization in machine learning theory. Using both cavity method arguments and path integrals, we review how the behavior of a coupled infinite dimensional system can be characterized as a stochastic process for each single site of the system. We provide a pedagogical treatment of dynamical mean field theory (DMFT), a framework that can be flexibly applied to these settings. The DMFT single site stochastic process is fully characterized by a set of (two-time) correlation and response functions. For linear time-invariant systems, we illustrate connections between random matrix resolvents and the DMFT response. We demonstrate applications of these ideas to machine learning models such as gradient flow, stochastic gradient descent on random feature models and deep linear networks in the feature learning regime trained on random data. We demonstrate how bias and variance decompositions (analysis of ensembling/bagging etc) can be computed by averaging over subsets of the DMFT noise variables. From our formalism we also investigate how linear systems driven with random non-Hermitian matrices (such as random feature models) can exhibit non-monotonic loss curves with training time, while Hermitian matrices with the matching spectra do not, highlighting a different mechanism for non-monotonicity than small eigenvalues causing instability to label noise. Lastly, we provide asymptotic descriptions of the training and test loss dynamics for randomly initialized deep linear neural networks trained in the feature learning regime with high-dimensional random data. In this case, the time translation invariance structure is lost and the hidden layer weights are characterized as spiked random matrices.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 12 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.01010v2</guid></item><item><title>[cond-mat updates on arXiv.org] Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system</title><link>https://arxiv.org/abs/2506.05999</link><description>arXiv:2506.05999v2 Announce Type: replace-cross
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Abstract: Self-driving labs (SDLs), employing automation and machine learning (ML) to accelerate experimental procedures, have enormous potential in the discovery of new materials. However, in thin film science, SDLs are mainly restricted to solution-based synthetic methods which are easier to automate but cannot access the broad chemical space of inorganic materials. This work presents an SDL based on magnetron co-sputtering. We are using combinatorial frameworks, obtaining accurate composition maps on multi-element, compositionally graded thin films. This normally requires time-consuming ex-situ analysis prone to systematic errors. We present a rapid and calibration-free in-situ, ML driven approach to produce composition maps for arbitrary source combinations and sputtering conditions. We develop a method to predict the composition distribution in a multi-element combinatorial thin film, using in-situ measurements from quartz-crystal microbalance sensors placed in a sputter chamber. For a given source, the sensor readings are learned as a function of the sputtering pressure and magnetron power, through active learning using Gaussian processes (GPs). The final GPs are combined with a geometric model of the deposition flux distribution in the chamber, which allows interpolation of the deposition rates from each source, at any position across the sample. We investigate several acquisition functions for the ML procedure. A fully Bayesian GP - BALM (Bayesian active learning MacKay) - achieved the best performance, learning the deposition rates for a single source in 10 experiments. Prediction accuracy for co-sputtering composition distributions was verified experimentally. Our framework dramatically increases throughput by avoiding the need for extensive characterisation or calibration, thus demonstrating the potential of ML-guided SDLs to accelerate materials exploration.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 12 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2506.05999v2</guid></item><item><title>[ChemRxiv] ACCEL: Automated Closed-loop Co-Optimization and Experimentation Learning Enables Phase-Pure Identification in Formamidinium-based Dion–Jacobson Halide Perovskites</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-8c93m?rft_dat=source%3Ddrss</link><description>Self-driving laboratories (SDLs) are poised to transform materials discovery by integrating automation with machine learning (ML) to accelerate data-driven experimentation. However, most SDL frameworks remain limited by single-feedback optimization and lack the multi-modal diagnostics needed to resolve both optical and structural evolution in complex hybrid systems. Here, we introduce ACCEL, an automated, ML-guided closed-loop platform for identifying and synthesizing target pure phases within quasi-2D halide perovskite systems. Using a ternary 3D:2D compositional space comprising 3D FAPbI3 and two Dion–Jacobson (DJ) spacer–based components, ACCEL optimizes the 3D:2D ratios to identify compositions that converge toward a structurally and optically stable α-FAPbI3-like phase within a quasi-2D design space. Automated in-situ photoluminescence (PL) is used to monitor crystallization kinetics in real time, revealing how the relative fraction of co-spacers governs nucleation rate and phase evolution during film formation. These kinetic insights are integrated with high-throughput synthesis, automated PL analysis, and X-ray diffraction (XRD) similarity metrics within a Gaussian Process–Bayesian Optimization framework to suppress phase heterogeneity and stabilize the target phase. Rather than targeting a predefined DJ layer number, ACCEL enables optimization based on user-defined optical and structural signatures, providing a generalizable route for autonomous phase discovery in complex hybrid materials.</description><author>ChemRxiv</author><pubDate>Mon, 12 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-8c93m?rft_dat=source%3Ddrss</guid></item><item><title>[RSC - Chem. Sci. latest articles] From Crystal Structure Prediction to Polymorphic Behaviour: Monte Carlo Threshold Mapping of Crystal Energy Landscapes</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC08644B</link><description><div><i><b>Chem. Sci.</b></i>, 2026, Accepted Manuscript<br /><b>DOI</b>: 10.1039/D5SC08644B, Edge Article</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Pedro Juan-Royo, Graeme Matthew Day<br />Crystal structure prediction has developed into a valuable tool for anticipating the likely crystalline arrangement that a molecule will adopt, with applications in materials discovery and polymorph screening. Although powerful,...<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Chem. Sci. latest articles</author><pubDate>Mon, 12 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC08644B</guid></item><item><title>[RSC - Chem. Sci. latest articles] Exploring stacking pressure-induced mechanical failure of a Ni-rich cathode in sulfide solid-state batteries</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC09321J</link><description><div><p><img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5SC09321J" /></p></div><div><i><b>Chem. Sci.</b></i>, 2026, Advance Article<br /><b>DOI</b>: 10.1039/D5SC09321J, Edge Article</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Yiman Feng, Zhixing Wang, Gui Luo, Duo Deng, Wenjie Peng, Wenchao Zhang, Hui Duan, Feixiang Wu, Xing Ou, Junchao Zheng, Jiexi Wang<br />Stacking pressure in sulfide all-solid-state batteries has dual effects: optimal pressure improves contact and ion transport, while excessive pressure induces structural degradation, leading to electrode cracking and capacity fade.<br />To cite this article before page numbers are assigned, use the DOI form of citation above.<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Chem. Sci. latest articles</author><pubDate>Mon, 12 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC09321J</guid></item><item><title>[ACS Materials Letters: Latest Articles (ACS Publications)] [ASAP] Pressure-Induced In Situ Lithiation of Si-Based Interlayers for Stable Li-Metal Anodes in All-Solid-State Batteries</title><link>http://dx.doi.org/10.1021/acsmaterialslett.5c01201</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsmaterialslett.5c01201/asset/images/medium/tz5c01201_0006.gif" /></p><div><cite>ACS Materials Letters</cite></div><div>DOI: 10.1021/acsmaterialslett.5c01201</div></description><author>ACS Materials Letters: Latest Articles (ACS Publications)</author><pubDate>Sun, 11 Jan 2026 18:52:15 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsmaterialslett.5c01201</guid></item><item><title>[ScienceDirect Publication: Joule] A critical outlook for large-scale all-solid-state batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2542435125004507?dgcid=rss_sd_all</link><description><p>Publication date: Available online 9 January 2026</p><p><b>Source:</b> Joule</p><p>Author(s): Seongjae Ko, Makoto Ue, Atsuo Yamada</p></description><author>ScienceDirect Publication: Joule</author><pubDate>Sun, 11 Jan 2026 01:50:45 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125004507</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Recycling of Thermoplastics with Machine Learning: A Review</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202509447?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 3, 8 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Sat, 10 Jan 2026 15:14:36 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202509447</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Electron Compensation Enhanced Triboelectric Sensor Assisted by Machine Learning for Tactile Perception Recognition</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202514567?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 3, 8 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Sat, 10 Jan 2026 15:14:36 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202514567</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Selective Ion Transport Regulation Enables High Current Density CO2‐to‐C2+ Conversion in Acid</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202516139?af=R</link><description>Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Sat, 10 Jan 2026 15:07:04 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202516139</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Triply Responsive Control of Ion Transport with an Artificial Channel Creates a Switchable AND to OR Logic Gate</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202517444?af=R</link><description>Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Sat, 10 Jan 2026 15:04:51 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202517444</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Coupled Engineering of Short‐/Long‐Range Disorder in Oxyhalides Unlocks Benchmark Sodium Superionic Conductor</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202518183?af=R</link><description>Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Sat, 10 Jan 2026 15:04:51 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202518183</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Atomistic Landscape of Pt Nanoparticles via Machine Learning: How Size Effect and Hydrogen Adsorption Govern Structural Ensembles and Catalytic Activity</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202519209?af=R</link><description>Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Sat, 10 Jan 2026 15:04:51 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202519209</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Empowering Chemistry Experts with Large Language Models for Literature Interpretation in Single‐Atom Catalysis Toward Advanced Oxidation</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202520525?af=R</link><description>Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Sat, 10 Jan 2026 15:04:51 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202520525</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Balancing Oxidative Stability and Ion Transport in Quasi‐Solid Polymer Electrolytes via Chlorine‐Driven Halogenation Engineering</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202521087?af=R</link><description>Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Sat, 10 Jan 2026 15:04:51 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202521087</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Machine Learning Driven High‐Throughput Screening of Asymmetric Dinuclear Cobalt for Nitrate‐to‐Ammonia Reduction with Near‐100% Selectivity</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202506009?af=R</link><description>Advanced Energy Materials, EarlyView.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Sat, 10 Jan 2026 14:09:28 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202506009</guid></item><item><title>[ChemRxiv] ConforFormer: representation for molecules through understanding of conformers</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-x68vd-v2?rft_dat=source%3Ddrss</link><description>Recent years have seen a growing interest in machine learning approaches for chemical tasks. The best existing methods focus on building base models that combine molecular graphs (“2D structures”) with atomic coordinates in 3D to predict molecular properties, typically through pre-training followed by fine-tuning on benchmark datasets. However, current approaches require updating the weights of the entire model during the fine-tuning procedure for each prediction task. While this enables state-of-the-art performance, it limits practical deployment, as real-world datasets are often too small to support the stable retraining of large models. Importantly, the 3D geometry of a molecule holds crucial information for predicting its properties, but a single molecular graph usually corresponds to several 3D geometries, called conformers, introducing ambiguity into the inference process. Typical solutions rely on molecular graphs, but this approach is not easily generalizable beyond organic molecules. Here, we present ConforFormer, a method that explicitly accounts for the diversity of 3D conformations of a molecule to derive a task-agnostic and conformation-agnostic vector representation. This model serves as a foundational framework, producing embeddings that can be generated once and directly applied to downstream tasks, including property prediction and structural similarity, without extensive fine-tuning.</description><author>ChemRxiv</author><pubDate>Sat, 10 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-x68vd-v2?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Graph learning of sequence statistics for polymer representation</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-tb0td?rft_dat=source%3Ddrss</link><description>Polymers underpin critical technologies from medicine to energy, but their immense chemical and structural diversity makes rational design exceptionally difficult. Machine learning offers a way to navigate this space, yet prevailing approaches inherit small-molecule representations that fail to encode polymer-specific architecture; the distinction between random, block and other statistical copolymers is often collapsed into a categorical tag or ignored. Here, we introduce SCALE (Statistical Copolymer Architecture with Learning Edges), which recasts a copolymer as a Markovian sequence over a monomer alphabet and embeds the transition probabilities P(j/i) as edge features within a graph attention network. Message passing thus computes contextualized monomer states analogous to applying a transfer operator along the chain, while attention learns a data-driven kernel over paths that weights sequence heterogeneity versus block persistence. On a robotically synthesized, high-throughput fluorescence library, SCALE attained RMSE ≈228 and R² ≈0.84, surpassing polymer-adapted neural baselines and descriptor regressors (e.g., wDMPNN RMSE ≈326; XGBoost RMSE ≈254). The model is interpretable: edges dominate predictions for statistical (random) copolymers, whereas nodes prevail for block copolymers, consistent with NOESY 2D NMR. Beyond photophysics, SCALE generalized to antibacterial design across penta- and hexa-copolymer libraries with validation from <300 syntheses. By elevating sequence statistics to first-class learning variables, SCALE provides a generalizable, data-efficient route to closed-loop polymer discovery.</description><author>ChemRxiv</author><pubDate>Sat, 10 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-tb0td?rft_dat=source%3Ddrss</guid></item><item><title>[npj Computational Materials] Machine learning for phase prediction of high entropy carbide ceramics from imbalanced data</title><link>https://www.nature.com/articles/s41524-025-01873-2</link><description><p>npj Computational Materials, Published online: 10 January 2026; <a href="https://www.nature.com/articles/s41524-025-01873-2">doi:10.1038/s41524-025-01873-2</a></p>Machine learning for phase prediction of high entropy carbide ceramics from imbalanced data</description><author>npj Computational Materials</author><pubDate>Sat, 10 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41524-025-01873-2</guid></item><item><title>[Wiley: Small Structures: Table of Contents] Dielectric Constant Guided Solvation Structure Design for Stable Solid Electrolyte Interphase in Lithium Metal Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/sstr.202500760?af=R</link><description>Small Structures, Volume 7, Issue 1, January 2026.</description><author>Wiley: Small Structures: Table of Contents</author><pubDate>Fri, 09 Jan 2026 19:05:13 GMT</pubDate><guid isPermaLink="true">10.1002/sstr.202500760</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] External pressure's influence on lithium-ion transport within solid-state electrolytes</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X26001180?dgcid=rss_sd_all</link><description><p>Publication date: 1 March 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 149</p><p>Author(s): Yuan He, Xiongying Zhang, Dong Lu</p></description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Fri, 09 Jan 2026 18:31:35 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X26001180</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Alterative aqueous polymer anode binder enabling interfacial stabilization and improved lithium-ion transport</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X26000423?dgcid=rss_sd_all</link><description><p>Publication date: 1 March 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 149</p><p>Author(s): Yuan Peng, Huimin Chen, Xiaowen Qv, Ao Zeng, Jianfeng Xia, Jiangtao Xu, Kunkun Guo</p></description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Fri, 09 Jan 2026 18:31:35 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X26000423</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Interface engineering of Li<sub>1.5</sub>Al<sub>0.5</sub>Ge<sub>1.5</sub>(PO<sub>4</sub>)<sub>3</sub> electrolytes via in-situ polymer–cerium hybrid interlayers for high-performance all-solid-state lithium metal batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25047759?dgcid=rss_sd_all</link><description><p>Publication date: 1 March 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 149</p><p>Author(s): Kaiqi Wu, Chengjin Peng, Fanglin Wu, Liyuan Huang, Liang Lan, Liqiang Kang, Yecheng Liu, Xin Ao, Shan Fang</p></description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Fri, 09 Jan 2026 18:31:35 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25047759</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] A general LLM-powered text mining framework: Applied to extract high entropy alloys</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625008195?dgcid=rss_sd_all</link><description><p>Publication date: October 2026</p><p><b>Source:</b> Computational Materials Science, Volume 264</p><p>Author(s): Haolun Yuan, Jun Zeng, Jie Zuo, Xin Wang, Dingguo Xu</p></description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Fri, 09 Jan 2026 18:31:33 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625008195</guid></item><item><title>[ScienceDirect Publication: Materials Today] Heteropolyanion regulation activating decoupled ion transition for Na superionic conductors</title><link>https://www.sciencedirect.com/science/article/pii/S1369702125005450?dgcid=rss_sd_all</link><description><p>Publication date: Available online 9 January 2026</p><p><b>Source:</b> Materials Today</p><p>Author(s): Tian Jiang, Qi Fan, Wenshan Gou, Anyang Yu, Changhao Zhu, Ruirui Zhang, Youwei Dong, Shijun Yuan, Qingyu Xu</p></description><author>ScienceDirect Publication: Materials Today</author><pubDate>Fri, 09 Jan 2026 18:31:29 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1369702125005450</guid></item><item><title>[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Correlating (Chemo-)Mechanical Coupling in TiS2 during Li+ Intercalation across Liquid and Solid Electrolytes Via Operando Analysis</title><link>http://dx.doi.org/10.1021/acsenergylett.5c02943</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsenergylett.5c02943/asset/images/medium/nz5c02943_0006.gif" /></p><div><cite>ACS Energy Letters</cite></div><div>DOI: 10.1021/acsenergylett.5c02943</div></description><author>ACS Energy Letters: Latest Articles (ACS Publications)</author><pubDate>Fri, 09 Jan 2026 16:22:26 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsenergylett.5c02943</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Harnessing Entropic Effects from Interlayer Coupling to Modulate Ion Transport and Rectification in Multilayered Janus Graphene Nanopores</title><link>http://dx.doi.org/10.1021/jacs.5c17242</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c17242/asset/images/medium/ja5c17242_0006.gif" /></p><div><cite>Journal of the American Chemical Society</cite></div><div>DOI: 10.1021/jacs.5c17242</div></description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Fri, 09 Jan 2026 12:51:38 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c17242</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Uncertainty‐Quantified Primary Particle Size Prediction in Li‐Rich NCM Materials via Machine Learning and Chemistry‐Aware Imputation</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202515694?af=R</link><description>Advanced Science, Volume 13, Issue 2, 9 January 2026.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Fri, 09 Jan 2026 11:35:15 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202515694</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Highly Selective CO2 Reduction to Pure Formic Acid Using a Nafion‐TiO2 Composite Porous Solid Electrolyte</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202515967?af=R</link><description>Advanced Science, Volume 13, Issue 2, 9 January 2026.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Fri, 09 Jan 2026 11:35:15 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202515967</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Macroscopically Ordered Piezo‐Potential in All‐Polymetric Solid Electrolytes Responding to Li Anode Volume Changes for Dendrites Suppression</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202509897?af=R</link><description>Advanced Science, Volume 13, Issue 2, 9 January 2026.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Fri, 09 Jan 2026 11:35:15 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202509897</guid></item><item><title>[Recent Articles in Phys. Rev. B] Reinforcement fine-tuning for materials design</title><link>http://link.aps.org/doi/10.1103/45zh-44bg</link><description>Author(s): Zhendong Cao and Lei Wang<br /><p>Reinforcement fine-tuning played an instrumental role in enhancing the instruction-following and reasoning abilities of large language models. In this work, we employ reinforcement fine-tuning for materials design, in which discriminative machine learning models are used to provide rewards to the au…</p><br />[Phys. Rev. B 113, 024106] Published Fri Jan 09, 2026</description><author>Recent Articles in Phys. Rev. B</author><pubDate>Fri, 09 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/45zh-44bg</guid></item><item><title>[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] ADAR-GPT: A continually fine-tuned language model for predicting A-to-I RNA editing sites</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2529073123?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 123, Issue 2, January 2026. <br />SignificanceA-to-I RNA editing by adenosine deaminases acting on RNA (ADAR) enzymes reshapes the transcriptome and holds great promise for therapeutic RNA design, yet identifying which adenosines are edited remains a central challenge. We present Adar-GPT,...</description><author>Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents</author><pubDate>Fri, 09 Jan 2026 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2529073123?af=R</guid></item><item><title>[cond-mat updates on arXiv.org] Fluctuation conductivity in ultraclean multicomponent superconductors</title><link>https://arxiv.org/abs/2601.04308</link><description>arXiv:2601.04308v1 Announce Type: new
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Abstract: We consider the intrinsic fluctuation conductivity in metals with multiply sheeted Fermi surfaces approaching a superconducting critical point. Restricting our attention to extreme type-II multicomponent superconductors motivates focusing on the ultraclean limit. Using functional-integral techniques, we derive the Gaussian fluctuation action from which we obtain the gauge-invariant electromagnetic linear response kernel. This allows us to compute the optical conductivity tensor. We identify essential conditions required for a nonzero longitudinal conductivity at finite frequencies in a disorder-free and translationally invariant system. Specifically, this is neither related to impurity scattering nor electron-phonon interaction, but derives indirectly from the multicomponent character of the incipient superconducting order and the parent metallic state. Under these conditions, the enhancement of the DC conductivity due to fluctuations close to the critical point follows the same critical behaviour as in the diffusive limit.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.04308v1</guid></item><item><title>[cond-mat updates on arXiv.org] Towards understanding the defect properties in the multivalent A-site Na$_{0.5}$Bi$_{0.5}$TiO$_3$-based perovskite ceramics</title><link>https://arxiv.org/abs/2601.04725</link><description>arXiv:2601.04725v1 Announce Type: new
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Abstract: A defect model involving cation and anion vacancies and anti-site defects is proposed that accounts for the non-stoichiometry of multi-valent $A$-site Na$_{0.5}$Bi$_{0.5}$TiO$_3$ based perovskite oxides with $ABO_3$ composition. A series of samples with varying $A$-site non-stoichiometry and $A$:$B$ ratios were prepared to investigate their electrical conductivity. The oxygen partial pressure and temperature dependent conductivities where studied with direct current (dc) and alternating current (ac) techniques, enabling to separate between ionic and electronic conduction. The Na-excess samples, regardless of the $A$:$B$ ratio, exhibit dominant ionic conductivity and $p$-type electronic conduction, with the highest total conductivity reaching $4 \times 10^{-4}$ S/cm at 450$^\circ$C. In contrast, the Bi-excess samples display more insulating characteristics and $n$-type electronic conductivity, with conductivity values within the 10$^{-8}$ S/cm range at 450$^\circ$C. These conductivity results strongly support the proposed defect model, which offers a straightforward description of defect chemistry in NBT-based ceramics and serves as a valuable guide for optimizing sample processing to achieve tailored properties.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.04725v1</guid></item><item><title>[cond-mat updates on arXiv.org] Scalable Dielectric Tensor Predictions for Inorganic Materials using Equivariant Graph Neural Networks</title><link>https://arxiv.org/abs/2601.04755</link><description>arXiv:2601.04755v1 Announce Type: new
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Abstract: Accurate prediction of dielectric tensors is essential for accelerating the discovery of next-generation inorganic dielectric materials. Existing machine learning approaches, such as equivariant graph neural networks, typically rely on specially-designed network architectures to enforce O(3) equivariance. However, to preserve equivariance, these specially-designed models restrict the update of equivariant features during message passing to linear transformations or gated equivariant nonlinearities. The inability to implicitly characterize more complex nonlinear structures may reduce the predictive accuracy of the model. In this study, we introduce a frame-averaging-based approach to achieve equivariant dielectric tensor prediction. We propose GoeCTP, an O(3)-equivariant framework that predicts dielectric tensors without imposing any structural restrictions on the backbone network. We benchmark its performance against several state-of-the-art models and further employ it for large-scale virtual screening of thermodynamically stable materials from the Materials Project database. GoeCTP successfully identifies various promising candidates, such as Zr(InBr$_3$)$_2$ (band gap $E_g = 2.41$ eV, dielectric constant $\overline{\varepsilon} = 194.72$) and SeI$_2$ (anisotropy ratio $\alpha_r = 96.763$), demonstrating its accuracy and efficiency in accelerating the discovery of advanced inorganic dielectric materials.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.04755v1</guid></item><item><title>[cond-mat updates on arXiv.org] Lateral Graphene-Metallene Interfaces at the Nanoscale</title><link>https://arxiv.org/abs/2601.04838</link><description>arXiv:2601.04838v1 Announce Type: new
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Abstract: Metallenes are atomically thin, nonlayered two-dimensional materials. While they have appealing properties, their isotropic metallic bonding makes their stabilization difficult and presents considerable challenges to their synthesis and practical applications. However, their stabilization can still be achieved by suspending them in the pores of two-dimensional template materials, making the properties of lateral interfaces of metallenes scientifically relevant. Here, we combined density-functional theory and universal machine-learning interatomic potentials to study lateral interfaces between graphene and 45 metallenes with various profiles. We optimized the interfaces and analyzed their energies, electronic structures, and stabilities at room temperature, defect formations, and structural deformations. While broad trends were identified using machine-learning analysis of all interfaces, density-functional theory was the main tool for studying the microscopic properties of selected elements. We found that the interfaces are the most stable energetically and with respect to lattice mismatch, defect formation, and lateral strain when their profiles were geometrically smooth. The most stable interfaces are found for transition metals. In addition, we demonstrate how universal machine-learning interatomic potentials now offer the accuracy required for the modeling of graphene-metallene interfaces. By systematically expanding the understanding of metallenes' interface properties, we hope these results guide and accelerate their synthesis to enable future applications and benefit from metallenes' appealing properties.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.04838v1</guid></item><item><title>[cond-mat updates on arXiv.org] Stable Machine Learning Potentials for Liquid Metals via Dataset Engineering</title><link>https://arxiv.org/abs/2601.05003</link><description>arXiv:2601.05003v1 Announce Type: new
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Abstract: Liquid metals are central to energy-storage and nuclear technologies, yet quantitative knowledge of their thermophysical properties remains limited. While atomistic simulations offer a route to computing liquid properties directly from atomic motion, the most accurate approach, ab initio molecular dynamics (AIMD), is computationally costly and restricted to short time and length scales. Machine learning interatomic potentials (MLPs) offer AIMD accuracy at far lower cost, but their application to liquids is limited by training datasets that inadequately sample atomic configurations, leading to unphysical force predictions and unstable trajectories. Here we introduce a physically motivated dataset-engineering strategy that constructs liquidlike training data synthetically rather than relying on AIMD configurations. The method exploits the established icosahedral short-range order of metallic liquids, twelvefold, near-close-packed local coordination, and generates "synthetic-liquid" structures by systematic perturbation of crystalline references. MLPs trained on these datasets close the sampling gaps that lead to unphysical predictions, remain numerically stable across temperatures, and reproduce experimental liquid densities, diffusivities, and melting temperatures for multiple elemental metals. The framework links atomic-scale sampling to long-term MD stability and provides a practical route to predictive modeling of liquid-phase thermophysical behavior beyond the limits of direct AIMD.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.05003v1</guid></item><item><title>[cond-mat updates on arXiv.org] Hierarchical Crystal Structure Prediction of Zeolitic Imidazolate Frameworks Using DFT and Machine-Learned Interatomic Potentials</title><link>https://arxiv.org/abs/2601.05097</link><description>arXiv:2601.05097v1 Announce Type: new
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Abstract: Crystal structure prediction (CSP) is emerging as a powerful method for the computational design of metal-organic frameworks (MOFs). In this article we demonstrate the high-throughput exploration of the crystal energy landscape of zinc imidazolate (ZnIm2), a highly polymorphic member of the zeolitic imidazolate (ZIF) family, with at least 24 reported structural and topological forms, with new polymorphs still being regularly discovered. With the aid of custom-trained machine-learned interatomic potentials (MLIPs) we have performed a high-throughput sampling of over 3 million randomly-generated crystal packing arrangements and identified 9626 energy minima characterized by 1493 network topologies, including 864 topologies that have not been reported before. Comparisons with previously reported structures revealed 13 topological matches to the experimentally-observed structures of ZnIm2, demonstrating the power of the CSP method in sampling experimentally-relevant ZIF structures. Finally, through a combination of topological analysis, density and porosity considerations, we have identified a set of structures representing promising targets for future experimental screening. Finally, we demonstrate how CSP can be used to assist in the identification of the products of the mechanochemical synthesis.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.05097v1</guid></item><item><title>[cond-mat updates on arXiv.org] Beyond the imbalance: site-resolved dynamics probing resonances in many-body localization</title><link>https://arxiv.org/abs/2601.05177</link><description>arXiv:2601.05177v1 Announce Type: new
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Abstract: We explore the limitations of using imbalance dynamics as a diagnostic tool for many-body localization (MBL) and show that spatial averaging can mask important microscopic features. Focusing on the strongly disordered regime of the random-field XXZ chain, we use state-of-the-art numerical techniques (Krylov time evolution and full diagonalization) to demonstrate that site-resolved spin autocorrelators reveal a rich and complex dynamical behavior that is obscured by the imbalance observable. By analyzing the time evolution and infinite-time limits of these local probes, we reveal resonant structures and rare local instabilities within the MBL phase. These numerical findings are supported by an analytical, few-site toy model that captures the emergence of a multiple-peak structure in local magnetization histograms, which is a hallmark of local resonances. These few-body local effects provide a more detailed understanding of ergodicity-breaking dynamics, and also allow us to explain the finite-size effects of long-time imbalance, and its sensitivity to the initial conditions in quench protocols. Overall, our experimentally testable predictions highlight the necessity of a refined, site-resolved approach to fully understand the complexities of MBL and its connection to rare-region effects.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.05177v1</guid></item><item><title>[cond-mat updates on arXiv.org] Chiral Graviton Modes in Fermionic Fractional Chern Insulators</title><link>https://arxiv.org/abs/2601.05196</link><description>arXiv:2601.05196v1 Announce Type: new
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Abstract: Chiral graviton modes are hallmark collective excitations of Fractional Quantum Hall (FQH) liquids. However, their existence on the lattice, where continuum symmetries that protect them from decay are lost, is still an open and urgent question, especially considering the recent advances in the realization of Fractional Chern Insulators (FCI) in transition metal dichalcogenides and rhombohedral pentalayer graphene. Here we present a comprehensive theoretical and numerical study of graviton-modes in fermionic FCI, and thoroughly demonstrate their existence. We first derive a lattice stress tensor operator in the context of the fermionic Harper-Hofstadter(HH) model which captures the graviton in the flat band limit. Importantly, we discover that such lattice stress-tensor operators are deeply connected to lattice quadrupolar density correlators, readily generalizable to generic Chern bands. We then explicitly show the adiabatic connection between FQH and FCI chiral graviton modes by interpolating from a low flux HH model to a Checkerboard lattice model that hosts a topological flat band. In particular, using state-of-the-art matrix product state and exact diagonalization simulations, we provide strong evidence that chiral graviton modes are long-lived excitations in FCIs despite the lack of continuous symmetries and the scattering with a two-magnetoroton continuum. By means of a careful finite-size analysis, we show that the lattice generates a finite but small intrinsic decay rate for the graviton mode. We discuss the relevance of our results for the exploration of graviton modes in FCI phases realized in solid state settings, as well as cold atom experiments.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.05196v1</guid></item><item><title>[cond-mat updates on arXiv.org] Exact Multimode Quantization of Superconducting Circuits via Boundary Admittance</title><link>https://arxiv.org/abs/2601.04407</link><description>arXiv:2601.04407v1 Announce Type: cross
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Abstract: We show that the Schur complement of the nodal admittance matrix, which reduces a multiport electromagnetic environment to the driving-point admittance $Y_{\mathrm{in}}(s)$ at the Josephson junction, naturally leads to an eigenvalue-dependent boundary condition determining the dressed mode spectrum. This identification provides a four-step quantization procedure: (i) compute or measure $Y_{\mathrm{in}}(s)$, (ii) solve the boundary condition $sY_{\mathrm{in}}(s) + 1/L_J = 0$ for dressed frequencies, (iii) synthesize an equivalent passive network, (iv) quantize with the full cosine nonlinearity retained. Within passive lumped-element circuit theory, we prove that junction participation decays as, we prove that junction participation decays as $O(\omega_n^{-1})$ at high frequencies when the junction port has finite shunt capacitance, ensuring ultraviolet convergence of perturbative sums without imposed cutoffs. The standard circuit QED parameters, coupling strength $g$, anharmonicity $\alpha$, and dispersive shift $\chi$, emerge as controlled limits with explicit validity conditions.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.04407v1</guid></item><item><title>[cond-mat updates on arXiv.org] Higher-Order Knowledge Representations for Agentic Scientific Reasoning</title><link>https://arxiv.org/abs/2601.04878</link><description>arXiv:2601.04878v1 Announce Type: cross
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Abstract: Scientific inquiry requires systems-level reasoning that integrates heterogeneous experimental data, cross-domain knowledge, and mechanistic evidence into coherent explanations. While Large Language Models (LLMs) offer inferential capabilities, they often depend on retrieval-augmented contexts that lack structural depth. Traditional Knowledge Graphs (KGs) attempt to bridge this gap, yet their pairwise constraints fail to capture the irreducible higher-order interactions that govern emergent physical behavior. To address this, we introduce a methodology for constructing hypergraph-based knowledge representations that faithfully encode multi-entity relationships. Applied to a corpus of ~1,100 manuscripts on biocomposite scaffolds, our framework constructs a global hypergraph of 161,172 nodes and 320,201 hyperedges, revealing a scale-free topology (power law exponent ~1.23) organized around highly connected conceptual hubs. This representation prevents the combinatorial explosion typical of pairwise expansions and explicitly preserves the co-occurrence context of scientific formulations. We further demonstrate that equipping agentic systems with hypergraph traversal tools, specifically using node-intersection constraints, enables them to bridge semantically distant concepts. By exploiting these higher-order pathways, the system successfully generates grounded mechanistic hypotheses for novel composite materials, such as linking cerium oxide to PCL scaffolds via chitosan intermediates. This work establishes a "teacherless" agentic reasoning system where hypergraph topology acts as a verifiable guardrail, accelerating scientific discovery by uncovering relationships obscured by traditional graph methods.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.04878v1</guid></item><item><title>[cond-mat updates on arXiv.org] A joint voxel flow - phase field framework for ultra-long microstructure evolution prediction with physical regularization</title><link>https://arxiv.org/abs/2601.04898</link><description>arXiv:2601.04898v1 Announce Type: cross
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Abstract: Phase-field (PF) modeling is a powerful tool for simulating microstructure evolution. To overcome the high computational cost of PF in solving complex PDEs, machine learning methods such as PINNs, convLSTM have been used to predict PF evolution. However, current methods still face shortages of low flexibility, poor generalization and short predicting time length. In this work, we present a joint framework coupling voxel-flow network (VFN) with PF simulations in an alternating manner for long-horizon temporal prediction of microstructure evolution. The VFN iteratively predicts future evolution by learning the flow of pixels from past snapshots, with periodic boundaries preserved in the process. Periodical PF simulations suppresses nonphysical artifacts, reduces accumulated error, and extends reliable prediction time length. The VFN is about 1,000 times faster than PF simulation on GPU. In validation using grain growth and spinodal decomposition, MSE and SSIM remain 6.76% and 0.911 when predicted 18 frames from only 2 input frames, outperforming similar predicting methods. For an ultra-long grain growth prediction for 82 frames from 2 input frames, grain number decreases from 600 to 29 with NMSE of average grain area remaining 1.64%. This joint framework enables rapid, generalized, flexible and physically consistent microstructure forecasting from image-based data for ultra-long time scales.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.04898v1</guid></item><item><title>[cond-mat updates on arXiv.org] Robust Reasoning as a Symmetry-Protected Topological Phase</title><link>https://arxiv.org/abs/2601.05240</link><description>arXiv:2601.05240v1 Announce Type: cross
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Abstract: Large language models suffer from "hallucinations"-logical inconsistencies induced by semantic noise. We propose that current architectures operate in a "Metric Phase," where causal order is vulnerable to spontaneous symmetry breaking. Here, we identify robust inference as an effective Symmetry-Protected Topological phase, where logical operations are formally isomorphic to non-Abelian anyon braiding, replacing fragile geometric interpolation with robust topological invariants. Empirically, we demonstrate a sharp topological phase transition: while Transformers and RNNs exhibit gapless decay, our Holonomic Network reveals a macroscopic "mass gap," maintaining invariant fidelity below a critical noise threshold. Furthermore, in a variable-binding task on $S_{10}$ ($3.6 \times 10^6$ states) representing symbolic manipulation, we demonstrate holonomic generalization: the topological model maintains perfect fidelity extrapolating $100\times$ beyond training ($L=50 \to 5000$), consistent with a theoretically indefinite causal horizon, whereas Transformers lose logical coherence. Ablation studies indicate this protection emerges strictly from non-Abelian gauge symmetry. This provides strong evidence for a new universality class for logical reasoning, linking causal stability to the topology of the semantic manifold.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.05240v1</guid></item><item><title>[cond-mat updates on arXiv.org] Machine learning short-ranged many-body interactions in colloidal systems using descriptors based on Voronoi cells</title><link>https://arxiv.org/abs/2502.19044</link><description>arXiv:2502.19044v2 Announce Type: replace
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Abstract: Machine learning (ML) strategies are opening the door to faster computer simulations, allowing us to simulate more realistic colloidal systems. Since the interactions in colloidal systems are often highly many-body, stemming from e.g. depletion and steric interactions, one of the challenges for these algorithms is capturing the many-body nature of these interactions. In this paper, we introduce a new ML-based strategy for fitting many-body interactions in colloidal systems where the many-body interaction is highly local. To this end, we develop Voronoi-based descriptors for capturing the local environment and fit the effective potential using a simple neural network. To test this algorithm, we consider a simple two-dimensional model for a colloid-polymer mixture, where the colloid-colloid interactions and colloid-polymer interactions are hard-disk like, while the polymers themselves interact as ideal gas particles. We find that a Voronoi-based description is sufficient to accurately capture the many-body nature of this system. Moreover, we find that the Pearson correlation function alone is insufficient to determine the predictive power of the network emphasizing the importance of additional metrics when assessing the quality of ML-based potentials.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2502.19044v2</guid></item><item><title>[cond-mat updates on arXiv.org] Characterizing the cage state of glassy systems and its sensitivity to frozen boundaries</title><link>https://arxiv.org/abs/2507.16339</link><description>arXiv:2507.16339v2 Announce Type: replace
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Abstract: Understanding the role that structure plays in the dynamical arrest observed in glassy systems remains an open challenge. Over the last decade, machine learning (ML) strategies have emerged as an important tool for probing this structure-dynamics relationship, particularly for predicting heterogeneous glassy dynamics from local structure. A recent advancement is the introduction of the cage state, a structural quantity that captures the average positions of particles while rearrangements are forbidden. During the caging regime, linear models trained on the cage state have been shown to outperform more complex ML methods trained on initial configurations only. In this paper, we explore the properties associated with the cage state in more detail to better understand why it serves as such an effective predictor for the dynamics. Specifically, we examine how the cage state in a binary hard-sphere mixture is influenced by both packing fraction and boundary conditions. Our results reveal that, as the system approaches the glassy regime, the cage state becomes increasingly influenced by long-range structural effects. This influence is evident both in its predictive power for particle dynamics and in the internal structure of the cage state, suggesting that the CS might be associated with some form of an amorphous growing structural length scale.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2507.16339v2</guid></item><item><title>[cond-mat updates on arXiv.org] Li+/H+ exchange in solid-state oxide Li-ion conductors</title><link>https://arxiv.org/abs/2509.13477</link><description>arXiv:2509.13477v2 Announce Type: replace
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Abstract: Understanding the moisture stability of oxide Li-ion conductors is important for their practical applications in solid-state batteries. Unlike sulfide or halide conductors, oxide conductors generally better resist degradation when in contact with water, but can still undergo topotactic \ch{Li+}/\ch{H+} exchange (LHX). Here, we combine density functional theory (DFT) calculations with a machine-learning interatomic potential model to investigate the thermodynamic driving force of the LHX reaction for two representative oxide Li-ion conductor families: garnets and NASICONs. Li-stuffed garnets exhibit a strong driving force for proton exchange due to their high Li chemical potential. In contrast, NASICONs demonstrate a higher resistance against proton exchange due to the lower Li chemical potential and the lower O-H bond covalency for polyanion-bonded oxygens. Our findings reveal a critical trade-off: Li stuffing enhances conductivity but increases moisture susceptibility. This study underscores the importance of designing Li-ion conductors that possess both high conductivity and high stability in practical environments.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2509.13477v2</guid></item><item><title>[cond-mat updates on arXiv.org] A universal machine learning model for the electronic density of states</title><link>https://arxiv.org/abs/2508.17418</link><description>arXiv:2508.17418v2 Announce Type: replace-cross
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Abstract: In the last few years several ``universal'' interatomic potentials have appeared, using machine-learning approaches to predict energy and forces of atomic configurations with arbitrary composition and structure, with an accuracy often comparable with that of the electronic-structure calculations they are trained on. Here we demonstrate that these generally-applicable models can also be built to predict explicitly the electronic structure of materials and molecules. We focus on the electronic density of states (DOS), and develop PET-MAD-DOS, a rotationally unconstrained transformer model built on the Point Edge Transformer (PET) architecture, and trained on the Massive Atomistic Diversity (MAD) dataset. We demonstrate our model's predictive abilities on samples from diverse external datasets, showing also that the DOS can be further manipulated to obtain accurate band gap predictions. A fast evaluation of the DOS is especially useful in combination with molecular simulations probing matter in finite-temperature thermodynamic conditions. To assess the accuracy of PET-MAD-DOS in this context, we evaluate the ensemble-averaged DOS and the electronic heat capacity of three technologically relevant systems: lithium thiophosphate (LPS), gallium arsenide (GaAs), and a high entropy alloy (HEA). By comparing with bespoke models, trained exclusively on system-specific datasets, we show that our universal model achieves semi-quantitative agreement for all these tasks. Furthermore, we demonstrate that fine-tuning can be performed using a small fraction of the bespoke data, yielding models that are comparable to, and sometimes better than, fully-trained bespoke models.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2508.17418v2</guid></item><item><title>[RSC - Digital Discovery latest articles] MOFReasoner: Think Like a Scientist-A Reasoning Large Language Model via Knowledge Distillation</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00429B</link><description><div><i><b>Digital Discovery</b></i>, 2025, Accepted Manuscript<br /><b>DOI</b>: 10.1039/D5DD00429B, Paper</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Xuefeng Bai, Zhiling Zheng, Xin Zhang, Hao-Tian Wang, Rui Yang, Jian-Rong Li<br />Large Language Models (LLMs) have potential in transforming chemical research. Nevertheless, their general-purpose design constrains scientific understanding and reasoning within specialized fields like chemistry. In this study, we introduce MOFReasoner,...<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Digital Discovery latest articles</author><pubDate>Fri, 09 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00429B</guid></item><item><title>[npj Computational Materials] Scaling reliable interatomic potentials to complex nuclear alloys via pretrained atomic models</title><link>https://www.nature.com/articles/s41524-025-01950-6</link><description><p>npj Computational Materials, Published online: 09 January 2026; <a href="https://www.nature.com/articles/s41524-025-01950-6">doi:10.1038/s41524-025-01950-6</a></p>Scaling reliable interatomic potentials to complex nuclear alloys via pretrained atomic models</description><author>npj Computational Materials</author><pubDate>Fri, 09 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41524-025-01950-6</guid></item><item><title>[ChemRxiv] Efficient Simulation of Optical Spectra via Machine Learning and Physical Decomposition of Environmental Effects</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-r7kfr?rft_dat=source%3Ddrss</link><description>Simulations of optical spectra can provide key insights to aid experimental interpretation of electronic excitation phenomena. For chromophores in the condensed phase, these spectra, which incorporate the coupling between electronic excitation and molecular and solvent nuclear motions, can be simulated using excitation energies obtained from molecular dynamics simulations of the chromophore and solvent. Here, we present a hybrid scheme that exploits machine learning and physically informed spectral densities to show that as few as 25 ground and excited state energetic gradient calculations can be used to construct models that accurately predict environment-influenced vibronic coupling in optical spectra. We demonstrate our approach for the green fluorescent protein chromophore in water and the cresyl violet chromophore in methanol. We show that our hybrid approach, employing a machine learning model for the high-frequency spectral density and an ab initio parameterized Debye spectral density for the low-frequency, results in systematic improvement of the optical absorption lineshape, leading to a simple machine learning scheme that can be used for simulation of spectral densities and optical spectra.</description><author>ChemRxiv</author><pubDate>Fri, 09 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-r7kfr?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] MolPic: Name/SMILES to Publication-Ready Molecular Figures</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-wjnk9?rft_dat=source%3Ddrss</link><description>Here, we present MolPic, an open-source Python-based software that can be used to generate high-resolution, publication-quality molecular figures directly from compound names or SMILES strings. MolPic supports single-molecule rendering, batch processing, and automated multi-panel 2D figure generation, which are suitable for manuscripts and presentations. MolPic generates a scalable vector graphics (SVG) image as the output. MolPic is fully compatible with Linux, macOS, and cloud-based environments such as Google Colab. The software is archived with a permanent DOI and is freely available to the scientific community.</description><author>ChemRxiv</author><pubDate>Fri, 09 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-wjnk9?rft_dat=source%3Ddrss</guid></item><item><title>[RSC - Chem. Sci. latest articles] Coupling abundant active sites and Ultra-short ion diffusion path: R-VO 2 /carbon nanotubes composite microspheres boosted high performance aqueous ammonium-ion batteries</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC08747C</link><description><div><i><b>Chem. Sci.</b></i>, 2026, Accepted Manuscript<br /><b>DOI</b>: 10.1039/D5SC08747C, Edge Article</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Lin-bo Tang, Xian-Kai Fan, Kaixiong Xiang, Wei Zhou, Weina Deng, Hai Zhu, Liang Chen, Junchao Zheng, Han Chen<br />Ammonium (NH4+) ions as charge carriers have exposed tremendous potentials in aqueous batteries because of the rich resources, ultrafast reaction kinetics, and negligible dendrite risks. However, the choices for cathode...<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Chem. Sci. latest articles</author><pubDate>Fri, 09 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC08747C</guid></item><item><title>[ChemRxiv] Toward On-the-Fly Prediction of Reaction Energetics for High-Throughput Screening</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-np10c?rft_dat=source%3Ddrss</link><description>Machine learning is increasingly used to predict reaction properties such as barrier heights, reaction energies, rates, or yields, as well as the underlying molecular geometries, including transition state structures. While such predictions have the potential to provide mechanistic insight for high-impact applications such as synthesis planning and reaction optimization, the field remains at an early stage of development. This Perspective discusses and critically assesses the current state-of-the art in reaction property prediction, highlighting the key limitations related to data availability and quality, molecular and transformation representations, and machine learning architectures used in both predictive and generative models. A special focus is given on current challenges and on possible paths forward toward efficient and accurate machine learning models for on-the-fly prediction of reaction energetics.</description><author>ChemRxiv</author><pubDate>Fri, 09 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-np10c?rft_dat=source%3Ddrss</guid></item><item><title>[RSC - Chem. Sci. latest articles] Unveiling sodium storage mechanisms in hard carbon via machine learning-driven simulations with accurate site occupation identification</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07068F</link><description><div><i><b>Chem. Sci.</b></i>, 2026, Accepted Manuscript<br /><b>DOI</b>: 10.1039/D5SC07068F, Edge Article</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Zhaoming Wang, Guanghui Shi, Guanghui Wang, Man Wang, Feng Ding, Xiao Wang<br />Hard carbon (HC) has attracted considerable interest as a promising anode material for sodium-ion batteries (SIBs) due to its high specific capacity, excellent cycling stability, and cost-effectiveness. Nevertheless, the sodium...<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Chem. Sci. latest articles</author><pubDate>Fri, 09 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07068F</guid></item><item><title>[Joule] A critical outlook for large-scale all-solid-state batteries</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00450-7?rss=yes</link><description>This commentary examines the practical challenges of scaling all-solid-state batteries, including physical, chemical, electrochemical, mechanical, safety, and cost-related constraints compared with present liquid-based batteries.</description><author>Joule</author><pubDate>Fri, 09 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00450-7?rss=yes</guid></item><item><title>[ChemRxiv] Optical Fiber Chemical Catalysis</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-tqklf?rft_dat=source%3Ddrss</link><description>This paper introduces Optical Fiber Chemistry (OFC) as a fourth-generation catalytic paradigm, distinguished not by incremental improvements in catalyst materials but by a fundamental reconfiguration of the catalytic reaction platform. By employing optical fibers as active photonic control elements, OFC achieves gen- uine coplanar coupling of photons, electrons, and ions within a single membrane electrode, thereby overcoming the intrinsic spatial separation that limits conven- tional thermal catalysis, photocatalysis, electrocatalysis, and photoelectrochemical systems. This architecture establishes an essential physical foundation for pro- grammable chemistry and artificial intelligence–driven chemical systems. Optical Fiber Chemical Catalysis (OFC) represents the most substantial ad- vance in photo–electro and multi-field synergistic catalysis since the seminal demon- stration of photoelectrochemical water splitting by Fujishima and Honda in 1972. Here, we define the concepts of optical fiber chemistry and optical fiber chemi- cal catalysis, delineate their fundamental elements, and formulate the underlying catalytic laws. The OFC framework enables economical, safe, efficient, and high– energy-density scale-up or distributed deployment of optical fiber chemical reaction units, forming modular optical fiber chemical stacks. Moreover, the OFC platform allows chemical reaction processes to be programmably regulated and serves as a core chemical platform for artificial intelligence laboratories and intelligent chemical manufacturing. Catalytic principle: The central feature of OFC is a sandwich-structured optical-fiber membrane electrode, in which rational structural design enables the synergistic coupling of optical fields, electric fields, and proton/ion transport path- ways at a single reaction interface. Within this architecture, photons, electrons, protons, ions, catalysts, reactants, and products coexist at the same interface, allowing photonic excitation and charge separation to occur synchronously and thereby markedly enhancing catalytic efficiency. On the basis of these principles, optical fiber chemical catalysis is expected to enable key reactions—including am- monia synthesis, noble-metal-free fuel cells, organic synthesis, and pharmaceutical manufacturing—under ambient temperature and pressure. Over the next decade, OFC is anticipated to emerge as a major technological route in chemical engineering and catalysis.</description><author>ChemRxiv</author><pubDate>Fri, 09 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-tqklf?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] ReactionForge: Temporal Graph Networks with Cross-Attention and Evidential Learning Surpass State-of-the-Art in Suzuki-Miyaura Yield Prediction</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-lrn7z?rft_dat=source%3Ddrss</link><description>Accurate prediction of chemical reaction yields remains essential for accelerating synthesis optimization, yet current machine learning models face critical limitations in capturing temporal dynamics, providing calibrated uncertainty estimates, and explicitly modeling reactant-to-product transformations. Here we introduce ReactionForge, a novel Temporal Graph Network architecture specifically designed for Suzuki-Miyaura cross-coupling yield prediction that addresses these challenges through five key innovations. First, we implement persistent temporal memory mechanisms using Gated Recurrent Units to track catalyst evolution and reagent dynamics across reaction sequences. Second, we develop cross-attention layers that explicitly compare reactant and product molecular graphs, learning which structural changes most influence reaction outcomes. Third, we incorporate hierarchical graph pooling via Self-Attention Graph Pooling to automatically discover functional group patterns. Fourth, we employ evidential deep learning to provide calibrated epistemic and aleatoric uncertainty in a single forward pass. Fifth, we use multi-task learning with yield, selectivity, and reaction time as joint objectives to improve generalization. Evaluated on 5,760 Suzuki-Miyaura reactions spanning five metal catalysts and diverse substrates, ReactionForge achieves R² = 0.968 ± 0.004 (RMSE = 5.12 ± 0.18%, MAE = 3.89 ± 0.12%), representing statistically significant improvements over YieldGNN (R² = 0.957 ± 0.005, paired t-test p = 0.002) and YieldBERT (R² = 0.810 ± 0.010, p < 0.001). The model provides well-calibrated uncertainty estimates (Expected Calibration Error = 0.031) that enable uncertainty-guided active learning, achieving 37% improved data efficiency over random sampling. Systematic ablation studies reveal that each architectural component contributes measurably to performance, with cross-attention and temporal memory each adding approximately ΔR² = 0.005. Interpretability analysis shows that learned attention weights successfully recover known structure-reactivity relationships, including chloride activation challenges and ligand-substrate compatibility patterns. Despite architectural complexity, ReactionForge trains 28% faster than YieldGNN. This work demonstrates that chemically motivated architectural innovations in graph neural networks can meaningfully advance reaction prediction when properly grounded in mechanistic understanding.</description><author>ChemRxiv</author><pubDate>Fri, 09 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-lrn7z?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Organic ionic plastic crystals composed of tetrahydrothiophenium cation with high conductivity</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-vk62v?rft_dat=source%3Ddrss</link><description>Organic ionic plastic crystals (OIPCs) are soft crystalline materials that exhibit plasticity and ionic conductivity, making them promising candidates for use as solid electrolytes. Previously, IPCs based on pyrrolidinium cations derived from the heterocyclic five-membered ring pyrrolidine have been synthesized, and their ionic conductivities have been reported. However, their performance has not yet achieved the required standard. In this study, we focused on tetrahydrothiophene, another five-membered heterocyclic compound, as a novel cationic structure. A series of novel IPCs was synthesized using tetrahydrothiophenium cations in combination with five different anions, yielding 15 compounds. Thermal analysis was conducted to determine the decomposition and phase-transition temperatures. Six of the synthesized compounds were identified as IPCs, and five were classified as ionic liquids. Among them, the compound 1-ethyltetrahydrothiphenium trifluoro(trifluoromethyl)borate ([C₂tht][CF₃BF₃]), consisting of ethyl-substituted tetrahydrothiophenium cation and CF₃BF₃ anion, exhibited an ionic conductivity of 7.19 × 10⁻⁴ S cm⁻¹ at 25 °C. Notably, [C₂tht][CF₃BF₃] demonstrated an approximately one order of magnitude higher ionic conductivity at room temperature than conventional pyrrolidinium-based IPCs.</description><author>ChemRxiv</author><pubDate>Fri, 09 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-vk62v?rft_dat=source%3Ddrss</guid></item><item><title>[Applied Physics Reviews Current Issue] Ferroelectric and ferroionic multifunctional quantum sensors: Incursion into applications</title><link>https://pubs.aip.org/aip/apr/article/13/1/011306/3377141/Ferroelectric-and-ferroionic-multifunctional</link><description><span class="paragraphSection">Ferroelectric materials are poised to drive the next technological leap through their emergent functionalities, including negative capacitance and resistance, charge accumulation without transport, and spontaneous polarization switching. The discovery of ferroionic material-systems that combine room-temperature ferroelectricity and fast ionic conductivity has opened an unprecedented avenue for multifunctional devices that merge the territories of electronics and ionics. These hybrid materials enable the direct coupling of ionic and electronic order parameters, allowing long-range electrostatic interactions, wireless field communication, and energy transduction across solid–solid and solid–air interfaces. Such capabilities offer potential solutions to long-standing challenges, including the Boltzmann limit in transistor subthreshold operation, voltage amplification without power dissipation, and nonvolatile polarization states with ionic reconfigurability. Beyond conventional applications, ferroionics support a new generation of quantum sensors and adaptive devices, spanning optical, electrical, mechanical, thermal, and magnetic domains. This review provides a comprehensive overview of the conceptual foundations, theoretical frameworks, and experimental progress underlying ferroionic systems, highlighting their role as a bridge between ferroelectrics, solid electrolytes, and correlated quantum materials. Finally, perspectives are offered on how ferroionic coupling may reshape device physics and enable sustainable, self-powered information and energy technologies.</span></description><author>Applied Physics Reviews Current Issue</author><pubDate>Fri, 09 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/13/1/011306/3377141/Ferroelectric-and-ferroionic-multifunctional</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Polydopamine coating on garnet-type solid electrolyte for enhancing interfacial compatibility in solid-state lithium metal batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25048753?dgcid=rss_sd_all</link><description><p>Publication date: 28 February 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 148</p><p>Author(s): Lifeng Guan, Lian Wu, Xinyuan Li, Xuanshuo Zhang, Xiuqing Hao, Jinxiu Wen, Wei Zeng</p></description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Thu, 08 Jan 2026 18:28:37 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25048753</guid></item><item><title>[ScienceDirect Publication: Science Bulletin] Machine learning-based diagnosis of uterine myomas and sarcomas using tumor-educated platelet transcriptomics: a retrospective multicenter study</title><link>https://www.sciencedirect.com/science/article/pii/S2095927325011600?dgcid=rss_sd_all</link><description><p>Publication date: 15 January 2026</p><p><b>Source:</b> Science Bulletin, Volume 71, Issue 1</p><p>Author(s): Xudong Liu, Roujie Huang, Hua Yang, Yu Dong, Lei Li, Zhe Li, Jia Zeng, Qingxia Zhang, Yun Liu, Lei Zhang, Yidi Ma, Lin Zhang, Weijie Tian, Yan You, Yaqian Li, Tianshu Sun, Xiaoyue Zhao, Wei Liu, Le Dang, Zhibo Zhang</p></description><author>ScienceDirect Publication: Science Bulletin</author><pubDate>Thu, 08 Jan 2026 18:28:36 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2095927325011600</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Local Symmetry Breaking Induced Superionic Conductivity in Argyrodites</title><link>http://dx.doi.org/10.1021/jacs.5c17193</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c17193/asset/images/medium/ja5c17193_0006.gif" /></p><div><cite>Journal of the American Chemical Society</cite></div><div>DOI: 10.1021/jacs.5c17193</div></description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Thu, 08 Jan 2026 18:12:05 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c17193</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] OBUSight: Clinically Aligned Generative AI for Ophthalmic Ultrasound Interpretation and Diagnosis</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202515864?af=R</link><description>Advanced Science, EarlyView.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 08 Jan 2026 13:20:36 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202515864</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Synergistic Effects of Solid Electrolyte Mild Sintering and Lithium Surface Passivation for Enhanced Lithium Metal Cycling in All‐Solid‐State Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202521791?af=R</link><description>Advanced Science, EarlyView.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 08 Jan 2026 13:11:10 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202521791</guid></item><item><title>[ScienceDirect Publication: Solid State Ionics] Enhanced ionic conductivity and dielectric performance of CaB₂O₄-doped 2-hydroxyethyl cellulose polymer electrolytes for electrical double layer capacitor applications</title><link>https://www.sciencedirect.com/science/article/pii/S0167273826000019?dgcid=rss_sd_all</link><description><p>Publication date: 1 March 2026</p><p><b>Source:</b> Solid State Ionics, Volume 436</p><p>Author(s): Ranaa M. Almarshedy, Siti Rohana Majid, Ninie Suhana Abdul Manan</p></description><author>ScienceDirect Publication: Solid State Ionics</author><pubDate>Thu, 08 Jan 2026 12:44:16 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0167273826000019</guid></item><item><title>[ScienceDirect Publication: Solid State Ionics] One – Step synthesis of glass ceramic Li<sub>6</sub>PS<sub>5</sub>Cl<sub>1-x</sub>I<sub>x</sub> solid electrolytes for all-solid-state batteries</title><link>https://www.sciencedirect.com/science/article/pii/S0167273825003352?dgcid=rss_sd_all</link><description><p>Publication date: 1 March 2026</p><p><b>Source:</b> Solid State Ionics, Volume 436</p><p>Author(s): Nurcemal Atmaca, Mahir Uenal, Hansen Chang, Oliver Clemens</p></description><author>ScienceDirect Publication: Solid State Ionics</author><pubDate>Thu, 08 Jan 2026 12:44:16 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0167273825003352</guid></item><item><title>[Wiley: Small: Table of Contents] From Composition to Ionic Conductivity: Machine Learning‐Guided Discovery and Experimental Validation of Argyrodite‐Type Lithium‐Ion Electrolytes (Small 2/2026)</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.71850?af=R</link><description>Small, Volume 22, Issue 2, 8 January 2026.</description><author>Wiley: Small: Table of Contents</author><pubDate>Thu, 08 Jan 2026 12:24:03 GMT</pubDate><guid isPermaLink="true">10.1002/smll.71850</guid></item><item><title>[Wiley: Small: Table of Contents] From Composition to Ionic Conductivity: Machine Learning‐Guided Discovery and Experimental Validation of Argyrodite‐Type Lithium‐Ion Electrolytes</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202509918?af=R</link><description>Small, Volume 22, Issue 2, 8 January 2026.</description><author>Wiley: Small: Table of Contents</author><pubDate>Thu, 08 Jan 2026 12:24:03 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202509918</guid></item><item><title>[Wiley: Small: Table of Contents] Organosilane Plasma Enhanced Interfacial Engineering to Boost Inorganic‐Rich Hybrid Solid Electrolyte Interface for Advanced Lithium Metal Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202510297?af=R</link><description>Small, Volume 22, Issue 2, 8 January 2026.</description><author>Wiley: Small: Table of Contents</author><pubDate>Thu, 08 Jan 2026 12:24:03 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202510297</guid></item><item><title>[Wiley: Small: Table of Contents] Conductive Composite Hydrogel with Unsymmetrical Structure as Multimodal Triboelectric Nanogenerators for Machine Learning‐Assisted Motion</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202512928?af=R</link><description>Small, Volume 22, Issue 2, 8 January 2026.</description><author>Wiley: Small: Table of Contents</author><pubDate>Thu, 08 Jan 2026 12:24:03 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202512928</guid></item><item><title>[Wiley: Small: Table of Contents] Adsorption‐Enhanced Bismuth Oxide Efficiently Convert CO2 to Formate Over a Wide Potential Window</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202512691?af=R</link><description>Small, EarlyView.</description><author>Wiley: Small: Table of Contents</author><pubDate>Thu, 08 Jan 2026 11:36:11 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202512691</guid></item><item><title>[Wiley: Small: Table of Contents] MOF in Polymer Electrolytes Raising Ion Transport for Breakthrough Lithium Metal Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202513488?af=R</link><description>Small, EarlyView.</description><author>Wiley: Small: Table of Contents</author><pubDate>Thu, 08 Jan 2026 11:17:57 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202513488</guid></item><item><title>[Recent Articles in Phys. Rev. Lett.] Enhancing Gravitational-Wave Detection: A Machine Learning Pipeline Combination Approach with Robust Uncertainty Quantification</title><link>http://link.aps.org/doi/10.1103/yfb3-fgf2</link><description>Author(s): Gregory Ashton, Ann-Kristin Malz, and Nicolo Colombo<br /><p>Gravitational-wave data from advanced-era interferometric detectors consists of background Gaussian noise, frequent transient artifacts, and rare astrophysical signals. Multiple search algorithms exist to detect the signals from compact binary coalescences, but their varying performance complicates …</p><br />[Phys. Rev. Lett. 136, 011402] Published Thu Jan 08, 2026</description><author>Recent Articles in Phys. Rev. Lett.</author><pubDate>Thu, 08 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/yfb3-fgf2</guid></item><item><title>[Recent Articles in Phys. Rev. B] Universal band center model for the HER activity of nonmetal sites in transition metal dichalcogenides</title><link>http://link.aps.org/doi/10.1103/zhg5-hhpl</link><description>Author(s): Ruixin Xu, Shiqian Cao, Tingting Bo, Yanyu Liu, and Wei Zhou<br /><p>In this work, the hydrogenation performances of nonmetal sites in the transition metal dichalcogenides with the stoichiometry of $M{\mathit{X}}_{2}$ are systematically investigated using the first principles calculations. The trained machine learning model demonstrates that the ${p}_{\mathrm{z}}$ ba…</p><br />[Phys. Rev. B 113, 035305] Published Thu Jan 08, 2026</description><author>Recent Articles in Phys. Rev. B</author><pubDate>Thu, 08 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/zhg5-hhpl</guid></item><item><title>[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Prevention of ubiquitination at K6 and K9 in mutant huntingtin exacerbates disease pathology in a knock-in mouse model</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2527258122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 123, Issue 2, January 2026. <br />SignificanceUbiquitination is a hallmark of mutant huntingtin (mHTT) aggregates, potentially preceding or promoting their degradation or accumulation. However, how specific ubiquitination events shape the fate of mHTT in vivo remains unclear. Here, using ...</description><author>Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents</author><pubDate>Thu, 08 Jan 2026 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2527258122?af=R</guid></item><item><title>[Wiley: Small Methods: Table of Contents] Interfacial Stability and Design Strategies for Halide Solid Electrolytes in High‐Voltage All‐Solid‐State Sodium‐Ion Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smtd.202502179?af=R</link><description>Small Methods, EarlyView.</description><author>Wiley: Small Methods: Table of Contents</author><pubDate>Thu, 08 Jan 2026 06:35:51 GMT</pubDate><guid isPermaLink="true">10.1002/smtd.202502179</guid></item><item><title>[cond-mat updates on arXiv.org] Physically Consistent Machine Learning for Melting Temperature Prediction of Refractory High-Entropy Alloys</title><link>https://arxiv.org/abs/2601.03801</link><description>arXiv:2601.03801v1 Announce Type: new
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Abstract: Predicting the melting temperature (Tm) of multi-component and high-entropy alloys (HEAs) is critical for high-temperature applications but computationally expensive using traditional CALPHAD or DFT methods. In this work, we develop a gradient-boosted decision tree (XGBoost) model to predict Tm for complex alloys based on elemental properties. To ensure physical consistency, we address the issue of data leakage by excluding temperature-dependent thermodynamic descriptors (such as Gibbs free energy of mixing) and instead rely on physically motivated elemental features. The optimized model achieves a coefficient of determination (R2) of 0.948 and a Mean Squared Error (MSE) of 9928 which is about 5% relative error for HEAs on a validation set of approximately 1300 compositions. Crucially, we validate the model using the Valence Electron Concentration (VEC) rule. Without explicit constraints during training, the model successfully captures the known stability transition between BCC and FCC phases at a VEC of approximately 6.87. These results demonstrate that data-driven models, when properly feature-engineered, can capture fundamental metallurgical principles for rapid alloy screening.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 08 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.03801v1</guid></item><item><title>[cond-mat updates on arXiv.org] Material exploration through active learning -- METAL</title><link>https://arxiv.org/abs/2601.03933</link><description>arXiv:2601.03933v1 Announce Type: new
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Abstract: The discovery and design of new materials are paramount in the development of green technologies. High entropy oxides represent one such group that has only been tentatively explored, mainly due to the inherent problem of navigating vast compositional spaces. Thanks to the emergence of machine learning, however, suitable tools are now readily available. Here, the task of finding oxygen carriers for chemical looping processes has been tackled by leveraging active learning-based strategies combined with first-principles calculations. High efficiency and efficacy have, moreover, been achieved by exploiting the power of recently developed machine learning interatomic potentials. Firstly, the proposed approaches were validated based on an established computational framework for identifying high entropy perovskites that can be used in chemical looping air separation and dry reforming. Chief among the insights thus gained was the identification of the best performing strategies, in the form of greedy or Thompson-based sampling based on uncertainty estimates obtained from Gaussian processes. Building on this newfound knowledge, the concept was applied to a more complex problem, namely the discovery of high entropy oxygen carriers for chemical looping oxygen uncoupling. This resulted in both qualitative as well as quantitative outcomes, including lists of specific materials with high oxygen transfer capacities and configurational entropies. Specifically, the best candidates were based on the known oxygen carrier CaMnO3 but also contained a variety of additional species, of which some, e.g., Ti; Co; Cu; and Ti, were expected while others were not, e.g., Y and Sm. The results suggest that adopting active learning approaches is critical in materials discovery, given that these methods are already shifting research practice and soon will be the norm.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 08 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.03933v1</guid></item><item><title>[cond-mat updates on arXiv.org] Transport properties in a model of confined granular mixtures at moderate densities</title><link>https://arxiv.org/abs/2601.04026</link><description>arXiv:2601.04026v1 Announce Type: new
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Abstract: This work derives the Navier--Stokes hydrodynamic equations for a model of a confined, quasi-two-dimensional, $s$-component mixture of inelastic, smooth, hard spheres. Using the inelastic version of the revised Enskog theory, macroscopic balance equations for mass, momentum, and energy are obtained, and constitutive equations for the fluxes are determined through a first-order Chapman--Enskog expansion. As for elastic collisions, the transport coefficients are given in terms of the solutions of a set of coupled linear integral equations. Approximate solutions to these equations for diffusion transport coefficients and shear viscosity are achieved by assuming steady-state conditions and considering leading terms in a Sonine polynomial expansion. These transport coefficients are expressed in terms of the coefficients of restitution, concentration, the masses and diameters of the mixture's components, and the system's density. The results apply to moderate densities and are not limited to particular values of the coefficients of restitution, concentration, mass, and/or diameter ratios. As an application, the thermal diffusion factor is evaluated to analyze segregation driven by temperature gradients and gravity, providing criteria that distinguish whether larger particles accumulate near the hotter or colder boundaries.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 08 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.04026v1</guid></item><item><title>[cond-mat updates on arXiv.org] libMobility: A Python library for hydrodynamics at the Smoluchowski level</title><link>https://arxiv.org/abs/2510.02135</link><description>arXiv:2510.02135v2 Announce Type: replace
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Abstract: Effective hydrodynamic modeling is crucial for accurately predicting fluid-particle interactions in diverse fields such as biophysics and materials science. Developing and implementing hydrodynamic algorithms is challenging due to the complexity of fluid dynamics, necessitating efficient management of large-scale computations and sophisticated boundary conditions. Furthermore, adapting these algorithms for use on massively parallel architectures like GPUs adds an additional layer of complexity. This paper presents the libMobility software library, which offers a suite of CUDA-enabled solvers for simulating hydrodynamic interactions in particulate systems at the Rotne-Prager-Yamakawa (RPY) level. The library facilitates precise simulations of particle displacements influenced by external forces and torques, including both the deterministic and stochastic components. Notable features of libMobility include its ability to handle linear and angular displacements, thermal fluctuations, and various domain geometries effectively. With an interface in Python, libMobility provides comprehensive tools for researchers in computational fluid dynamics and related fields to simulate particle mobility efficiently. This article details the technical architecture, functionality, and wide-ranging applications of libMobility. libMobility is available at https://github.com/stochasticHydroTools/libMobility.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 08 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2510.02135v2</guid></item><item><title>[cond-mat updates on arXiv.org] Kinetic Flux Equations for Ion Exchange in Silicate Glasses</title><link>https://arxiv.org/abs/2601.03207</link><description>arXiv:2601.03207v2 Announce Type: replace
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Abstract: Ion exchange kinetic flux equations have been extensively investigated since the mid-twentieth century and continue to provide a fundamental framework for describing mass transport phenomena in solid materials. Despite the maturity of this field, inconsistencies remain in the literature concerning the definition, dimensional consistency, and physical interpretation of the parameters involved. A rigorous and unified treatment of these equations is therefore essential to ensure the reproducibility and comparability of theoretical and experimental studies. The present study aims to establish a coherent and systematic development of ion exchange kinetic flux equations, with particular emphasis on the consistent definition and dimensional formulation of the relevant physical quantities. Beyond refining the theoretical foundations, this study extends the classical formulation by incorporating the influence of mechanical stress on ion transport and considering cross-term interactions within the framework of linear irreversible thermodynamics. These developments provide a more comprehensive description of ion exchange kinetics, particularly as applied to silicate glasses, where coupling between chemical and mechanical effects plays a crucial role in determining transport behavior and performance.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 08 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.03207v2</guid></item><item><title>[cond-mat updates on arXiv.org] Agentic Exploration of Physics Models</title><link>https://arxiv.org/abs/2509.24978</link><description>arXiv:2509.24978v4 Announce Type: replace-cross
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Abstract: The process of scientific discovery relies on an interplay of observations, analysis, and hypothesis generation. Machine learning is increasingly being adopted to address individual aspects of this process. However, it remains an open challenge to fully automate the heuristic, iterative loop required to discover the laws of an unknown system by exploring it through experiments and analysis, without tailoring the approach to the specifics of a given task. Here, we introduce SciExplorer, an agent that leverages large language model tool-use capabilities to enable exploration of systems without any domain-specific blueprints, and apply it to physical systems that are initially unknown to the agent. We test SciExplorer on a broad set of models spanning mechanical dynamical systems, wave evolution, and quantum many-body physics. Despite using a minimal set of tools, primarily based on code execution, we observe impressive performance on tasks such as recovering equations of motion from observed dynamics and inferring Hamiltonians from expectation values. The demonstrated effectiveness of this setup opens the door towards similar scientific exploration in other domains, without the need for finetuning or task-specific instructions.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 08 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2509.24978v4</guid></item><item><title>[cond-mat updates on arXiv.org] Masgent: An AI-assisted Materials Simulation Agent</title><link>https://arxiv.org/abs/2512.23010</link><description>arXiv:2512.23010v2 Announce Type: replace-cross
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Abstract: Density functional theory (DFT) and machine learning potentials (MLPs) are essential for predicting and understanding materials properties, yet preparing, executing, and analyzing these simulations typically requires extensive scripting, multi-step procedures, and significant high-performance computing (HPC) expertise. These challenges hinder reproducibility and slow down discovery. Here, we introduce Masgent, an AI-assisted materials simulation agent that unifies structure manipulation, automated VASP input generation, DFT workflow construction and analysis, fast MLP-based simulations, and lightweight machine learning (ML) utilities within a single platform. Powered by large language models (LLMs), Masgent enables researchers to perform complex simulation tasks through natural-language interaction, eliminating most manual scripting and reducing setup time from hours to seconds. By standardizing protocols and integrating advanced simulation and data-driven tools, Masgent lowers the barrier to performing state-of-the-art computational methodologies, enabling faster hypothesis testing, pre-screening, and exploratory research for both new and experienced practitioners.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 08 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.23010v2</guid></item><item><title>[ChemRxiv] The unique example of approximation of the electronic term of diatomic molecules by Morse potential. HF, DF, TF.</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-z97tt?rft_dat=source%3Ddrss</link><description>The Morse approximations M1(r) and M2(r) of the 1Σ+ ground state potential curves of three hydrofluoride isotopologues are analyzed. Qualitative differences between HF and heavy isotopologues were found. The result of the HF approximation is a function mainly described by the characteristics of a simple term. For HF, the anharmonicity M1(r) is lower than for M2(r), ωехе<ωехе' and De>D', therefore the curve M1(r) lies above U(r). The M2(r) model is constructed from the values of ωе and De, using the equation De=ωе2/4ωехе, so that its potential curve lies below U(r). For DF and TF, the anharmonicity of M1(r) is greater than of M2(r), ωехе>ωехе' and De<D', therefore, the curve M1(r) lies below U(r) and is outside its potential well with possible intersections. For DF and TF this results in the emergence of inversion of anharmonicity. The shape of U(r) for all three molecules is well described by the Morse formula, their parameters are close to each other, and the differences between HF and heavy isotopologues are small. The differences between the extrapolated and true De values for HF, DF, TF are 290, -230, -460 cm-1, and the differences between the values of anharmonicities ωехе and ωехе' are -0.51, 0.22, 0.30 cm-1, i.e. within 0.5‒1%. In the plot of the differences δ(r)≡U(r)‒M(r), the curves M2(r) of the three isotopologues are in the upper half-plane. The curve δ(r) M1(r) HF, which has not experienced an inversion, is also located there due to the small differences ∆ωехe and ∆De. It has the Morse potential shape, similar to M2(r), and its amplitude is lower than that for M2(r). For DF and TF, the amplitude of δ(r) for M1(r) is greater than δ(r) for M2(r), and their maxima coincide. Inversion of anharmonicity changes only the model term, therefore, a section of the δ(r) curves distorted by the Herzberg anomaly remains in the negative half-plane, because of the broadening of the U(r) term in the lower part of the potential well. This area occupies about half of its depth.</description><author>ChemRxiv</author><pubDate>Thu, 08 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-z97tt?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Machine-Learning-Accelerated Simulations of Vibrational Activation for Controlled Photoisomerization in a Molecular Motor</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-wlm7r?rft_dat=source%3Ddrss</link><description>The precise manipulation of photochemical reactions across broad configurational spaces requires sophisticated design of external control fields. Using the photoisomerization of a molecular motor as a prototype, this study integrates enhanced sampling and active learning to construct accurate machine-learned multi-state potential energy surfaces (PESs). By combining active-learning trajectories with enhanced sampling, our approach efficiently covers substantial reaction regions, enabling trajectory propagation extending to tens of picoseconds at a low computational cost within the machine learning framework. Furthermore, local control theory (LCT) is employed to selectively activate specific vibrational motions, leading to accelerated access to reactive regions, enhanced nonadiabatic transitions, and significantly improved selectivity toward the dominant photoproduct. This combined strategy of machine-learning potentials and LCT offers an efficient and generalizable framework for controlling excited-state dynamics in complex systems.</description><author>ChemRxiv</author><pubDate>Thu, 08 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-wlm7r?rft_dat=source%3Ddrss</guid></item><item><title>[RSC - Chem. Sci. latest articles] Dynamic Protein Structures in Solution: Decoding the Amide I Band with 2D-IR Spectral Libraries and Machine Learning</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC09973K</link><description><div><i><b>Chem. Sci.</b></i>, 2026, Accepted Manuscript<br /><b>DOI</b>: 10.1039/D5SC09973K, Edge Article</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Amy Farmer, Kelly Brown, Sophie E.T. Kendall-Price, Partha Malakar, Gregory M Greetham, Neil Hunt<br />The dynamic three-dimensional structures of proteins dictate their function, but accessing structures in solution at physiological temperatures is challenging. Ultrafast 2D-IR spectroscopy of the protein amide I band produces a...<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Chem. Sci. latest articles</author><pubDate>Thu, 08 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC09973K</guid></item><item><title>[ChemRxiv] Atomistic insights into the chemical stability and ionic transport at Li-metal/Li-Argyrodite interfaces</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-5d9nq-v2?rft_dat=source%3Ddrss</link><description>All solid-state batteries (ASSBs) based on solid-state electrolytes (SSEs) are a novel Li-ion battery technology with the potential of enhanced safety, longer lifetimes, and increased energy density when coupled with the Li-metal anode. Li-Argyrodite (Li6PS5Cl) is a promising SSE with high ionic conductivity, produced using cheap and sustainable precursors, and therefore of interest to both academia and industry. Like many other sulfide-based SSEs, it is however unstable against Li-metal. Using ab-initio and machine-learning methods, we simulate three representative Li-metal/Li-Argyrodite interface models to investigate whether the exact surface termination affects the chemical stability and ion transport capability. We present a systematic approach to create low-energy interfaces by screening 28 low Miller-index surface terminations of Li-argyrodite and coupling them with Li-metal. Custom-made machine-learned interatomic potentials trained on ab-initio data enable the simulation of large interface models with over 2000 atoms for 5 ns. We find that all three interfaces decompose into an amorphous solid-electrolyte interphase (SEI) layer, consisting of Li3P, Li2S and LiCl, which then crystallizes into an antifluorite phase Li2S{1-x-y}P{x}Cl{y}; {x,y = 0.14-0.15}. A two orders of magnitude decrease in Li-ion flux shows that the crystalline SEI layer is a sluggish ion conductor, similar to Li2S. While all three interfaces form the same crystalline SEI layer, the exact rates of the decomposition and crystallisation depend on the actual surface composition. These atomic-level insights could potentially be used to control the SEI formation in sulphide-based SSEs and others.</description><author>ChemRxiv</author><pubDate>Thu, 08 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-5d9nq-v2?rft_dat=source%3Ddrss</guid></item><item><title>[Communications Materials] Extracting and reconstructing knowledge in materials science literature using large language models</title><link>https://www.nature.com/articles/s43246-025-01043-3</link><description><p>Communications Materials, Published online: 08 January 2026; <a href="https://www.nature.com/articles/s43246-025-01043-3">doi:10.1038/s43246-025-01043-3</a></p>Reconstructing knowledge on synthesis routes and properties from inorganic science literature is crucial yet challenging, particularly in maintaining completeness and logical consistency. Here, the authors develop a generalized method based on GPT-4 to fine-tune LLMs, achieving high precision in material synthesis extraction and demonstrating broad applicability across domains, ultimately constructing a comprehensive knowledge graph for materials science optimization.</description><author>Communications Materials</author><pubDate>Thu, 08 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s43246-025-01043-3</guid></item><item><title>[Applied Physics Letters Current Issue] Pressure-induced evolution of the electronic structure and bandgap expansion in MgPbN 2</title><link>https://pubs.aip.org/aip/apl/article/128/1/012107/3376998/Pressure-induced-evolution-of-the-electronic</link><description><span class="paragraphSection">The structural, electronic, and optical properties of MgPbN<sub>2</sub> under pressure have been systematically studied using first-principles calculations combined with the CALYPSO crystal structure prediction method. Two ambient pressure phases (<span style="font-style: italic;">Pna</span>2<sub>1</sub> and I4¯2d) and two high-pressure phases ( R3¯m and Fd3¯m) were identified, all of which are dynamically, mechanically, and thermally stable, and exhibit semiconductor characteristics. Notably, their bandgaps increase with increasing pressure. This phenomenon is primarily attributed to two factors. First, the strengthened orbital coupling under pressure enhances electron cloud overlap, raising the energy of antibonding states (conduction band) and lowering the energy of bonding states (valence band). Second, high pressure alters the distribution of electron clouds, causing electrons to become more localized around the atoms. This localized electron distribution reduces electron transitions between energy bands, thereby increasing the bandgap. This analysis provides a unified view of the electronic structure evolution under compression, linking microscopic orbital interactions to macroscopic observable properties. The calculations and analysis of the optical absorption and reflectivity coefficient suggest that the two high-pressure phases of MgPbN<sub>2</sub> have potential applications in transparent optics and ultraviolet detection. This study provides insights into the role of pressure in tuning optical properties.</span></description><author>Applied Physics Letters Current Issue</author><pubDate>Thu, 08 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apl/article/128/1/012107/3376998/Pressure-induced-evolution-of-the-electronic</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Optimization of porous electrode configuration for organic redox flow battery by machine learning based on back propagation neural network based on firefly</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25047668?dgcid=rss_sd_all</link><description><p>Publication date: 28 February 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 148</p><p>Author(s): Fengming Chu, Yongzhuo Wang, Xi Liu, Tong Liu</p></description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Wed, 07 Jan 2026 18:32:48 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25047668</guid></item><item><title>[ScienceDirect Publication: Progress in Materials Science] Advanced simulations from DFT to machine learning for solid-state hydrogen storage: fundamentals, progresses, challenges and perspectives</title><link>https://www.sciencedirect.com/science/article/pii/S0079642525002336?dgcid=rss_sd_all</link><description><p>Publication date: Available online 6 January 2026</p><p><b>Source:</b> Progress in Materials Science</p><p>Author(s): Shuling Chen, Mei Yang, Shaoyang Shen, Liuzhang Ouyang</p></description><author>ScienceDirect Publication: Progress in Materials Science</author><pubDate>Wed, 07 Jan 2026 18:32:44 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0079642525002336</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Dynamic Li‐S Coordination Boosted Superionic Conduction in Cubic LiBS2 Solid Electrolyte</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202527133?af=R</link><description>Advanced Functional Materials, EarlyView.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Wed, 07 Jan 2026 15:35:52 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202527133</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Sulfonated Cellulose Acetate Nanofibers Induced Zincophilic‐Hydrophobic Interface to Regulate Ion Transport for Long‐Lifespan Zinc‐Iodine Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202522067?af=R</link><description>Advanced Science, EarlyView.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Wed, 07 Jan 2026 15:22:47 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202522067</guid></item><item><title>[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Machine Learning Prediction of Two-Dimensional Polymerization of Nitrogen in FeNx</title><link>http://dx.doi.org/10.1021/acs.jpclett.5c03557</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpclett.5c03557/asset/images/medium/jz5c03557_0007.gif" /></p><div><cite>The Journal of Physical Chemistry Letters</cite></div><div>DOI: 10.1021/acs.jpclett.5c03557</div></description><author>The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)</author><pubDate>Wed, 07 Jan 2026 15:15:41 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpclett.5c03557</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Single-Round Aptamer Discovery Empowered by Machine Learning: Revealing Structure–Function Principles of Target Binding</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202506736?af=R</link><description><p><img src="https://www.chinesechemsoc.org/cms/asset/eee7b718-affd-467a-bfc8-e29ad085279f/keyimage.jpg" /></p><div><cite>CCS Chemistry, Ahead of Print.</cite></div><div>DOI:10.31635/ccschem.025.202506736</div>High-throughput sequencing has revolutionized aptamer discovery; however, the process is still limited by the lack of effective methods to extract structural insights from diverse sequences, crucial for aptamer truncation, optimization, and molecular ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Wed, 07 Jan 2026 11:28:21 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202506736?af=R</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Outside Back Cover: Rhodopsin‐Mimicking Reversible Photo‐Switchable Chloride Channels Based on Azobenzene‐Appended Semiaza‐Bambusurils for Light‐Controlled Ion Transport and Cancer Cell Apoptosis</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.2025-m0501054600?af=R</link><description>Angewandte Chemie International Edition, EarlyView.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Wed, 07 Jan 2026 05:23:27 GMT</pubDate><guid isPermaLink="true">10.1002/anie.2025-m0501054600</guid></item><item><title>[Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents] Thermodynamic Mechanisms of Co‐S Bond Anchoring in Few‐Layered 1T‐MoS2 for Enhanced Capacitive Performance via Spin State Regulation and Ion Diffusion Kinetics</title><link>https://onlinelibrary.wiley.com/doi/10.1002/eem2.70218?af=R</link><description>ENERGY &amp;ENVIRONMENTAL MATERIALS, EarlyView.</description><author>Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents</author><pubDate>Wed, 07 Jan 2026 05:20:14 GMT</pubDate><guid isPermaLink="true">10.1002/eem2.70218</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Customizing Ion Transport by Anionphilic Nanofiber‐Polymer Electrolyte for Stable Zinc Metal Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202519057?af=R</link><description>Advanced Materials, EarlyView.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Wed, 07 Jan 2026 05:17:00 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202519057</guid></item><item><title>[cond-mat updates on arXiv.org] A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse design</title><link>https://arxiv.org/abs/2601.02424</link><description>arXiv:2601.02424v1 Announce Type: new
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Abstract: The synthesis of nanocrystals has been highly dependent on trial-and-error, due to the complex correlation between synthesis parameters and physicochemical properties. Although deep learning offers a potential methodology to achieve generative inverse design, it is still hindered by the scarcity of high-quality datasets that align nanocrystal synthesis routes with their properties. Here, we present the construction of a large-scale, aligned Nanocrystal Synthesis-Property (NSP) database and demonstrate its capability for generative inverse design. To extract structured synthesis routes and their corresponding product properties from literature, we develop NanoExtractor, a large language model (LLM) enhanced by well-designed augmentation strategies. NanoExtractor is validated against human experts, achieving a weighted average score of 88% on the test set, significantly outperforming chemistry-specialized (3%) and general-purpose LLMs (38%). The resulting NSP database contains nearly 160,000 aligned entries and serves as training data for our NanoDesigner, an LLM for inverse synthesis design. The generative capability of NanoDesigner is validated through the successful design of viable synthesis routes for both well-established PbSe nanocrystals and rarely reported MgF2 nanocrystals. Notably, the model recommends a counter-intuitive, non-stoichiometric precursor ratio (1:1) for MgF2 nanocrystals, which is experimentally confirmed as critical for suppressing byproducts. Our work bridges the gap between unstructured literature and data-driven synthesis, and also establishes a powerful human-AI collaborative paradigm for accelerating nanocrystal discovery.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.02424v1</guid></item><item><title>[cond-mat updates on arXiv.org] Protein-Water Energy Transfer via Anharmonic Low-Frequency Vibrations</title><link>https://arxiv.org/abs/2601.02699</link><description>arXiv:2601.02699v1 Announce Type: new
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Abstract: Heat dissipation is ubiquitous in living systems, which constantly convert distinct forms of energy into each other. The transport of thermal energy in liquids and even within proteins is well understood but kinetic energy transfer across a heterogeneous molecular boundary provides additional challenges. Here, we use atomistic molecular dynamics simulations under steady-state conditions to analyze how a protein dissipates surplus thermal energy into the surrounding solvent. We specifically focus on collective degrees of freedom that govern the dynamics of the system from the diffusive regime to mid-infrared frequencies. Using a fully anharmonic analysis of molecular vibrations, we analyzed their vibrational spectra, temperatures, and heat transport efficiencies. We find that the most efficient energy transfer mechanisms are associated with solvent-mediated friction. However, this mechanism only applies to a small number of degrees of freedom of a protein. Instead, less efficient vibrational energy transfer in the far-infrared dominates heat transfer overall due to a large number of vibrations in this frequency range. A notable by-product of this work is a highly sensitive measure of deviations from energy equi-partition in equilibrium systems, which can be used to analyze non-ergodic properties.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.02699v1</guid></item><item><title>[cond-mat updates on arXiv.org] Interplay of Structure and Dynamics in Solid Polymer Electrolytes: a Molecular Dynamics Study of LiPF6/polypropylene carbonate</title><link>https://arxiv.org/abs/2601.02869</link><description>arXiv:2601.02869v1 Announce Type: new
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Abstract: Solid-state batteries (SSB) are emerging as next-generation electrochemical energy storage devices. Achieving high energy density in SSB relies on solid polymer electrolytes (SPE) that are electrochemically stable against both lithium metal and high-potential positive electrodes, two conditions that are difficult to satisfy without chemical degradation. In this work, molecular dynamics simulations are employed to investigate the relationship between structure and dynamics in carbonate-based SPE composed of polypropylene carbonate and lithium hexafluorophosphate (LiPF$_6$), at salt concentrations ranging from 0.32 to 1.21 mol$/$kg. Structural properties are analyzed under ambient pressure at the experimentally relevant temperature $T = 353$ K. Since the slow dynamical processes governing ion transport in these systems are inaccessible to direct molecular dynamics, transport properties are simulated at elevated temperatures up to 900 K and extrapolated to $T = 353$ K using Arrhenius behavior. The results reveal strong ionic correlations, a limited fraction of free ions, and a predominance of negatively charged clusters, especially at high salt concentration. At high temperature, the self-diffusion coefficient of Li$^+$ exceeds that of PF$_6^-$ due to weaker Li$^+$-carbonate and ion-ion interactions. However, at $T = 353$ K, Li$^+$ mobility becomes lower than that of the anion, consistent with typical experimental observations in SPE. As expected, the ionic conductivity $\sigma$ increases with temperature, while at $T = 353$ K it exhibits a maximum for salt concentrations between 1.0 and 1.1 mol$/$kg. Overall, the estimated physico-chemical parameters highlight the key role of ion correlations in SPE and suggest strategies to optimize electrolyte performance. The Arrhenius extrapolation approach used here provides valuable insight into ion transport mechanisms in solid polymer electrolytes.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.02869v1</guid></item><item><title>[cond-mat updates on arXiv.org] DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations</title><link>https://arxiv.org/abs/2601.02938</link><description>arXiv:2601.02938v1 Announce Type: new
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Abstract: In computational physics and materials science, first-principles methods, particularly density functional theory, have become central tools for electronic structure prediction and materials design. Recently, rapid advances in artificial intelligence (AI) have begun to reshape the research landscape, giving rise to the emerging field of deep-learning electronic structure calculations. Despite numerous pioneering studies, the field remains in its early stages; existing software implementations are often fragmented, lacking unified frameworks and standardized interfaces required for broad community adoption. Here we present DeepH-pack, a comprehensive and unified software package that integrates first-principles calculations with deep learning. By incorporating fundamental physical principles into neural-network design, such as the nearsightedness principle and the equivariance principle, DeepH-pack achieves robust cross-scale and cross-material generalizability. This allows models trained on small-scale structures to generalize to large-scale and previously unseen materials. The toolkit preserves first-principles accuracy while accelerating electronic structure calculations by several orders of magnitude, establishing an efficient and intelligent computational paradigm for large-scale materials simulation, high-throughput materials database construction, and AI-driven materials discovery.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.02938v1</guid></item><item><title>[cond-mat updates on arXiv.org] Novel fast Li-ion conductors for solid-state electrolytes from first-principles</title><link>https://arxiv.org/abs/2601.03151</link><description>arXiv:2601.03151v1 Announce Type: new
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Abstract: We present a high-throughput computational screening for fast lithium-ion conductors to identify promising materials for application in all solid-state electrolytes. Starting from more than 30,000 Li-containing experimental structures sourced from Crystallography Open Database, Inorganic Crystal Structure Database and Materials Platform for Data Science, we perform highly automated calculations to identify electronic insulators. On these ~1000 structures, we use molecular dynamics simulations to estimate Li-ion diffusivities using the pinball model, which describes the potential energy landscape of diffusing lithium with accuracy similar to density functional theory while being 200-500 times faster. Then we study the ~60 most promising and previously unknown fast conductors with full first-principles molecular dynamics simulations at several temperatures to estimate their activation barriers. The results are discussed in detail for the 9 fastest conductors, including $Li_7NbO_6$ which shows a remarkable ionic conductivity of ~5 mS/cm at room temperature. We further present the entire screening protocol, including the workflows where the accuracy of the pinball model is improved self-consistently, necessary to automatically running the required calculations and analysing their results.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.03151v1</guid></item><item><title>[cond-mat updates on arXiv.org] Kinetic Flux Equations for Ion Exchange in Silicate Glasses</title><link>https://arxiv.org/abs/2601.03207</link><description>arXiv:2601.03207v1 Announce Type: new
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Abstract: Ion exchange kinetic flux equations have been extensively investigated since the mid-twentieth century and continue to provide a fundamental framework for describing mass transport phenomena in solid materials. Despite the maturity of this field, inconsistencies remain in the literature concerning the definition, dimensional consistency, and physical interpretation of the parameters involved. A rigorous and unified treatment of these equations is therefore essential to ensure the reproducibility and comparability of theoretical and experimental studies. The present study aims to establish a coherent and systematic development of ion exchange kinetic flux equations, with particular emphasis on the consistent definition and dimensional formulation of the relevant physical quantities. Beyond refining the theoretical foundations, this study extends the classical formulation by incorporating the influence of mechanical stress on ion transport and considering cross-term interactions within the framework of linear irreversible thermodynamics. These developments provide a more comprehensive description of ion exchange kinetics, particularly as applied to silicate glasses, where coupling between chemical and mechanical effects plays a crucial role in determining transport behavior and performance.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.03207v1</guid></item><item><title>[cond-mat updates on arXiv.org] Machine Learning H-theorem</title><link>https://arxiv.org/abs/2508.14003</link><description>arXiv:2508.14003v3 Announce Type: replace
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Abstract: H-theorem provides a microscopic foundation of the Second Law of Thermodynamics and is therefore essential to establishing statistical physics, but at the same time, H-theorem has been subject to controversy that in part persists till this day. To better understand H-theorem and its relation to the arrow of time, we study the equilibration of randomly oriented and positioned hard disks with periodic boundary conditions. Using a model based on the DeepSets architecture, which imposes permutation invariance of the particle labels, we train a model to capture the irreversibility of the H-functional.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2508.14003v3</guid></item><item><title>[cond-mat updates on arXiv.org] Bloch oscillations of helicoidal spin-orbit coupled Bose-Einstein condensates in deep optical lattices</title><link>https://arxiv.org/abs/2509.14873</link><description>arXiv:2509.14873v2 Announce Type: replace
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Abstract: We consider helicoidal spin-orbit coupled Bose-Einstein condensates in deep optical lattice and study the dynamics of Bloch oscillation. We show that the variation of helicoidal gauge potential with spin-orbit coupling is different in zero-momentum and plane-wave phases. The characteristics of Bloch oscillation are different in the two phases. In the zero-momentum phase, the Bloch oscillation is harmonic while it is anharmonic in the plane-wave phase. The amplitude of Bloch oscillation is found to be affected by the helicoidal gauge potential and spin-orbit coupling. We examine that the decay of Bloch oscillation caused by mean-field interaction can be managed by helicoidal spin-orbit coupling.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2509.14873v2</guid></item><item><title>[cond-mat updates on arXiv.org] Tuning Separator Chemistry: Improving Zn Anode Compatibility via Functionalized Chitin Nanofibers</title><link>https://arxiv.org/abs/2512.19449</link><description>arXiv:2512.19449v2 Announce Type: replace
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Abstract: Aqueous zinc (Zn) batteries (AZBs) face significant challenges due to the limited compatibility of Zn anodes with conventional separators, leading to dendrite growth, hydrogen evolution reaction (HER), and poor cycling stability. While separator design is crucial for optimizing battery performance, its potential remains underexplored. The commonly used glass fiber (GF) filters were not originally designed as battery separators. To address their limitations, nanochitin derived from waste shrimp shells was used to fabricate separators with varying concentrations of amine and carboxylic functional groups. This study investigates how the type and concentration of these groups influence the separator's properties and performance. In a mild acidic electrolyte that protonates the amine groups, the results showed that the density of both ammonium and carboxylic groups in the separators significantly affected water structure and ionic conductivity. Quasi-Elastic Neutron Scattering (QENS) revealed that low-functionalized chitin, particularly with only ammonium groups, promotes strongly bound water with restricted mobility, thereby enhancing Zn plating and stripping kinetics. These separators exhibit exceptional Zn stability over 2000 hours at low current densities (0.5 mA/cm2), maintaining low overpotentials and stable polarization. Additionally, the full cell consisting of Zn||NaV3O8.1.5H2O showed a cycle life of over 2000 cycles at 2 A/g, demonstrating the compatibility of the nanochitin-based separators with low concentrations of functional surface groups. These results demonstrate the importance of a simple separator design for improving the overall performance of AZBs.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.19449v2</guid></item><item><title>[cond-mat updates on arXiv.org] Unsupervised Topological Phase Discovery in Periodically Driven Systems via Floquet-Bloch State</title><link>https://arxiv.org/abs/2512.24822</link><description>arXiv:2512.24822v2 Announce Type: replace-cross
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Abstract: Floquet engineering offers an unparalleled platform for realizing novel non-equilibrium topological phases. However, the unique structure of Floquet systems, which includes multiple quasienergy gaps, poses a significant challenge to classification using conventional analytical methods. We propose a novel unsupervised machine learning framework that employs a kernel defined in momentum-time ($\boldsymbol{k},t$) space, constructed directly from Floquet-Bloch eigenstates. This approach is intrinsically data-driven and requires no prior knowledge of the underlying topological invariants, providing a fundamental advantage over prior methods that rely on abstract concepts like the micromotion operator or homotopic transformations. Crucially, this work successfully reveals the intrinsic topological characteristics encoded within the Floquet eigenstates themselves. We demonstrate that our method robustly and simultaneously identifies the topological invariants associated with both the $0$-gap and the $\pi$-gap across various symmetry classes (1D AIII, 1D D, and 2D A), establishing a robust methodology for the systematic classification and discovery of complex non-equilibrium topological matter.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.24822v2</guid></item><item><title>[tandf: Materials Research Letters: Table of Contents] Physical-information machine learning for strength and ductility prediction of metastable β titanium alloys</title><link>https://www.tandfonline.com/doi/full/10.1080/21663831.2025.2611741?af=R</link><description>. <br /></description><author>tandf: Materials Research Letters: Table of Contents</author><pubDate>Wed, 07 Jan 2026 02:19:21 GMT</pubDate><guid isPermaLink="true">/doi/full/10.1080/21663831.2025.2611741?af=R</guid></item><item><title>[Nature Communications] Thermotropic liquid-assisted interface management enables efficient and stable perovskite solar cells and modules</title><link>https://www.nature.com/articles/s41467-025-68231-0</link><description><p>Nature Communications, Published online: 07 January 2026; <a href="https://www.nature.com/articles/s41467-025-68231-0">doi:10.1038/s41467-025-68231-0</a></p>In this work, Chang et al. report a thermotropic liquid additive for perovskite solar cells that enables dynamic interface management, simultaneously passivating defects and suppressing ion migration to deliver high efficiency and substantially enhanced operational stability.</description><author>Nature Communications</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41467-025-68231-0</guid></item><item><title>[ChemRxiv] A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-zd9mr-v6?rft_dat=source%3Ddrss</link><description>Computational blind challenges offer critical, unbiased assessment opportunities to assess and accelerate scientific progress, as demonstrated by a breadth of breakthroughs over the last decade. We report the outcomes and key insights from an open science community blind challenge focused on computational methods in drug discovery, using lead optimization data from the AI-driven Structure-enabled Antiviral Platform (ASAP) Discovery Consortium’s pan-coronavirus antiviral discovery program, in partnership with Polaris and the OpenADMET project. This collaborative initiative invited global participants from both academia and industry to develop and apply computational methods to predict the biochemical potency and crystallographic ligand poses of small molecules against key coronavirus targets, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) main protease (Mpro), as well as multiple ADMET assay endpoints, using previously undisclosed comprehensive experimental drug discovery datasets as benchmarks. By evaluating submissions across multiple tasks and compounds, we established performance leaderboards and conducted meta-analyses to assess methodological strengths, common pitfalls, and areas for improvement. This analysis provides a foundation for best practices in real-world machine learning evaluation, grounded in community-driven benchmarking. We also highlight how next-generation platforms, such as Polaris, enable rigorous challenge design, embedded evaluation frameworks, and broad community engagement. This paper reports the collective findings of the challenge, offering a high-level overview of the data, evaluation infrastructure, and top- performing strategies. We further provide context and support for the accompanying papers authored by the challenge participants in this special issue, which explore individual approaches in greater depth. Together, these contributions aim to advance reproducible, trustworthy, and high-impact computational methods in drug discovery, and to explore best practices and pitfalls in future blind challenge design and execution, including planned initiatives for the OpenADMET project.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-zd9mr-v6?rft_dat=source%3Ddrss</guid></item><item><title>[Nature Communications] Uncertainty-aware machine learning to predict non-cancer human toxicity for the global chemicals market</title><link>https://www.nature.com/articles/s41467-025-67374-4</link><description><p>Nature Communications, Published online: 07 January 2026; <a href="https://www.nature.com/articles/s41467-025-67374-4">doi:10.1038/s41467-025-67374-4</a></p>Uncertainty-aware machine learning models predict human toxicity for more than 100,000 chemicals, highlighting potency and uncertainty hotspots to guide safer use and to focus efforts to improve prediction confidence.</description><author>Nature Communications</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41467-025-67374-4</guid></item><item><title>[ChemRxiv] Learning EXAFS from atomic structure through physics-informed machine learning</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-sv3f6?rft_dat=source%3Ddrss</link><description>Extended X-ray absorption fine structure (EXAFS) provides element-specific access to local atomic environments and is widely used to relate structure and reactivity across chemical systems. However, quantitative EXAFS interpretation still relies on manually constructed structural models and extensive parameter tuning, creating a growing bottleneck as experimental datasets increase in size and complexity. Addressing this bottleneck requires a direct and systematic mapping between atomic structure and EXAFS response. Here we introduce AI-EXAFS, a physics-informed graph neural network that predicts full EXAFS spectra directly from three-dimensional atomic coordinates. By formulating the learning problem around the physical principles governing EXAFS signal formation, the model learns transferable structure–spectrum relationships and eliminates the need for user-defined parameter selection at inference. Trained on 86,000 transition-metal complexes, AI-EXAFS reproduces reference theoretical spectra with accuracy consistent with established EXAFS analysis practice and generalizes to experimentally relevant systems, including platinum single-atom catalysts. AI-EXAFS provides an accurate and readily deployable forward model for EXAFS, enabling standardized first-pass structural screening and offering a scalable foundation for future extensions toward more realistic and data-rich EXAFS analysis.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-sv3f6?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Defined by Shape: Elucidating the Molecular Recognition of Dynamic Loops with Covalent Ligands</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-qwc85?rft_dat=source%3Ddrss</link><description>Protein loops harness conformational heterogeneity to perform an array of functions, ranging from catalyzing enzymatic reactions to communicating allosteric signals. Although attractive targets for small molecule modulation, these functional hubs are often considered unligandable due to their lack of well-defined binding pockets and highly dynamic structure. Recent studies, however, have demonstrated the power of covalent chemistry to selectively capture cryptic pockets formed by protein loops. Herein, we leverage machine learning to elucidate the molecular basis of covalent ligand:loop recognition in the transcriptional coactivator Med25. Key to our success was classification by ligand shape prior to model training, which led to descriptive and predictive models. The models were experimentally validated through the synthesis and in vitro testing of novel top-ranked ligands, revealing canonical structure-affinity relationships, including an activity cliff. Further feature analyses identified traditional topological and spatial parameters predictive of binding, and molecular modeling uncovered a potential binding pocket with at least two distinct conformations with high shape complementarity. Collectively, these findings reveal the hidden potential of dynamic loops as specific sites for covalent small molecule modulation, challenging the notion that protein loops are unligandable and demonstrating their capacity for exquisite, shape-based molecular recognition.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-qwc85?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] QuantumPDB: A Workflow for High-Throughput Quantum Cluster Model Generation from Protein Structures</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-w5x1d?rft_dat=source%3Ddrss</link><description>Computational modeling of enzymes provides molecular-level insight into catalysis, but the preparation of quantum mechanical (QM) calculations starting from experimental structures is a significant bottleneck for high-throughput studies. Automated tools developed to accelerate this process may fail to generalize across distinct active site chemistries and geometries. To overcome these limitations, we present QuantumPDB, a Python package that automates the generation of hierarchical coordination/interaction spheres around an active center to create QM cluster models directly from raw protein structures. The workflow integrates structure cleaning, protonation state assignment, and QM calculation setup. It uses chemically meaningful models constructed from contact-based interaction spheres derived from Voronoi tessellation, enabling accurate representation of complex active site geometries. We provide an overview of our modular code and describe how it may be employed to automate high-throughput protein screening. To demonstrate its utility, we curated a dataset of 989 holo-enzymes from the PDB and performed QM calculations on 1,673 enzyme cluster models of 842 of these enzymes. Analysis of computed properties suggests that enzyme environments simulated with density functional theory consistently modulate substrate charge toward neutrality and reduce the substrate dipole moment. This phenomenon appears to be general, even in cases where the active site consists predominantly of neutral residues. By automating and standardizing multi-sphere QM model construction, QuantumPDB provides a robust platform for large-scale, data-driven investigations of proteins.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-w5x1d?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Generalization and Usability of Co-Folded GPCR–Ligand Complexes: A Physics-Guided Assessment</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-1rkqz?rft_dat=source%3Ddrss</link><description>Deep learning co-folding models for end-to-end protein–ligand structure prediction mark a major advance beyond AlphaFold2, yet their reliability for decision-making in drug discovery remains unclear. Here, we benchmark Boltz, a state-of-the-art co-folding model, using a curated set of ligand-bound human G protein-coupled receptors (GPCRs) from families unseen during training. We find that the receptor backbones are generally predicted with reasonable accuracy, but ligand poses often deviate significantly from experimental structures. We then evaluate physics-based refinement with rigid-receptor (Glide) and induced-fit docking (IFD-MD) methods, which recover more than half of the misplaced ligands to near-experimental accuracy. As conventional evaluations for co-folded structures focus on distance-based metrics such as root-mean-squared deviation (RMSD), which can miss subtle but consequential binding-site errors, we carry out a further assessment of Boltz performance using free-energy perturbation (FEP+), which is both accurate and sensitive to starting-structure quality, on curated congeneric ligand series with known binding affinities that target the GPCRs. A significant fraction of the 14 congeneric series tested in this fashion fail to reproduce experimental binding affinities via FEP+ when employing the Boltz generated complex, even when the binding-site RMSD is low in some cases. IFD-MD rescues these failures and restores retrospective FEP signals to native-like level for all of these series. Together, these results delineate current generalization and usability limits of co-folded GPCR–ligand complexes and motivate a workflow that pairs deep learning predictions with physics-based refinement and validation before high-stakes decisions in drug discovery.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-1rkqz?rft_dat=source%3Ddrss</guid></item><item><title>[Communications Physics] Interpolation-based coordinate descent method for parameterized quantum circuits</title><link>https://www.nature.com/articles/s42005-025-02473-8</link><description><p>Communications Physics, Published online: 07 January 2026; <a href="https://www.nature.com/articles/s42005-025-02473-8">doi:10.1038/s42005-025-02473-8</a></p>Parameterized quantum circuits are a common tool in variational quantum algorithms and quantum machine learning. The authors design an interpolation-based coordinate descent method that reconstructs the cost landscape from a few circuit runs and achieves more efficient training than standard gradient and coordinate descent methods in our numerical tests.</description><author>Communications Physics</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s42005-025-02473-8</guid></item><item><title>[RSC - Digital Discovery latest articles] A case study on hybrid machine learning and quantum-informed modelling for solubility prediction of drug compounds in organic solvents</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00456J</link><description><div><p><img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00456J" /></p></div><div><i><b>Digital Discovery</b></i>, 2026, Advance Article<br /><b>DOI</b>: 10.1039/D5DD00456J, Paper</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Weiling Wang, Isabel Cooley, Morgan R. Alexander, Ricky D. Wildman, Anna K. Croft, Blair F. Johnston<br />Machine learning pipeline integrates COSMO-RS and multiple molecular descriptors to predict and interpret solubility across diverse solute–solvent systems.<br />To cite this article before page numbers are assigned, use the DOI form of citation above.<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Digital Discovery latest articles</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00456J</guid></item><item><title>[ChemRxiv] ConfDENSE: A conformer aware electron density based machine learning paradigm for navigating the odorant landscape</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-8tmtg?rft_dat=source%3Ddrss</link><description>Olfaction arises from the interaction of odorants with olfactory receptors, a process shaped by molecular geometry, electron distribution, and conformational preference. We present ConfDENSE, a Set2Set enhanced PointNet model that learns directly from Hirshfeld promolecule electron-density point clouds, preserving full 3D electronic in- formation without downsampling.Despite using no receptor structural data, ConfDENSE accurately identifies bioac- tive conformers from ensemble inputs. For the only available human odorant receptor structures, the model’s selected conformers achieve sub-angstrom RMSDs to crystallo- graphic ligand poses and frequently outperform conventional docking. Combining ConfDENSE with explainability analysis further reveals the substruc- tures most responsible for receptor engagement, aligning with experimental interaction patterns. This ligand-centric and interpretable framework naturally supports phar- macophore extraction and scaffold-based design, enabling identification of conserved binding motifs even when receptor structures are missing. ConfDENSE thus provides a compact, physics-aware approach to computational olfaction, linking electron density, conformational preference, and odorant recognition in a structurally agnostic manner.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-8tmtg?rft_dat=source%3Ddrss</guid></item><item><title>[AI for Science - latest papers] MOSES: combining automated ontology construction with a multi-agent system for explainable chemical knowledge reasoning</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae3127</link><description>The vast and multiscale nature of chemical knowledge—from molecular structures to material properties—presents significant challenges for both human researchers and artificial intelligence (AI) systems. While large language models (LLMs) can process chemical information, they operate as black boxes without transparent reasoning. Here, we present our multi-agent ontology system for explainable knowledge synthesis (MOSES), a framework that combines automated knowledge organization with multi-agent collaboration to create an AI system for interpretable chemical knowledge reasoning. Using supramolecular chemistry as a testbed, we automatically constructed an ontology of over 10 000 classes from 52 publications and developed a multi-agent system that enables transparent knowledge retrieval and reasoning. Evaluations by human experts and LLMs show that MOSES significantly outperforms chemistry-oriented LLMs and leading general-purpose LLMs—including GPT-4.1 and o3—as well as GraphRAG-augmented GPT-4.1 models, on complex chemical questions, achieving superior scores in both direct assessments and Elo ratings. MOSES’s traceable reasoning paths reveal how it constructs answers through iterative refinement rather than probabilistic generation. However, we observe an asymmetry in handling positive versus negative knowledge claims, underscoring fundamental challenges in open-world reasoning. Our work demonstrates a pathway toward AI systems that can reason over complex scientific knowledge in a transparent and explainable manner.</description><author>AI for Science - latest papers</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae3127</guid></item><item><title>[Cell Reports Physical Science] A review of advancements and challenges in nanoplastics detection</title><link>https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00641-1?rss=yes</link><description>Zhang et al. review cutting-edge strategies for separating, detecting, and characterizing nanoplastics across environmental and biological systems. This work bridges advances in spectroscopy, microscopy, mass spectrometry, and machine learning to highlight analytical limitations, emerging solutions, and future opportunities for standardized nanoplastics monitoring.</description><author>Cell Reports Physical Science</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00641-1?rss=yes</guid></item><item><title>[iScience] Large language models for predicting one-year major adverse cardiovascular events in acute coronary syndrome</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(26)00019-2?rss=yes</link><description>Effective risk stratification is crucial for managing acute coronary syndrome (ACS). This study evaluated whether general-purpose large language models (LLMs) can reliably execute the complex clinical reasoning required for cardiovascular prognosis. We quantitatively assessed three LLMs —ChatGPT 4o, DeepSeek R1, and Grok 3—for predicting one-year major adverse cardiovascular events (MACEs), using 29 guideline-recommended features from 903 participants in the LM-ACS cohort and 64 participants in the MIMIC database.</description><author>iScience</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(26)00019-2?rss=yes</guid></item><item><title>[iScience] Diverse Intracellular Trafficking of Insulin Analogs by Machine Learning-based Colocalization and Diffusion Analysis</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02777-4?rss=yes</link><description>Insulin signaling is vital for cellular homeostasis, with dysregulation leading to severe metabolic disorders, particularly diabetes. While insulin analogs are crucial in type-1 diabetes treatment, identifying potential variations in intracellular trafficking and sorting from endogenous insulin is challenging. Current methods rely on static imaging and bulk receptor assays in non-physiological conditions, which disrupts native signaling and masks temporal trafficking dynamics. Here, we directly recorded and compared the intracellular trafficking of ATTO655-labeled recombinant human insulin (HI655) and rapid-acting analog insulin aspart (IAsp655) in live cells.</description><author>iScience</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02777-4?rss=yes</guid></item><item><title>[Applied Physics Letters Current Issue] Rigid-flexible free-standing multichannel carbon nanofiber-silicon composite anodes due to PS-induced channel ordering</title><link>https://pubs.aip.org/aip/apl/article/128/1/013903/3376912/Rigid-flexible-free-standing-multichannel-carbon</link><description><span class="paragraphSection">Silicon (Si)-carbon composite has been regarded as one of the most promising anodes for next-generation lithium-ion batteries (LIBs). However, low mechanical strength of carbon matrix is incapable of maintaining structural stability and electron/ion conductivity of Si anodes. Herein, we employ electrospinning-carbonization to construct free-standing Si@carbon nanofibers with internal ordered channels, uniformly distributed Si nanoparticles, and extraordinary elastic modulus (0.22 GPa) by introducing polystyrene as an oriented filler. The free-standing and rigid-flexible ordered multichannel carbon nanofibers (OM-CNFs) can absorb the volume variation of Si, effectively enhancing the mechanical strength and chemical stability of electrodes. The Si nanoparticles uniformly embedded into the highly conductive OM-CNFs matrix establish a bicontinuous structure and increase the contact area between Si and CNFs, thus boosting the rate capability. Consequently, the Si@OM-CNFs anode delivers an excellent reversible capacity of 939.9 mA h g<sup>−1</sup> even at 5 A g<sup>−1</sup> after 300 cycles. The assembled full-cell with a prelithiated Si@OM-CNFs anode and LiFePO<sub>4</sub> cathode delivers a high energy density of 341 Wh kg<sup>−1</sup>. This work provides insights into the design of high mechanical strength Si/C composite anodes for high-performance LIBs.</span></description><author>Applied Physics Letters Current Issue</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apl/article/128/1/013903/3376912/Rigid-flexible-free-standing-multichannel-carbon</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Optimizing solid electrolyte interphase with KOTF for dendrites-free and high-performance Lithium Metal Batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25048984?dgcid=rss_sd_all</link><description><p>Publication date: 20 February 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 147</p><p>Author(s): Yangtao Zhou, Dequan Huang, Man Zhang, Guangda Yin, Yi Liang, Qichang Pan, Fenghua Zheng, Sijiang Hu, Hongqiang Wang, Qingyu Li</p></description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25048984</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] A hierarchical sandwich Li<sub>6.4</sub>Ga<sub>0.2</sub>La<sub>3</sub>Zr<sub>2</sub>O<sub>12</sub>/ZIF-8@SiO<sub>2</sub>/PVDF-HFP heterostructure with high ionic conductivity for dendrite-free solid-state lithium batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25048583?dgcid=rss_sd_all</link><description><p>Publication date: 20 February 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 147</p><p>Author(s): Hu Wang, Shala Yang, Pengfei Pang, Jiangchao Chen, Yongbo Yan, Mingjie Liao, Dazhi Pang, Zheqi Zhang, Yunyun Zhao, Wenping Liu, Huarui Xu, Guisheng Zhu, Kunpeng Jiang</p></description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25048583</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Hierarchical rose-like VS<sub>2</sub> with sulfur vacancies for high-performance all-solid-state lithium-ion batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25050005?dgcid=rss_sd_all</link><description><p>Publication date: 20 February 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 147</p><p>Author(s): Peidian Chong, Shijie Yu, Lin Zheng, Lei Zhang, Mingdeng Wei, Hongfei Liu, Yi Ren, Jianbiao Wang</p></description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25050005</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Prediction of Lithium-ion battery states via combination of implantable sensors and machine learning</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25047243?dgcid=rss_sd_all</link><description><p>Publication date: 20 February 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 147</p><p>Author(s): Zijun Huang, Feng Tong, Guo Chen, Xuan Chen, Xianjie Xu, Zhefu Mu, Jiaxin Sun, Sheng Huang, Xiuquan Gu</p></description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25047243</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] A review on metal–organic framework-based polymer solid-state electrolytes for energy storage</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25049096?dgcid=rss_sd_all</link><description><p>Publication date: 20 February 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 147</p><p>Author(s): Zelong Zhuang, Xiaojin Yang, Jie Cui, Jingwei Liu, Xueming Yang</p></description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25049096</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Descriptor and graph-based molecular representations in prediction of copolymer properties using machine learning</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625008183?dgcid=rss_sd_all</link><description><p>Publication date: October 2026</p><p><b>Source:</b> Computational Materials Science, Volume 264</p><p>Author(s): Elaheh Kazemi-Khasragh, Rocío Mercado, Carlos Gonzalez, Maciej Haranczyk</p></description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 06 Jan 2026 12:43:08 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625008183</guid></item><item><title>[Recent Articles in Phys. Rev. B] Signatures of coherent phonon transport in frequency-dependent lattice thermal conductivity</title><link>http://link.aps.org/doi/10.1103/kn91-g9hh</link><description>Author(s): Đorđe Dangić<br /><p>Thermal transport in highly anharmonic, amorphous, or alloyed materials often deviates from the predictions of conventional phonon-based models. First-principles approaches have introduced a coherent contribution to account for these deviations and to explain ultralow lattice thermal conductivity, b…</p><br />[Phys. Rev. B 113, 024301] Published Tue Jan 06, 2026</description><author>Recent Articles in Phys. Rev. B</author><pubDate>Tue, 06 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/kn91-g9hh</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Accelerating the Discovery of High‐Conductivity Glass Electrolytes via Machine Learning</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202503813?af=R</link><description>Advanced Energy Materials, EarlyView.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 06 Jan 2026 05:35:12 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202503813</guid></item><item><title>[cond-mat updates on arXiv.org] Physically-Constrained Autoencoder-Assisted Bayesian Optimization for Refinement of High-Dimensional Defect-Sensitive Single Crystalline Structure</title><link>https://arxiv.org/abs/2601.00855</link><description>arXiv:2601.00855v1 Announce Type: new
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Abstract: Physical properties and functionalities of materials are dictated by global crystal structures as well as local defects. To establish a structure-property relationship, not only the crystallographic symmetry but also quantitative knowledge about defects are required. Here we present a hybrid Machine Learning framework that integrates a physically-constrained variational-autoencoder (pcVAE) with different Bayesian Optimization (BO) methods to systematically accelerate and improve crystal structure refinement with resolution of defects. We chose the pyrochlore structured Ho2Ti2O7 as a model system and employed the GSAS2 package for benchmarking crystallographic parameters from Rietveld refinement. However, the function space of these material systems is highly nonlinear, which limits optimizers like traditional Rietveld refinement, into trapping at local minima. Also, these naive methods don't provide an extensive learning about the overall function space, which is essential for large space, large time consuming explorations to identify various potential regions of interest. Thus, we present the approach of exploring the high Dimensional structure parameters of defect sensitive systems via pretrained pcVAE assisted BO and Sparse Axis Aligned BO. The pcVAE projects high-Dimensional diffraction data consisting of thousands of independently measured diffraction orders into a lowD latent space while enforcing scaling invariance and physical relevance. Then via BO methods, we aim to minimize the L2 norm based chisq errors in the real and latent spaces separately between experimental and simulated diffraction patterns, thereby steering the refinement towards potential optimum crystal structure parameters. We investigated and compared the results among different pcVAE assisted BO, non pcVAE assisted BO, and Rietveld refinement.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.00855v1</guid></item><item><title>[cond-mat updates on arXiv.org] A Chemically Grounded Evaluation Framework for Generative Models in Materials Discovery</title><link>https://arxiv.org/abs/2601.00886</link><description>arXiv:2601.00886v1 Announce Type: new
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Abstract: Generative models hold great promise for accelerating materials discovery, but their evaluation often overlooks the chemical validity and stability requirements crucial to real-world applications. Density Functional Theory (DFT) simulations are the gold standard for evaluating such properties but are computationally intensive and inaccessible to non-experts. We propose a chemically grounded, user-friendly evaluation framework that integrates DFT-based stability analysis with commonly used machine learning (ML) metrics. Through systematic experiments using both perturbative and generative methods, we demonstrate that conventional ML metrics can misrepresent chemical feasibility. To address this, we propose new insights on robust metrics and highlight the importance of simulation-informed evaluation for developing reliable generative models in materials science.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.00886v1</guid></item><item><title>[cond-mat updates on arXiv.org] Disordered Dynamics in High Dimensions: Connections to Random Matrices and Machine Learning</title><link>https://arxiv.org/abs/2601.01010</link><description>arXiv:2601.01010v1 Announce Type: new
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Abstract: We provide an overview of high dimensional dynamical systems driven by random matrices, focusing on applications to simple models of learning and generalization in machine learning theory. Using both cavity method arguments and path integrals, we review how the behavior of a coupled infinite dimensional system can be characterized as a stochastic process for each single site of the system. We provide a pedagogical treatment of dynamical mean field theory (DMFT), a framework that can be flexibly applied to these settings. The DMFT single site stochastic process is fully characterized by a set of (two-time) correlation and response functions. For linear time-invariant systems, we illustrate connections between random matrix resolvents and the DMFT response. We demonstrate applications of these ideas to machine learning models such as gradient flow, stochastic gradient descent on random feature models and deep linear networks in the feature learning regime trained on random data. We demonstrate how bias and variance decompositions (analysis of ensembling/bagging etc) can be computed by averaging over subsets of the DMFT noise variables. From our formalism we also investigate how linear systems driven with random non-Hermitian matrices (such as random feature models) can exhibit non-monotonic loss curves with training time, while Hermitian matrices with the matching spectra do not, highlighting a different mechanism for non-monotonicity than small eigenvalues causing instability to label noise. Lastly, we provide asymptotic descriptions of the training and test loss dynamics for randomly initialized deep linear neural networks trained in the feature learning regime with high-dimensional random data. In this case, the time translation invariance structure is lost and the hidden layer weights are characterized as spiked random matrices.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.01010v1</guid></item><item><title>[cond-mat updates on arXiv.org] Predicting Coherent B2 Stability in Ru-Containing Refractory Alloys Through Thermodynamic Elastic Design Maps</title><link>https://arxiv.org/abs/2601.01326</link><description>arXiv:2601.01326v1 Announce Type: new
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Abstract: Ruthenium-based B2 intermetallics are promising for refractory superalloys but are limited by the trade-off between high thermodynamic stability and elastic precipitation strain. We present a physics-guided machine learning framework integrating high-throughput Density Functional Theory (DFT), Random Forest screening, and Symbolic Regression to navigate this design space. This approach resolves the paradox where stoichiometric compounds like RuHf fail to achieve theoretical solvus temperatures. By deriving a closed-form physical law, we quantify the strain penalty: a 1% lattice misfit reduces the solvus temperature by approximately 200 degrees C. This finding confirms that maximizing thermodynamic driving force alone is insufficient. We demonstrate that multi-component alloying is structurally necessary, identifying ternary additions such as Al and Ti as essential lattice-tuning agents that zero out the elastic penalty. This framework establishes a rigorous, constraint-based protocol for alloy design, enabling the precise engineering of zero-misfit, high-stability microstructures.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.01326v1</guid></item><item><title>[cond-mat updates on arXiv.org] Common sublattice-pure van Hove singularities in the kagome superconductors $\textit{A}$V$_{3}$Sb$_{5}$ ($\textit{A}$ = K, Rb, Cs)</title><link>https://arxiv.org/abs/2601.01428</link><description>arXiv:2601.01428v1 Announce Type: new
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Abstract: Kagome materials offer a versatile platform for exploring correlated and topological quantum states, where van Hove singularities (VHSs) play a pivotal role in driving electronic instabilities, exhibiting distinct behaviors depending on electron filling and interaction settings. In the recently discovered kagome superconductors $\textit{A}$V$_{3}$Sb$_{5}$ ($\textit{A}$ = K, Rb, Cs), unconventional charge density wave order, superconductivity, and electronic chirality emerge, yet the nature of VHSs near the Fermi level ($\textit{E}$$_{F}$) and their connection to these exotic orders remain elusive. Here, using high-resolution polarization-dependent angle-resolved photoemission spectroscopy, we uncover a universal electronic structure across $\textit{A}$V$_{3}$Sb$_{5}$ that is distinct from density-functional theory predictions that show noticeable discrepancies. We identify multiple common sublattice-pure VHSs near $\textit{E}$$_{F}$, arising from strong V-$\textit{d}$/Sb-$\textit{p}$ hybridization, which significantly promote bond-order fluctuations and likely drive the observed charge density wave order. These findings provide direct spectroscopic evidence for hybridization-driven VHS formation in kagome metals and establish a unified framework for understanding the intertwined electronic instabilities in $\textit{A}$V$_{3}$Sb$_{5}$.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.01428v1</guid></item><item><title>[cond-mat updates on arXiv.org] A Universal Model for the Resting Potential in Nanofluidic Systems</title><link>https://arxiv.org/abs/2601.01536</link><description>arXiv:2601.01536v1 Announce Type: new
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Abstract: The resting voltage, $V$, which is the potential drop required to nullify the electrical current ($i=0$), is a key characteristic of water desalination and energy harvesting systems that utilize macroscopically large nanoporous membranes, as well as for physiological ion channels subjected to asymmetric salt concentrations. To date, existing analytical expressions for $V_{i=0}$ have been limited to simple scenarios. In this work, we derive a universal, self-consistent theoretical model, devoid of unnecessary oversimplifying assumptions, that unifies all previous models within a single framework. This new model, verified by non-approximated numerical simulations, predicts the behavior of $V_{i=0}$ for arbitrary concentration gradients and for arbitrary diffusion coefficients and ionic valences. We show how the interplay between diffusion coefficients and ionic valencies significantly varies the system response and why it is essential to account for all system parameters. Ultimately, this model can be used to improve experimental interpretation of ion transport measurements.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.01536v1</guid></item><item><title>[cond-mat updates on arXiv.org] Anharmonic lattice dynamics study of phonon transport in layered and molecular-crystal indium iodides</title><link>https://arxiv.org/abs/2601.01766</link><description>arXiv:2601.01766v1 Announce Type: new
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Abstract: Indium iodides, which adopt layered or molecular-crystal-like arrangements depending on composition, are expected to exhibit low lattice thermal conductivity because of their heavy constituent atoms and weak In-I bonding. In this work, we employed first-principles anharmonic lattice dynamics calculations to systematically investigate phonon transport in indium iodides from particle- and wave-like perspectives. The calculated lattice thermal conductivities of both materials remained below 1 W/m-K over a broad temperature range. Notably, the influence of wave-like phonon transport differed by composition: in InI3, the wave-like contribution became comparable to the particle-like Peierls contribution, whereas it remained negligible in InI. We also investigated the thermal transport properties of the experimentally reported high-pressure phase of InI3. Motivated by experimental indications of stacking faults and partial disorder in indium site occupancy within the rhombohedral phase, we constructed several ordered structural models with different stacking sequences. These stacking sequences exhibited no significant energetic preference and had similar lattice thermal conductivities, suggesting that in-plane thermal transport is largely governed by the vibrational properties of the In2I6 layers themselves rather than by the specific stacking sequence. These findings provide insight into phonon transport in layered and molecular-crystal systems with structural complexity and contribute to a broader understanding of thermal transport mechanisms in layered and molecular-crystal-like materials.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.01766v1</guid></item><item><title>[cond-mat updates on arXiv.org] Spin-correlation Driven Ferroelectric Quantum Criticality in a Perovskite Quantum Spin-liquid System, Ba3CuSb2O9</title><link>https://arxiv.org/abs/2601.01906</link><description>arXiv:2601.01906v1 Announce Type: new
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Abstract: Here we have experimentally demonstrated spin-correlation-driven ferroelectric quantum criticality in a prototype quantum spin-liquid system, Ba3CuSb2O9, a quantum phenomenon rarely observed. The dielectric constant follows a clear T2 scaling, showing that the material behaves as a quantum paraelectric without developing ferroelectric order. Magnetically, the system avoids long-range order down to 1.8 K and instead displays a T3/2 dependence in its inverse susceptibility, a hallmark of antiferromagnetic quantum critical fluctuations. Together with known spin-orbital-lattice entanglement in this compound, these signatures point to a strong interplay between spin dynamics and the polar lattice. Our results place this perovskite spin-liquid family at the forefront of this domain and suggest the flexibility of this family in a suitable environment by tuning chemical/ external pressure.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.01906v1</guid></item><item><title>[cond-mat updates on arXiv.org] Score-based diffusion models for accurate crystal-structure inpainting and reconstruction of hydrogen positions</title><link>https://arxiv.org/abs/2601.01959</link><description>arXiv:2601.01959v1 Announce Type: new
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Abstract: Generative AI models, such as score-based diffusion models, have recently advanced the field of computational materials science by enabling the generation of new materials with desired properties. In addition, these models could also be leveraged to reconstruct crystal structures for which partial information is available. One relevant example is the reliable determination of atomic positions occupied by hydrogen atoms in hydrogen-containing crystalline materials. While crucial to the analysis and prediction of many materials properties, the identification of hydrogen positions can however be difficult and expensive, as it is challenging in X-ray scattering experiments and often requires dedicated neutron scattering measurements. As a consequence, inorganic crystallographic databases frequently report lattice structures where hydrogen atoms have been either omitted or inserted with heuristics or by chemical intuition. Here, we combine diffusion models from the field of materials science with techniques originally developed in computer vision for image inpainting. We present how this knowledge transfer across domains enables a much faster and more accurate completion of host structures, compared to unconditioned diffusion models or previous approaches solely based on DFT. Overall, our approach exceeds a success rate of 97% in terms of finding a structural match or predicting a more stable configuration than the initial reference, when starting both from structures that were already relaxed with DFT, or directly from the experimentally determined host structures.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.01959v1</guid></item><item><title>[cond-mat updates on arXiv.org] New RVE concept and FFT methods in micromechanics of composites subjected to body force with compact support</title><link>https://arxiv.org/abs/2601.00822</link><description>arXiv:2601.00822v1 Announce Type: cross
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Abstract: We consider static linear elastic composite materials (CMs) with periodic structure. The core of the proposed methodology is the generation of a novel dataset using specially designed body force fields with compact support (BFCS), enabling a new RVE concept that reduces the infinite periodic medium to a finite domain without boundary artifacts. This functionally reduced RVE is used for translated averaging of direct numerical simulations (DNS) results, efficiently computed via a newly developed FFT-based solver for BFCS loading. The resulting dataset captures localized field responses and is used to train machine learning (ML) and neural networks (NN) models to learn effective nonlocal surrogate operators. These operators accurately predict macroscopic responses while reflecting microstructural features and nonlocal interactions. By accounting for field localization while simultaneously eliminating influences from finite sample size and boundary effects, it provides a physically grounded and data-driven framework for constructing accurate surrogate models for the homogenization of complex materials.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.00822v1</guid></item><item><title>[cond-mat updates on arXiv.org] AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials</title><link>https://arxiv.org/abs/2601.01185</link><description>arXiv:2601.01185v1 Announce Type: cross
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Abstract: Machine-learning potentials (MLIPs) have been a breakthrough for computational physics in bringing the accuracy of quantum mechanics to atomistic modeling. To achieve near-quantum accuracy, it is necessary that neighborhoods contained in the training set are rather close to the ones encountered during a simulation. Yet, constructing a single training set that works well for all applications is, and likely will remain, infeasible, so, one strategy is to supplement training protocols for MLIPs with additional learning methods, such as active learning, or fine-tuning. This strategy, however, yields very complex training protocols that are difficult to implement efficiently, and cumbersome to interpret, analyze, and reproduce.
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To address the above difficulties, we propose AutoPot, a software for automating the construction and archiving of MLIPs. AutoPot is based on BlackDynamite, a software operating parametric tasks, e.g., running simulations, or single-point ab initio calculations, in a highly-parallelized fashion, and Motoko, an event-based workflow manager for orchestrating interactions between the tasks. The initial version of AutoPot supports selection of training configurations from large training candidate sets, and on-the-fly selection from molecular dynamics simulations, using Moment Tensor Potentials as implemented in MLIP-2, and single-point calculations of the selected training configurations using VASP. Another strength of AutoPot is its flexibility: BlackDynamite tasks and orchestrators are Python functions to which own existing code can be easily added and manipulated without writing complex parsers. Therefore, it will be straightforward to add other MLIP and ab initio codes, and manipulate the Motoko orchestrators to implement other training protocols.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.01185v1</guid></item><item><title>[cond-mat updates on arXiv.org] Quantum Extreme Reservoir Computing for Phase Classification of Polymer Alloy Microstructures</title><link>https://arxiv.org/abs/2601.02150</link><description>arXiv:2601.02150v1 Announce Type: cross
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Abstract: Quantum machine learning (QML) is expected to offer new opportunities to process high-dimensional data efficiently by exploiting the exponentially large state space of quantum systems. In this work, we apply quantum extreme reservoir computing (QERC) to the classification of microstructure images of polymer alloys generated using self-consistent field theory (SCFT). While previous QML efforts have primarily focused on benchmark datasets such as MNIST, our work demonstrates the applicability of QERC to engineering data with direct materials relevance. Through numerical experiments, we examine the influence of key computational parameters-including the number of qubits, sampling cost (the number of measurement shots), and reservoir configuration-on classification performance. The resulting phase classifications are depicted as phase diagrams that illustrate the phase transitions in polymer morphology, establishing an understandable connection between quantum model outputs and material behavior. These results illustrate QERC performance on realistic materials datasets and suggest practical guidelines for quantum encoder design and model generalization. This work establishes a foundation for integrating quantum learning techniques into materials informatics.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.02150v1</guid></item><item><title>[cond-mat updates on arXiv.org] Projected branes as platforms for crystalline, superconducting, and higher-order topological phases</title><link>https://arxiv.org/abs/2507.23783</link><description>arXiv:2507.23783v2 Announce Type: replace
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Abstract: Projected branes are constituted by only a small subset of sites of a higher-dimensional crystal, otherwise placed on a hyperplane oriented at an irrational or a rational slope therein, for which the effective Hamiltonian is constructed by systematically integrating out the sites of the parent lattice that fall outside such branes [Commun. Phys. 5, 230 (2022)]. Specifically, when such a brane is constructed from a square lattice, it gives rise to an aperiodic Fibonacci quasi-crystal or its rational approximant in one dimension. In this work, starting from square lattice-based models for topological crystalline insulators, protected by the discrete four-fold rotational ($C_4$) symmetry, we show that the resulting one-dimensional projected topological branes encode all the salient signatures of such phases in terms of robust endpoint zero-energy modes, quantized local topological markers, and mid-gap modes bound to dislocation lattice defects, despite such linear branes being devoid of the $C_4$ symmetry of the original lattice. Furthermore, we show that such branes can also feature all the hallmarks of two-dimensional strong and weak topological superconductors through Majorana zero-energy bound states residing near their endpoints and at the core of dislocation lattice defects, besides possessing suitable quantized local topological markers. Finally, we showcase a successful incarnation of a square lattice-based second-order topological insulator with the characteristic corner-localized zero modes in its geometric descendant one-dimensional quasi-crystalline or crystalline branes that feature a quantized localizer index and endpoint zero-energy modes only when one of its end points passes through a corner of the parent crystal. Possible designer quantum and meta material-based platforms to experimentally harness our theoretically proposed topological branes are discussed.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2507.23783v2</guid></item><item><title>[cond-mat updates on arXiv.org] Efficient Identification of Critical Transitions via Flow Matching: A Scalable Generative Approach for Many-Body Systems</title><link>https://arxiv.org/abs/2508.15318</link><description>arXiv:2508.15318v4 Announce Type: replace
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Abstract: We propose a machine learning framework based on Flow Matching (FM) to identify critical properties in many-body systems efficiently. Using the 2D XY model as a benchmark, we demonstrate that a single network, trained only on configurations from a small ($32\times 32$) lattice at sparse temperature points, effectively generalizes across both temperature and system size. This dual generalization enables two primary applications for large-scale computational physics: (i) a rapid "train-small, predict-large" strategy to locate phase transition points for significantly larger systems ($128\times 128$) without retraining, facilitating efficient finite-size scaling analysis; and (ii) the fast generation of high-fidelity, decorrelated initial spin configurations for large-scale Monte Carlo simulations, providing a robust starting point that bypasses the long thermalization times of traditional samplers. These capabilities arise from the combination of the Flow Matching framework, which learns stable probability-flow vector fields, and the inductive biases of the U-Net architecture that capture scale-invariant local correlations. Our approach offers a scalable and efficient tool for exploring the thermodynamic limit, serving as both a rapid explorer for phase boundaries and a high-performance initializer for high-precision studies.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2508.15318v4</guid></item><item><title>[cond-mat updates on arXiv.org] Graph atomic cluster expansion for foundational machine learning interatomic potentials</title><link>https://arxiv.org/abs/2508.17936</link><description>arXiv:2508.17936v2 Announce Type: replace
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Abstract: Foundational machine learning interatomic potentials that can accurately and efficiently model a vast range of materials are critical for accelerating atomistic discovery. We introduce universal potentials based on the graph atomic cluster expansion (GRACE) framework, trained on several of the largest available materials datasets. Through comprehensive benchmarks, we demonstrate that the GRACE models establish a new Pareto front for accuracy versus efficiency among foundational interatomic potentials. We further showcase their exceptional versatility by adapting them to specialized tasks and simpler architectures via fine-tuning and knowledge distillation, achieving high accuracy while preventing catastrophic forgetting. This work establishes GRACE as a robust and adaptable foundation for the next generation of atomistic modeling, enabling high-fidelity simulations across the periodic table.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2508.17936v2</guid></item><item><title>[cond-mat updates on arXiv.org] DeFecT-FF: Accelerated Modeling of Defects in Cd-Zn--Te-Se-S Compounds Combining High-Throughput DFT and Machine Learning Force Fields</title><link>https://arxiv.org/abs/2510.23514</link><description>arXiv:2510.23514v2 Announce Type: replace
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Abstract: We developed DeFecT-FF, a framework for predicting the energies and ground-state configurations of native point defects, extrinsic dopants, impurities, and defect complexes in zincblende-phase Cd/Zn-Te/Se/S compounds relevant to CdTe-based solar cells. The framework combines high-throughput DFT data with crystal graph-based machine learning force fields (MLFFs) trained to reproduce DFT energies and forces. Alloying at Cd or Te sites offers a route to tune the electronic and defect properties of CdTe absorbers for improved solar efficiency. Given the vast number of possible defect types, charge states, and symmetry-breaking configurations, traditional DFT approaches are computationally prohibitive. Our dataset includes GGA-PBE and HSE06-optimized structures for bulk, alloyed, interface, and grain-boundary systems. Using active learning, we expanded the dataset and trained MLFFs to accurately predict energies across charge states. The model enabled rapid screening and discovery of new low-energy defect configurations, validated through HSE06 calculations with spin-orbit coupling. The DeFecT-FF framework is publicly available as a nanoHUB tool, allowing users to upload crystallographic files, automatically generate defects, and compute defect formation energies versus Fermi level and chemical potentials, greatly reducing the need for expensive DFT simulations.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2510.23514v2</guid></item><item><title>[cond-mat updates on arXiv.org] Time-Temperature-Transformation (TTT) Diagrams to rationalize the nucleation and quenchability of metastable $\alpha$-Li$_3$PS$_4$</title><link>https://arxiv.org/abs/2512.05841</link><description>arXiv:2512.05841v2 Announce Type: replace
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Abstract: $\alpha$-Li$_3$PS$_4$ is a promising solid-state electrolyte with the highest ionic conductivity among its polymorphs. However, its formation presents a thermodynamic paradox: the $\alpha$-phase is the equilibrium phase at high temperature and transforms to the stable $\gamma$-Li$_3$PS$_4$ polymorph when cooled to room temperature; however, $\alpha$-Li$_3$PS$_4$ can be synthesized and quenched in a metastable state via rapid heating at relatively low temperatures. The origin of this synthesizability and anomalous stability has remained elusive. Here, we resolve this paradox by establishing a comprehensive time-temperature-transformation (TTT) diagram, constructed from a computational temperature-size phase diagram and experimental high-time-resolution isothermal measurements. Our density functional theory calculations reveal that at the nanoscale, the $\alpha$-phase is stabilized by its low surface energy, which drastically lowers the nucleation barrier across a wide temperature range. This size-dependent stabilization is directly visualized using in-situ synchrotron X-ray diffraction and electron microscopy, capturing the rapid nucleation of nano-sized $\alpha$-phase and its subsequent slow transformation. This work presents a generalizable framework that integrates thermodynamic and kinetic factors for understanding nucleation and phase transformation mechanisms, providing a rational strategy for the targeted synthesis of functional metastable materials.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.05841v2</guid></item><item><title>[cond-mat updates on arXiv.org] Linear magnetoresistance of two-dimensional massless Dirac fermions in the quantum limit</title><link>https://arxiv.org/abs/2512.13475</link><description>arXiv:2512.13475v2 Announce Type: replace
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Abstract: Linear magnetoresistance is a hallmark of 3D Weyl metals in the quantum limit. Recently, a pronounced linear magnetoresistance has also been observed in 2D graphene [Xin et al., Nature 616, 270 (2023)]. However, a comprehensive theoretical understanding remains elusive. By employing the self-consistent Born approximation, we derive the analytical expressions for the magnetoresistivity of 2D massless Dirac fermions in the quantum limit. Notably, our result recovers the minimum conductivity in the clean limit and reveals a linear dependence of resistivity on the magnetic field for Gaussian impurity potentials, in quantitative agreement with experiments. These findings shed light on the magnetoresistance behavior of 2D Dirac fermions under ultra-high magnetic fields.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.13475v2</guid></item><item><title>[cond-mat updates on arXiv.org] Insights Into Radiation Damage in YBa$_2$Cu$_3$O$_{7-\delta}$ From Machine-Learned Interatomic Potentials</title><link>https://arxiv.org/abs/2512.24430</link><description>arXiv:2512.24430v2 Announce Type: replace
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Abstract: Accurate prediction of radiation damage in YBa$_2$Cu$3$O${7-\delta}$ (YBCO) is essential for assessing the performance of high-temperature superconducting (HTS) tapes in compact fusion reactors. Existing empirical interatomic potentials have been used to model radiation damage in stoichiometric YBCO, but fail to describe oxygen-deficient compositions, which are ubiquitous in industrial Rare-Earth Barium Copper Oxide conductors and strongly influence superconducting properties. In this work, we demonstrate that modern machine-learned interatomic potentials enable predictive modelling of radiation damage in YBCO across a wide range of oxygen stoichiometries, with higher fidelity than previous empirical models. We employ two recently developed approaches: an Atomic Cluster Expansion (ACE) potential and a tabulated Gaussian Approximation Potential (tabGAP). Both models accurately reproduce Density Functional Theory (DFT) energies, forces, and threshold displacement energy distributions, providing a reliable description of atomic-scale collision processes. Molecular dynamics simulations of 5 keV cascades predict enhanced peak defect production and recombination relative to a widely used empirical potential, indicating different cascade evolution. By explicitly varying oxygen deficiency, we show that total defect production depends only weakly on stoichiometry, offering insight into the robustness of radiation damage processes in oxygen-deficient YBCO. Finally, fusion-relevant 300 keV cascade simulations reveal amorphous regions with dimensions comparable to the superconducting coherence length, consistent with electron microscopy observations of neutron-irradiated HTS tapes. These results establish machine-learned interatomic potentials as efficient and predictive tools for investigating radiation damage in YBCO across relevant compositions and irradiation conditions.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.24430v2</guid></item><item><title>[cond-mat updates on arXiv.org] Controllable diatomic molecular quantum thermodynamic machines</title><link>https://arxiv.org/abs/2504.03131</link><description>arXiv:2504.03131v2 Announce Type: replace-cross
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Abstract: We present quantum heat machines using a diatomic molecule modelled by a $q$-deformed potential as a working medium. We analyze the effect of the deformation parameter and other potential parameters on the work output and efficiency of the quantum Otto and quantum Carnot heat cycles. Furthermore, we derive the analytical expressions of work and efficiency as a function of these parameters. Interestingly, our system operates as a quantum heat engine across the range of parameters considered. In addition, the efficiency of the quantum Otto heat engine is seen to be tunable by the deformation parameter. Our findings provide useful insight for understanding the impact of anharmonicity on the design of quantum thermal machines.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2504.03131v2</guid></item><item><title>[ChemRxiv] A Systematic Review of Prompt Engineering Paradigms in Organic Chemistry: Mining, Prediction, and Model Architectures</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-625v3?rft_dat=source%3Ddrss</link><description>Large language models (LLMs) have emerged as transformative tools in scientific research, offering a powerful alternative to traditional, resource-intensive machine learning methods. By leveraging the vast knowledge encoded during pre-training, prompt engineering—the systematic design and optimization of input instructions—enables researchers to guide LLMs toward accurate and domain-specific outputs without updating model parameters. This review presents the first systematic examination of prompt engineering techniques within organic chemistry, focusing on two critical application areas: text mining and predictive tasks. We analyze the core paradigms of prompt engineering, including prompt design, prompt learning, and prompt tuning, and clarify terminological inconsistencies in the literature. The discussion is contextualized within the three principal LLM architectures (encoder-only, decoder-only, and encoder-decoder), with an evaluation of their respective performances on chemistry-related tasks. Furthermore, we explore practical workflows for extracting structured chemical data from texts and knowledge graphs, as well as advanced prompt strategies for reaction condition prediction, reaction optimization, and catalytic performance prediction. This review highlights the significant potential of LLM-driven prompt engineering to accelerate discovery in organic chemistry, from synthetic pathway optimization to automated literature analysis, while also addressing persistent challenges such as the limitations associated with various prompt engineering techniques and the constraints related to each related sub-task. We conclude by outlining future research directions aimed at deepening the integration of chemical knowledge with evolving AI methodologies.</description><author>ChemRxiv</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-625v3?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Ensemble Analyzer: An Open-Source Python Framework for Automated Conformer Ensemble Refinement</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-kq1wk?rft_dat=source%3Ddrss</link><description>Accurate prediction of molecular properties often requires considering the full conformational ensemble rather than a single optimized structure. While modern sampling tools have revolutionized conformational sampling by enabling the rapid generation of ensembles, the subsequent refinement at higher levels of theory remains computationally demanding and technically complex. Existing workflows typically rely on ad hoc scripts and manual intervention, limiting reproducibility and accessibility.
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Here, we present Ensemble Analyzer (EnAn), an open-source Python framework designed to automate the refinement and analysis of conformational ensembles. Built on the Atomic Simulation Environment (ASE), EnAn integrates seamlessly with widely known quantum chemistry engines such as ORCA and Gaussian, providing a modular and extensible architecture that streamlines the entire pipeline. EnAn also supports automated generation and comparison of electronic and vibronic spectra, enabling rapid visualization and interpretation. By minimizing manual data handling and standardizing workflows, EnAn effectively manages reproducible exploration of complex conformational spaces.</description><author>ChemRxiv</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-kq1wk?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Electrostatic Patterning Controls Mineral Nucleation Inside Ferritin</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-8bq1h?rft_dat=source%3Ddrss</link><description>Ferritin protein nanocages store iron across nearly all living organisms. In mammals, two subtypes of ferritin exist: heavy (H) chain and light (L) chain. They have very similar 3D structures, but each performs a slightly different role in iron mineral formation. How sequence differences between the two subtypes affect mineral formation within the nanocages is still unclear. Single-particle reconstruction of cryo-TEM images was used to build models of unmineralized and partially mineralized human L-chain and H-chain ferritin, which showed that subtle differences in protein structure led to changes in the location of mineral formation within ferritin. Explicit-solvent atomistic molecular dynamics (MD) simulations were used to explore how sequence-dependent electrostatics modulate ion transport, cluster formation, and mineral nucleation within the confined environment of human L- and H-chain apoferritin nanocages. Employing NaCl as a computational probe, we show that the internal charge distribution governs ion selectivity and nucleation pathways. Analysis of liquid and solid ionic clusters, combined with Markov State Models (MSMs), reveals that mineralization proceeds through a two-step mechanism involving dense liquid-like precursors that crystallize homogeneously within the cavity. These findings provide molecular insight into how ferritin sequence variability tunes confinement-driven nucleation and suggest general principles for designing biomimetic nanoreactors.</description><author>ChemRxiv</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-8bq1h?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Machine Learning Prediction of Henry Coefficients of Polar and Nonpolar Gases in Covalent Organic Frameworks: Effects of Interlayer Shifts and Functionalization</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-ps69l?rft_dat=source%3Ddrss</link><description>Covalent organic frameworks (COFs) are promising materials for gas separation and carbon capture. Computational techniques based on Monte Carlo simulation can be used to predict the gas adsorption properties of COFs with high accuracy, however they are too inefficient to be deployed in a high-throughput manner for screening large COF databases. In this paper, we systematically train and evaluate a range of machine learning models for predicting the Henry coefficients for CO2 and CH4 gas adsorption in COF materials. To account for COF structural variability, we train our models on datasets that include both chemically functionalized frameworks and interlayer displaced stacking configurations. By comparing predictive performance across descriptor–model architecture combinations, we demonstrate how different models capture the key physical factors governing gas adsorption, including electrostatics, local atomic environments, and van der Waals interactions. Our results therefore provide a framework for building machine learning models for scalable, high-throughput screening of COF materials with targeted gas adsorption properties.</description><author>ChemRxiv</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-ps69l?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Bridging the Gap: Aqueous Phase Organic Synthesis as a Foundation for Advanced Chemical and Biological Discovery</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-0hw4d?rft_dat=source%3Ddrss</link><description>For over a century, synthetic chemistry demanded the rigorous exclusion of water, relying on toxic, volatile organic solvents. This paradigm, while successful, is environmentally and economically costly. This review advocates for a fundamental shift: adopting water not just as a green solvent, but as a transformative medium that reshapes our understanding of reactivity and bridges chemistry with biology. Aqueous phase organic synthesis (APOS) has evolved from accidental observations to a deliberate discipline. Water’s unique properties—its high polarity, hydrogen-bonding capacity, and the hydrophobic effect—make it an active participant. This effect drives reactant aggregation and stabilizes transition states, leading to dramatic rate enhancements in pericyclic and condensation reactions. A broad range of reactions thrive in water, including classical carbon–carbon bond-forming reactions like the aldol and Diels–Alder, and modern cross-couplings (e.g., Suzuki–Miyaura) enabled by water-tolerant catalysts. Multicomponent and click chemistries are particularly powerful. Challenges like poor solubility are addressed with micellar catalysis, water-soluble ligands, and precise control of the reaction microenvironment. Beyond sustainability, APOS drives discovery, often yielding improved selectivities, new pathways, and streamlined syntheses of complex targets like pharmaceuticals. Its greatest promise lies in interfacing with biology. Bioorthogonal reactions, such as azide–alkyne cycloadditions, enable labeling and imaging in living organisms. Aqueous compatibility is essential for in situ therapeutic strategies, chemical biology, and advanced bioconjugation techniques for modifying biomolecules. The future converges with emerging technologies: machine learning to navigate complex aqueous systems, flow chemistry for scalability, and the integration of enzymatic with synthetic catalysis. This points toward a unified chemical-biological engineering paradigm. In conclusion, APOS is a mature, versatile field. It is a cornerstone of green chemistry and a critical bridge to biology, accelerating progress in medicine and materials science. Embracing water is both an environmental imperative and a strategic pathway to the next generation of scientific discovery.
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Introduction: The Solvent Problem in Organic Synthesis
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For generations, organic synthesis has been defined by precise control over molecular structure carried out in rigorously dried environments. Since the emergence of modern organic chemistry in the nineteenth century, water – the very medium that sustains life – has been regarded as an obstacle to chemical transformation. This long-standing assumption has shaped laboratory routines, industrial manufacturing, and chemical education, reinforcing the idea that successful synthesis depends on strict exclusion of moisture. This introduction revisits that historical mindset, evaluates its environmental and economic consequences, and presents the case for a fundamental transition toward aqueous phase organic synthesis (APOS) as a more suitable platform for future chemical and biological innovation.</description><author>ChemRxiv</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-0hw4d?rft_dat=source%3Ddrss</guid></item><item><title>[RSC - Chem. Sci. latest articles] Reactive Fluorescent Probe for Covalent Membrane-Anchoring: Enabling Real-time Imaging of Protein Aggregation Dynamics in Live Cells</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07716H</link><description><div><i><b>Chem. Sci.</b></i>, 2026, Accepted Manuscript<br /><b>DOI</b>: 10.1039/D5SC07716H, Edge Article</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Hongbei Wei, Liren Xu, Ke Wei, Wenhai Bian, Yifan Wen, Wanyi Yu, Hui Zhang, Tony D. James, Xiaolong Sun<br />Aberrant aggregation of membrane proteins is a pathological hallmark of various diseases, including neurodegenerative disorders and cancer. The visualization of membrane protein aggregation has emerged as an important approach for...<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Chem. Sci. latest articles</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07716H</guid></item><item><title>[npj Computational Materials] Composition-dependent dislocation mobility in FeNiCrCoCu high-entropy alloys based on atomistic simulations and machine learning analysis</title><link>https://www.nature.com/articles/s41524-025-01942-6</link><description><p>npj Computational Materials, Published online: 06 January 2026; <a href="https://www.nature.com/articles/s41524-025-01942-6">doi:10.1038/s41524-025-01942-6</a></p>Composition-dependent dislocation mobility in FeNiCrCoCu high-entropy alloys based on atomistic simulations and machine learning analysis</description><author>npj Computational Materials</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41524-025-01942-6</guid></item><item><title>[npj Computational Materials] AI-assisted rapid crystal structure generation towards a target local environment</title><link>https://www.nature.com/articles/s41524-025-01931-9</link><description><p>npj Computational Materials, Published online: 06 January 2026; <a href="https://www.nature.com/articles/s41524-025-01931-9">doi:10.1038/s41524-025-01931-9</a></p>AI-assisted rapid crystal structure generation towards a target local environment</description><author>npj Computational Materials</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41524-025-01931-9</guid></item><item><title>[Applied Physics Letters Current Issue] Polarization-controlled multistate thermal conductivity in ferroelectric HfO 2 thin films</title><link>https://pubs.aip.org/aip/apl/article/128/1/012902/3376407/Polarization-controlled-multistate-thermal</link><description><span class="paragraphSection">While nanoscale electronic logic circuits are well established, the development of nanoscale thermal logic circuits has been slow, mainly due to the absence of efficient and controllable nonvolatile field-effect thermal transistors. In this study, we investigate polarization-dependent thermal conductivity in ferroelectric orthorhombic hafnium dioxide (o-HfO<sub>2</sub>) thin films. Using molecular dynamics simulations with machine learning potentials, we show that a 24-nm-long o-HfO<sub>2</sub> film can exhibit four distinct and stable thermal conductivity states arising from different ferroelectric polarization configurations. Notably, these states achieve a maximum switching ratio of 160.8% under 2% tensile strain. Our results suggest a practical pathway toward nonvolatile field-effect thermal transistors.</span></description><author>Applied Physics Letters Current Issue</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apl/article/128/1/012902/3376407/Polarization-controlled-multistate-thermal</guid></item><item><title>[RSC - Chem. Sci. latest articles] Tailoring terminal groups in sulfonyl solvents to boost compatibility with lithium metal anodes</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC09242F</link><description><div><p><img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5SC09242F" /></p></div><div><i><b>Chem. Sci.</b></i>, 2026, Advance Article<br /><b>DOI</b>: 10.1039/D5SC09242F, Edge Article</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Jinmin Wang, Shuang Wei, Mingming Fang, Angye Li, Qian Zheng, Xubing Dong, Yuanmao Chen, Kang Yuan, Xinyang Yue, Zheng Liang<br />The synthesis of a multifunctional <em>N</em>,<em>N</em>-dimethylsulfamoyl fluoride (DMSF) solvent overcomes key limitations of conventional sulfonyl-based electrolytes including high viscosity, low ionic conductivity, and poor lithium metal compatibility.<br />To cite this article before page numbers are assigned, use the DOI form of citation above.<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Chem. Sci. latest articles</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC09242F</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] Microkinetic modeling of methane activation in Mo/ZSM-5 with machine learning potentials</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725007250?dgcid=rss_sd_all</link><description><p>Publication date: February 2026</p><p><b>Source:</b> Journal of Catalysis, Volume 454</p><p>Author(s): Yanqi Huang, Xiang Ryan Zhou, Brandon C. Bukowski</p></description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Mon, 05 Jan 2026 18:32:11 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725007250</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Thermoplastic Elastomeric Bottlebrush Copolymer Glue Electrolyte Featuring Dual‐Ion Transport Channels and Strong Segregation: Experimental and Simulation Insights (Adv. Funct. Mater. 2/2026)</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.73555?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Mon, 05 Jan 2026 15:13:34 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.73555</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Regulating Metallic Deposition Behavior by Gradient Alloy/Solid Electrolyte Interphase for Durable Na/K Anodes (Adv. Funct. Mater. 2/2026)</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.73556?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Mon, 05 Jan 2026 15:13:34 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.73556</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Autonomous Liquid Metal Droplets Actuated by Ion Diffusion</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202511943?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Mon, 05 Jan 2026 15:13:34 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202511943</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Microcrack‐Structured Visualizable Hydrogel Sensor for Machine Learning‐Assisted Handwriting Recognition</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202512316?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Mon, 05 Jan 2026 15:13:34 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202512316</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Regulating Metallic Deposition Behavior by Gradient Alloy/Solid Electrolyte Interphase for Durable Na/K Anodes</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202515253?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Mon, 05 Jan 2026 15:13:34 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202515253</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Thermoplastic Elastomeric Bottlebrush Copolymer Glue Electrolyte Featuring Dual‐Ion Transport Channels and Strong Segregation: Experimental and Simulation Insights</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202515492?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Mon, 05 Jan 2026 15:13:34 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202515492</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] CGRP Enhances the Regeneration of Bone Defects by Regulating Bone Marrow Mesenchymal Stem Cells Through Promoting ANGPTL4 Secretion by Bone Blood Vessels</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202522295?af=R</link><description>Advanced Science, EarlyView.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Mon, 05 Jan 2026 09:55:39 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202522295</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Pd/Cu Dual Catalysis for Stereodivergent Allylic Alkylation of α-F-Substituted Azaaryl Acetates and Acetamides with Morita–Baylis–Hillman Carbonates</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202506840?af=R</link><description><p><img src="https://www.chinesechemsoc.org/cms/asset/357bdab3-6886-4ca9-a13c-add5fd911bac/keyimage.png" /></p><div><cite>CCS Chemistry, Ahead of Print.</cite></div><div>DOI:10.31635/ccschem.025.202506840</div>A palladium/copper dual catalytic system has been developed for the asymmetric allylic alkylation of Morita–Baylis–Hillman carbonates with α-fluoro-2-azaaryl acetates. This system delivers a series of chiral fluorinated compounds featuring an azaaryl ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Mon, 05 Jan 2026 07:20:22 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202506840?af=R</guid></item><item><title>[Wiley: Carbon Energy: Table of Contents] Strategies to Enhance Ionic Conductivity of Na3Zr2Si2O12 Solid Electrolyte for Advanced Solid‐State Sodium Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/cey2.70157?af=R</link><description>Carbon Energy, EarlyView.</description><author>Wiley: Carbon Energy: Table of Contents</author><pubDate>Mon, 05 Jan 2026 07:00:12 GMT</pubDate><guid isPermaLink="true">10.1002/cey2.70157</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Diffusion‐MRI‐Based Estimation of Cortical Architecture via Machine Learning (DECAM) in Primate Brains</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202512752?af=R</link><description>Advanced Science, EarlyView.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Mon, 05 Jan 2026 05:33:28 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202512752</guid></item><item><title>[cond-mat updates on arXiv.org] Automated electrostatic characterization of quantum dot devices in single- and bilayer heterostructures</title><link>https://arxiv.org/abs/2601.00067</link><description>arXiv:2601.00067v1 Announce Type: new
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Abstract: As quantum dot (QD)-based spin qubits advance toward larger, more complex device architectures, rapid, automated device characterization and data analysis tools become critical. The orientation and spacing of transition lines in a charge stability diagram (CSD) contain a fingerprint of a QD device's capacitive environment, making these measurements useful tools for device characterization. However, manually interpreting these features is time-consuming, error-prone, and impractical at scale. Here, we present an automated protocol for extracting underlying capacitive properties from CSDs. Our method integrates machine learning, image processing, and object detection to identify and track charge transitions across large datasets without manual labeling. We demonstrate this method using experimentally measured data from a strained-germanium single-quantum-well (planar) and a strained-germanium double-quantum-well (bilayer) QD device. Unlike for planar QD devices, CSDs in bilayer germanium heterostructure exhibit a larger set of transitions, including interlayer tunneling and distinct loading lines for the vertically stacked QDs, making them a powerful testbed for automation methods. By analyzing the properties of many CSDs, we can statistically estimate physically relevant quantities, like relative lever arms and capacitive couplings. Thus, our protocol enables rapid extraction of useful, nontrivial information about QD devices.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 05 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.00067v1</guid></item><item><title>[cond-mat updates on arXiv.org] Atomic-Scale Mechanisms of Li-Ion Transport Mediated by Li10GeP2S12 in Composite Solid Polyethylene Oxide Electrolytes</title><link>https://arxiv.org/abs/2601.00112</link><description>arXiv:2601.00112v1 Announce Type: new
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Abstract: Polymer electrolytes incorporating Li$_{10}$GeP$_{2}$S$_{12}$ (LGPS) nanoparticles show promise for solid-state lithium batteries owing to their enhanced ionic conductivity, though the governing mechanisms remain unclear. We combine molecular dynamics (MD) simulations, experimental ionic conductivity measurements, and density functional theory (DFT) calculations to elucidate the effect of LGPS loading on polyethylene oxide (PEO) structure and Li-ion transport. MD and experimental results agree up to 10\% LGPS, showing a volcano-shaped conductivity trend driven by polymer segmental dynamics and interfacial effects. Beyond 10\%, experiments reveal additional conductivity enhancement unexplained by MD, suggesting a distinct transport regime. DFT calculations indicate that Li-ion migration at the PEO|LGPS interface proceeds via vacancy-mediated hopping, with low barriers favored by S-rich interfacial sites and hindered by Ge. These findings link interfacial chemistry and microstructure to Li-ion dynamics, offering guidelines for designing high-performance composite polymer electrolytes.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 05 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.00112v1</guid></item><item><title>[cond-mat updates on arXiv.org] Engineering Ideal 2D Type-II Nodal Line Semimetals via Stacking and Intercalation of van der Waals Layers</title><link>https://arxiv.org/abs/2601.00407</link><description>arXiv:2601.00407v1 Announce Type: new
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Abstract: Two-dimensional type-II topological semimetals (TSMs), characterized by strongly tilted Dirac cones, have attracted intense interest for their unconventional electronic properties and exotic transport behaviors. However, rational design remains challenging due to the sensitivity of band tilting to lattice geometry, atomic coordination, and symmetry constraints. Here, we present a bottom-up approach to engineer ideal type-II nodal line semimetals (NLSMs) in van der Waals bilayers via atomic intercalation. Using monolayer $h$-AlN as a prototype, we show that fluorine-intercalated bilayer AlN (F@BL-AlN) hosts a symmetry-protected type-II nodal loop precisely at the Fermi level, enabled by preserved mirror symmetry ($\mathcal{M}_z$) and tailored interlayer hybridization. First-principles calculations reveal that fluorine not only tunes interlayer coupling but also aligns the Fermi energy with the nodal line, stabilizing the type-II NLSM phase. The system exhibits tunable electronic properties under external electric and strain fields and features a van Hove singularity that induces spontaneous ferromagnetism, realizing a ferromagnetic topological semimetal state. This work provides a versatile platform for designing type-II NLSMs and offers practical guidance for their experimental realization.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 05 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.00407v1</guid></item><item><title>[cond-mat updates on arXiv.org] Kinetic Turing Instability and Emergent Spectral Scaling in Chiral Active Turbulence</title><link>https://arxiv.org/abs/2508.21012</link><description>arXiv:2508.21012v5 Announce Type: cross
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Abstract: The spontaneous emergence of coherent structures from chaotic backgrounds is a hallmark of active biological swarms. We investigate this self-organization by simulating an ensemble of polar chiral active agents that couple locally via a Kuramoto interaction. We demonstrate that the system's transition from chaos to active turbulence is characterized by quantized loop phase currents and coherent clustering, and that this transition is strictly governed by a kinetic Turing instability. By deriving the continuum kinetic theory for the model, we identify that the competition between local phase-locking and active agent motility selects a critical structural wavenumber. The instability then drives the system into a state of developed, active turbulence that exhibits stable, robust power-laws in spectral density, suggestive of universality and consistent with observations from a broad range of turbulent phenomena. Our results bridge the gap between discrete chimera states and continuous fluid turbulence, suggesting that the statistical scaling laws of active turbulence can arise from fundamental kinetic instability criteria.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 05 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2508.21012v5</guid></item><item><title>[cond-mat updates on arXiv.org] New RVE concept in thermoelasticity of periodic composites subjected to compact support loading</title><link>https://arxiv.org/abs/2601.00018</link><description>arXiv:2601.00018v1 Announce Type: cross
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Abstract: This paper introduces an advanced Computational Analytical Micromechanics (CAM) framework for linear thermoelastic composites (CMs) with periodic microstructures. The approach is based on an exact new Additive General Integral Equation (AGIE), formulated for compactly supported loading conditions, such as body forces and localized thermal effects (for example laser heating). In addition, new general integral equations (GIEs) are established for arbitrary mechanical and thermal loading. A unified iterative scheme is developed for solving the static AGIEs, where the compact support of loading serves as a new fundamental training parameter. At the core of the methodology lies a generalized Representative Volume Element (RVE) concept that extends Hill classical definition of the RVE. Unlike conventional RVEs, this generalized RVE is not fixed geometrically but emerges naturally from the characteristic scale of localized loading, thereby reducing the analysis of an infinite periodic medium to a finite, data-driven domain. This formulation automatically filters out nonrepresentative subsets of effective parameters while eliminating boundary effects, edge artifacts, and finite-size sample dependencies. Furthermore, the AGIE-based CAM framework integrates seamlessly with machine learning (ML) and neural network (NN) architectures, supporting the development of accurate, physics-informed surrogate nonlocal operators.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 05 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.00018v1</guid></item><item><title>[cond-mat updates on arXiv.org] Additive general integral equations in thermoelastic micromechanics of composites</title><link>https://arxiv.org/abs/2601.00019</link><description>arXiv:2601.00019v1 Announce Type: cross
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Abstract: This work presents an enhanced Computational Analytical Micromechanics (CAM) framework for the analysis of linear thermoelastic composite materials (CMs) with random microstructure. The proposed approach is grounded in an exact Additive General Integral Equation (AGIE), specifically formulated for compactly supported loading, including both body forces and localized thermal changes (such as those from laser heating). New general integral equations (GIEs) for arbitrary mechanical and thermal loading are proposed. A unified iterative solution strategy is developed for the static AGIE, applicable to CMs with both perfectly and imperfectly bonded interfaces, where the compact support of loading is introduced as a new fundamental training parameter. Central to this methodology is a generalized Representative Volume Element (RVE) concept, which extends Hill classical definition. The resulting RVE is not predefined geometrically, but rather emerges from the characteristic scale of the localized loading, effectively reducing the analysis of an infinite, randomly heterogeneous medium to a finite, data-driven domain. This generalized RVE approach enables automatic exclusion of unrepresentative subsets of effective parameters, while inherently eliminating boundary effects, edge artifacts, and finite size limitations. Moreover, the AGIE-based CAM framework is naturally compatible with machine learning (ML) and neural network (NN) architectures, facilitating the construction of accurate and physically informed surrogate nonlocal operators.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 05 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.00019v1</guid></item><item><title>[cond-mat updates on arXiv.org] Accurate Screening of Functional Materials with Machine-Learning Potential and Transfer-Learned Regressions: Heusler Alloy Benchmark</title><link>https://arxiv.org/abs/2508.20556</link><description>arXiv:2508.20556v2 Announce Type: replace
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Abstract: A machine learning-accelerated high-throughput (HTP) workflow for the discovery of magnetic materials is presented. As a test case, we screened quaternary and all-$d$ Heusler compounds for stable compounds with large magnetocrystalline anisotropy energy ($E_{\mathrm{aniso}}$). Structure optimization and evaluation of formation energy and distance to hull convex were performed using the eSEN-30M-OAM interatomic potential, while local magnetic moments, phonon stability, magnetic stability, and $E_{\mathrm{aniso}}$ were predicted by eSEM models trained on our DxMag Heusler database. A frozen transfer learning strategy was employed to improve accuracy. Candidate compounds identified by the ML-HTP workflow were validated with density functional theory, confirming high predictive precision. We also benchmark the performance of different uMLIPs, discuss the fidelity of local magnetic moment prediction, and demonstrate generalization to unseen elements via transfer learning from a universal interatomic potential.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 05 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2508.20556v2</guid></item><item><title>[cond-mat updates on arXiv.org] Modulation of structural short-range order due to chemical patterning in multi-component amorphous interfacial complexions</title><link>https://arxiv.org/abs/2509.06166</link><description>arXiv:2509.06166v2 Announce Type: replace
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Abstract: Amorphous interfacial complexions have been shown to restrict grain growth and improve damage tolerance in nanocrystalline alloys, with increased chemical complexity stabilizing the complexions themselves. Here, we investigate local chemical composition and structural short-range order in Cu-rich, multi-component nanocrystalline alloys to understand how dopants self-organize within these amorphous complexions and how local structure is altered. High resolution scanning transmission electron microscopy and elemental analysis are used to study both grain boundaries and interphase boundaries, with chemical partitioning observed for both. Notably, the amorphous-crystalline transition region is observed to be enriched in certain dopant species and depleted of others as compared to the interior of the amorphous complexions. This chemical patterning can be explained in terms of the elemental preference for ordered or disordered grain boundary environments. As only a qualitative measure of structural short-range order can be obtained with nanobeam electron diffraction for these specimens, atomistic simulations with a custom-built machine learning interatomic potential are then used to probe how dopant patterning affects local structural state. Increased grain boundary chemical complexity is found to result in a more disordered complexion structure, with segregation to the amorphous-crystalline transition regions driving changes in local structure that are sensitive to dopant ratios. As a whole, the intimate connection between local chemistry and order in amorphous interfacial complexions is demonstrated, opening the door for microstructural engineering within the amorphous complexions themselves.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 05 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2509.06166v2</guid></item><item><title>[cond-mat updates on arXiv.org] Temperature and Pressure Dependent Vibrational Properties of Pristine and Doped Vacancy-Ordered Double Perovskite</title><link>https://arxiv.org/abs/2512.21810</link><description>arXiv:2512.21810v2 Announce Type: replace
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Abstract: Understanding lattice dynamics and structural transitions in vacancy-ordered double perovskites is crucial for developing lead-free optoelectronic materials, yet the role of dopants in modulatingthese properties remains poorly understood. We investigate the vibrational and optical properties of pristine and Antimony(Sb)-doped Cs$_2$TiCl$_6$ vacancy-ordered double perovskite through temperature-dependent Raman spectroscopy (4-273 K), high-pressure studies (0- \~30 GPa), ambient powder XRD, and photoluminescence measurements. Sb doping improves phase purity, reducing impurity-related Raman modes present in pristine samples. Most notably, Sb-doped samples exhibit an anomalous Raman mode M$_1$ appearing exclusively below 100 K at 314-319 cm$^{-1}$, accompanied by changes in the temperature coefficient $\chi$ and anharmonic constant $A$ across this threshold. This behavior is absent in pristine Cs$_2$TiCl$_6$. While these observations suggest possible structural changes at low temperature, the origin of the M$_1$ mode remains unclear and may arise from disorder-activated vibrations, symmetry breaking, or dopant-induced local distortions. Low-temperature structural characterization is needed to confirm the nature of this transition. Photoluminescence shows broad self-trapped exciton emission at 448 nm with broader FWHM in Sb-doped samples (164.73 nm) compared to Bi-doped samples (138.2 nm), consistent with enhanced structural disorder. High-pressure Raman measurements reveal continuous mode hardening to 30 GPa with no phase transitions. These results demonstrate that Sb doping modulates the vibrational properties of Cs$_2$TiCl$_6$, though further investigation is required to establish the underlying mechanisms.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 05 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.21810v2</guid></item><item><title>[cond-mat updates on arXiv.org] Support Vector Machine Kernels as Quantum Propagators</title><link>https://arxiv.org/abs/2502.11153</link><description>arXiv:2502.11153v3 Announce Type: replace-cross
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Abstract: Selecting optimal kernels for regression in physical systems remains a challenge, often relying on trial-and-error with standard functions. In this work, we establish a mathematical correspondence between support vector machine kernels and quantum propagators, demonstrating that kernel efficacy is determined by its spectral alignment with the system's Green's function. Based on this isomorphism, we propose a unified, physics-informed framework for kernel selection and design. For systems with known propagator forms, we derive analytical selection rules that map standard kernels to physical operators. For complex systems where the Green's function is analytically intractable, we introduce a constructive numerical method using the Kernel Polynomial Method with Jackson smoothing to generate custom, physics-aligned kernels. Numerical experiments spanning electrical conductivity, electronic band structure, anharmonic oscillators, and photonic crystals demonstrate that this framework consistently performs well as long as there is an alignment with a Green's function.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 05 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2502.11153v3</guid></item><item><title>[ChemRxiv] Ternary Transition Metal Oxides for Electrochemical Energy Storage: Synthesis, Advantages, Design Strategies, and Future Prospects</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-2jncj-v2?rft_dat=source%3Ddrss</link><description>Ternary transition metal oxides (TTMOs) have emerged as a new class of electrode materials for high-performance energy storage systems, particularly supercapacitors (SCs) and hybrid battery-capacitor devices. This comprehensive review aims to comprehensively survey recent advances in the design, synthesis, and analysis of TTMOs-based nanostructures for supercapacitor (SC) electrodes. It begins by outlining the key concepts related to charge storage mechanisms in SC electrodes, electric double-layer capacitance (EDL), pseudocapacitive (PC), and battery-type (BT) behavior, followed by a clarification of device configurations, including symmetric SC (SSC), asymmetric SC (ASC), and hybrid SC (HSC) devices. This review then examines the fabrication strategies for TTMOs, emphasizing the impact of synthetic approaches on material morphology, crystallinity, and electrochemical performance. Special attention is given to the structure-property relationships that govern ion transport and charge storage dynamics in these materials. The influence of morphological features, including dimensionality, porosity, and hierarchical architecture, on electrochemical behavior is critically analyzed. A comparative evaluation of electrochemical matrices across various TTMO electrodes is presented, highlighting key performance and challenges. Ultimately, the review highlights emerging trends, current limitations, and future research directions that are poised to accelerate the development of next-generation TTMO materials for advanced energy storage technologies.</description><author>ChemRxiv</author><pubDate>Mon, 05 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-2jncj-v2?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Tunable Gelation and Viscoelasticity of Lung-Tissue–Mimetic Sealant: Linking Shear History to In Situ Mechanical Performance through Physics-Informed Machine Learning</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-zkrvp?rft_dat=source%3Ddrss</link><description>Hydrogel-based lung sealants are optimized largely based on formulation and post-cure mechanics, even though deformation history during delivery and placement plays a critical, yet under-characterized, role in determining in situ performance. This disconnect is particularly consequential on the application of lung sealants, where excessive stiffness promotes delamination while insufficient stiffness compromises airtight sealing. Here, we establish a quantitative process–structure–property framework for a gelatin–tannic acid–transglutaminase lung-mimetic sealant by integrating time-resolved rheology with physics-informed machine learning.
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Using small-amplitude oscillatory shear and steady torsional flow at 37 °C, we show that gelation follows a reproducible kinetic clock (storage modulus, G′– loss modulus, G″, crossover at ~100 s; modulus plateau by ~600 s), while the attainable viscoelastic state is strongly governed by deformation history. Increasing oscillatory strain amplitude from 0.1 % to 50 % suppresses network maturation, reducing the post-gelation storage modulus, G′, from ~10⁶ to ~10⁴ Pa, whereas sustained steady shear (0.01–1 s⁻¹) decreases magnitude of complex viscosity by 2–3 orders of magnitude and permanently downshifts elastic moduli, G′–, from ≈3.5×10³ to ≈4×10² Pa. These modulus ranges span the effective compliance of lung parenchyma under physiological tidal strain, delineating mechanical regimes associated with airtight sealing, strain accommodation, or premature interfacial failure. Controlled aeration during the processing of the sealant further decreases stiffness by ~30 % without altering gelation kinetics, providing an additional, physically interpretable compliance-tuning mechanism.
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To unify these effects, we introduce a dimensionless Degree of Gelation (DoG), serving as a rheological state variable that collapses oscillatory and steady-shear histories into a single, time-resolved descriptor of network evolution. Machine-learning models trained on experimentally accessible inputs (time, strain amplitude, shear rate, frequency, aeration) accurately predict DoG (R² ≈ 0.9) and, in inverse mode, identify handling conditions required to achieve targeted in situ mechanical states.
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This rheology–machine-learning framework reframes lung sealant development from a static materials optimization problem to a controllable, process-driven design strategy. By quantitatively linking applicator-level parameters to failure-relevant mechanical outcomes—airtightness, compliance, and resistance to delamination—it provides a mechanistic and generalizable foundation for the design of injectable hydrogels, bioadhesives, and tissue-interfacing</description><author>ChemRxiv</author><pubDate>Mon, 05 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-zkrvp?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Discovery of β-Sheet Peptide Assembly Codes via an Experimentally Validated Predictive Computational Platform</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-4c5h0?rft_dat=source%3Ddrss</link><description>Deciphering the sequence codes governing ordered peptide assemblies remains challenging due to the need to explore vast sequence space with atomic resolution. Here, we present an experimentally validated computational framework combining hybrid-resolution molecular dynamics and machine learning for the discovery of β-sheet-rich amyloid-forming peptides. Through exhaustive simulations of all 8,000 tripeptides, we demonstrate that the widely used aggregation propensity (AP) is not effective in predicting β-sheet assembly. We introduce Amyloid-Like Tendency (ALT), a metric enabled by our hybrid-resolution simulations that effectively identifies cross-β architectures. Leveraging this physics-informed dataset, we further fine-tuned the Uni-Mol model to efficiently screen 160,000 tetrapeptides. Experimental validation of 46 candidates confirmed a predictive accuracy of ~85%, yielding 26 novel amyloid-forming peptides, including multiple hydrogelators. Mechanistic analysis reveals that specific sidechain stacking and central amino acid identity, beyond generic hydrophobicity, dictate ordered assembly. This establishes a scalable pipeline for the targeted design of functional peptide materials.</description><author>ChemRxiv</author><pubDate>Mon, 05 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-4c5h0?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Continued Challenges in High-Throughput Materials Predictions: MatterGen predicts compounds from the training dataset.</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-mkls8-v2?rft_dat=source%3Ddrss</link><description>High-throughput computational tools and generative AI models aim to revolutionise materials discovery by enabling the rapid prediction of novel inorganic compounds. However, these tools face persistent challenges with modelling compounds where multiple elements occupy the same crystallographic site, often leading to misclassification of known disordered phases as new ordered compounds. Recently, Microsoft revealed MatterGen as a tool for predicting new materials. As a proof of concept, MatterGen was used to predict the novel compound TaCr2O6, which was subsequently synthesised in a disordered form as Ta1/3Cr2/3O2. However, detailed crystallographic analysis, presented in this paper, reveals that this is not a novel compound but is identical to the already known compound Ta1/2Cr1/2O2 reported in 1972 and actually included in MatterGen’s training dataset. These findings underscore the necessity of rigorous human verification in AI-assisted materials research, limiting their use for rapid and large-scale prediction of new materials. While generative models hold great promise, their effectiveness is currently limited by unresolved issues with disorder prediction and dataset validation. Improved integration with crystallographic expertise is essential to realise their full potential.</description><author>ChemRxiv</author><pubDate>Mon, 05 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-mkls8-v2?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Pressure- and Temperature-Dependent Ionic Transport in Ag₄Zr₃S₈ Nanocrystal Pellets</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-t7qrx?rft_dat=source%3Ddrss</link><description>Nanocrystal (NC)–derived solid electrolytes provide access to compositionally complex and metastable ion conductors, yet their measured transport properties are often dominated by extrinsic contact effects. We probe the coupled roles of temperature, uniaxial pressure, pellet microstructure, and electrode material on the electrochemical impedance response of Ag₄Zr₃S₈ NC pellets. Ag₄Zr₃S₈ NCs were synthesized via colloidal routes using distinct sulfur sources and consolidated into pellets with controlled surface chemistry. EIS was performed over 298–393 K and 0.43–8.67 MPa using blocking and non-blocking electrodes. Pressure-dependent Nyquist analysis shows impedance is overwhelmingly dominated by interfacial and constriction resistances, with pressure primarily reducing contact limitations rather than altering intrinsic ion transport. Temperature–pressure heat maps of the high-frequency resistance reveal thermally activated transport strongly modulated by mechanical contact and electrode compatibility. These results establish pressure-resolved impedance spectroscopy as a diagnostic framework for separating intrinsic and extrinsic transport contributions in NC-based solid electrolytes.</description><author>ChemRxiv</author><pubDate>Mon, 05 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-t7qrx?rft_dat=source%3Ddrss</guid></item><item><title>[iScience] Mechanistic Evidence for Dibutyl Phthalate as an Environmental Trigger for Inflammatory Bowel Disease</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02880-9?rss=yes</link><description>Dibutyl phthalate (DBP) is a ubiquitous pollutant, but its molecular link to inflammatory bowel disease (IBD) is undefined. We employed an integrative network toxicology framework, combining DBP target databases with IBD patient transcriptomics to address this gap. A computational pipeline using machine learning and molecular docking predicted a core six-gene signature (KYNU, PCK1, LCN2, CDC25B, EPHB4, SORD). We validated these predictions in human colonic epithelial cells (NCM460). DBP exposure induced a pro-inflammatory state and upregulated the core genes, with LCN2 showing the strongest response.</description><author>iScience</author><pubDate>Mon, 05 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02880-9?rss=yes</guid></item><item><title>[Applied Physics Letters Current Issue] Bidirectional optically modulated In 2 O 3 transistors with inorganic solid electrolyte gating for neuromorphic visual systems</title><link>https://pubs.aip.org/aip/apl/article/128/1/013301/3376081/Bidirectional-optically-modulated-In2O3</link><description><span class="paragraphSection">Inspired by retinal visual processing, we demonstrate a bidirectional optically controlled neuromorphic In<sub>2</sub>O<sub>3</sub> transistor based on an inorganic solid electrolyte Li<sub>1+x</sub>Al<sub>x</sub>Ti<sub>2-x</sub>(PO<sub>4</sub>)<sub>3</sub> (LATP) gate dielectric. The device exhibits light-controlled bidirectional visual bipolar cell behavior, exhibiting excitatory and inhibitory responses under ultraviolet (275 nm) and green light (520 nm) stimuli, respectively. X-ray photoelectron spectroscopy and capacitance–frequency measurements reveal that mobile Li<sup>+</sup> ions in the LATP dielectric layer can adsorb electrons and form Coulombic binding states, thereby dynamically modulating photogenerated carrier transport. Optical pulse trains dynamically regulate the channel current, enabling bidirectional optical neural plasticity. Furthermore, a large-area device array was employed for image encoding and retinal damage simulation, highlighting its potential for artificial vision and neuromorphic computing. These findings establish an effective strategy for developing bidirectional optical, reconfigurable, and large-scale integrable neuromorphic devices, providing additional insights into the role of dielectric layer ion dynamics in neuromorphic optoelectronics.</span></description><author>Applied Physics Letters Current Issue</author><pubDate>Mon, 05 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apl/article/128/1/013301/3376081/Bidirectional-optically-modulated-In2O3</guid></item><item><title>[ScienceDirect Publication: Acta Materialia] Dual Engine-driven Strategy for Advanced Copper Alloy Design employing Large Language Models</title><link>https://www.sciencedirect.com/science/article/pii/S1359645425011735?dgcid=rss_sd_all</link><description><p>Publication date: Available online 3 January 2026</p><p><b>Source:</b> Acta Materialia</p><p>Author(s): Fei Tan, Zixuan Zhao, Yanbin Jiang, Wenchao Zhang, Tong Xie, Wei Chen, Muzhi Ma, Yangfan Liu, Yanpeng Ye, Zhu Xiao, Qian Lei, Guofu Xu, Jie Ren, Yuyuan Zhao, Zhou Li</p></description><author>ScienceDirect Publication: Acta Materialia</author><pubDate>Sun, 04 Jan 2026 18:28:43 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1359645425011735</guid></item><item><title>[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Minimally Invasive, Label-Free, Point-of-Care Histopathological Diagnostic Platform of Malignant Tumors of the Female Reproductive System Based on Raman Spectroscopy and Machine Learning</title><link>http://dx.doi.org/10.1021/acs.jpclett.5c03704</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpclett.5c03704/asset/images/medium/jz5c03704_0005.gif" /></p><div><cite>The Journal of Physical Chemistry Letters</cite></div><div>DOI: 10.1021/acs.jpclett.5c03704</div></description><author>The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)</author><pubDate>Sun, 04 Jan 2026 17:52:36 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpclett.5c03704</guid></item><item><title>[ChemRxiv] Atomistic insights into the chemical stability and ionic transport at Li-metal/Li-Argyrodite interfaces</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-5d9nq?rft_dat=source%3Ddrss</link><description>All solid-state batteries (ASSBs) based on solid-state electrolytes (SSEs) are a novel Li-ion battery technology with the potential of enhanced safety, longer lifetimes, and increased energy density when coupled with the Li-metal anode. Li-Argyrodite (Li6PS5Cl) is a promising SSE with high ionic conductivity, produced using cheap and sustainable precursors, and therefore of interest to both academia and industry. Like many other sulfide-based SSEs, it is however unstable against Li-metal. Using ab-initio and machine-learning methods, we simulate three representative Li-metal/Li-Argyrodite interface models to investigate whether the exact surface termination affects the chemical stability and ion transport capability. We present a systematic approach to create low-energy interfaces by screening 28 low Miller-index surface terminations of Li-argyrodite and coupling them with Li-metal. Custom-made machine-learned interatomic potentials trained on ab-initio data enable the simulation of large interface models with over 2000 atoms for 5 ns. We find that all three interfaces decompose into an amorphous solid-electrolyte interphase (SEI) layer, consisting of Li3P, Li2S and LiCl, which then crystallizes into an antifluorite phase Li2S{1-x-y}P{x}Cl{y}; {x,y = 0.14-0.15}. A two orders of magnitude decrease in Li-ion flux shows that the crystalline SEI layer is a sluggish ion conductor, similar to Li2S. While all three interfaces form the same crystalline SEI layer, the exact rates of the decomposition and crystallisation depend on the actual surface composition. These atomic-level insights could potentially be used to control the SEI formation in sulphide-based SSEs and others.</description><author>ChemRxiv</author><pubDate>Sun, 04 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-5d9nq?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Cellulose Coating Altered the Electro-Chemo-Mechanical Evolution of Sodium Thioantimonate Electrolyte in Solid-state Sodium Batteries: An Operando Raman Study</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-bwt22?rft_dat=source%3Ddrss</link><description>All-solid-state batteries (ASSBs) attracted increasing attention due to their improved safety and energy densities; yet electrolyte decomposition and subsequent contact loss limited the interfacial stability of ASSBs. Herein, we report an operando Raman characterization that provides high voltage, time, and spatial resolutions, which enables simultaneous analysis of interfacial decomposition mechanism and morphological evolution. Using Na3SbS4 electrolyte (NSS) and its carboxymethyl-cellulose-encapsulated analogue (NSS-CMC) as exemplars, we precisely contrasted the subtle differences in the two-step reduction mechanism of the two electrolytes. In both systems, Na3SbS3 formed as an intermediate, and Na3Sb binary as one major final product; while the CMC coating altered the kinetics of Na3SbS3 formation and consumption, and extended the formation potential of Na3Sb from 1.35 V (seen in NSS) to 0.50 V (vs. Na/Na+). Oxidation of NSS and NSS-CMC both occur near 2.20 V, although CMC coating altered the crystallinity of the oxidative products. Simultaneously, we captured phenomena that are unique to solid-state electrochemical systems such as particle relocation, morphological change, and reversed reactions. We inferred CMC’s dual role as a voltage barrier and a mechanical buffer in suppressing the electro-chemo-mechanical decomposition of NSS electrolyte. The deep mechanistic insights unravel the exact modification needed for improved interfacial stability.</description><author>ChemRxiv</author><pubDate>Sun, 04 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-bwt22?rft_dat=source%3Ddrss</guid></item><item><title>[ScienceDirect Publication: Artificial Intelligence Chemistry] Accelerated green material and solvent discovery with chemistry- and physics-guided generative AI</title><link>https://www.sciencedirect.com/science/article/pii/S2949747725000235?dgcid=rss_sd_all</link><description><p>Publication date: Available online 2 January 2026</p><p><b>Source:</b> Artificial Intelligence Chemistry</p><p>Author(s): Eslam G. Al-Sakkari, Ahmed Ragab, Marzouk Benali, Olumoye Ajao, Daria C Boffito, Hanane Dagdougui</p></description><author>ScienceDirect Publication: Artificial Intelligence Chemistry</author><pubDate>Sat, 03 Jan 2026 12:38:39 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2949747725000235</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Minutes‐Scale Ultrafast Synthesis of New Oxyhalides Solid Electrolytes with Interfacial Ionic Conduction for All‐Solid‐State Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202516259?af=R</link><description>Angewandte Chemie International Edition, Volume 65, Issue 1, 2 January 2026.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Sat, 03 Jan 2026 06:30:47 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202516259</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Potential‐Gated Polymer Integrates Reversible Ion Transport and Storage for solid‐state Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202513365?af=R</link><description>Advanced Materials, Volume 38, Issue 1, 2 January 2026.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Sat, 03 Jan 2026 06:20:51 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202513365</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Generative Artificial Intelligence Navigated Development of Solvents for Next Generation High‐Performance Magnesium Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202510083?af=R</link><description>Advanced Materials, Volume 38, Issue 1, 2 January 2026.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Sat, 03 Jan 2026 06:20:51 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202510083</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Generality‐Driven Optimization of Enantio‐ and Regioselective Mono‐Reduction of 1,2‐Dicarbonyls by High‐Throughput Experimentation and Machine Learning</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202519425?af=R</link><description>Angewandte Chemie International Edition, Volume 65, Issue 1, 2 January 2026.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Sat, 03 Jan 2026 06:15:46 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202519425</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] An All‐Solid‐State Li–Cu Battery via Cuprous/Lithium‐Ion Halide Solid Electrolyte</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202518966?af=R</link><description>Angewandte Chemie International Edition, Volume 65, Issue 1, 2 January 2026.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Sat, 03 Jan 2026 06:15:46 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202518966</guid></item><item><title>[iScience] AI-Driven Routing and Layered Architectures for Intelligent ICT in Nanosensor Networked Systems</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(26)00001-5?rss=yes</link><description>This review examines the emerging integration of nanosensor networks with modern information and communication technologies to address critical needs in healthcare, environmental monitoring, and smart infrastructure. It evaluates how machine learning and artificial intelligence techniques improve data processing, energy management, real-time communication, and scalable system coordination within nanosensor environments. The analysis compares major learning approaches, including supervised, unsupervised, reinforcement, and deep learning methods, and highlights their effectiveness in data routing, anomaly detection, security, and predictive maintenance.</description><author>iScience</author><pubDate>Sat, 03 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(26)00001-5?rss=yes</guid></item><item><title>[ChemRxiv] The growing role of open source software in molecular modeling</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-6n5lz?rft_dat=source%3Ddrss</link><description>The increasing importance and predictive power of modern molecular modeling, driven by physics- and machine learning-based methods, necessitates a new collaborative architecture to replace the isolated, traditional model of software development. The traditional approach often led to redundant engineering effort, high costs, and opaque systems that limit reproducibility, independent scrutiny, and scientific independence.
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This perspective advocates for permissively licensed open source software as a scientific and economic multiplier by reducing the duplication of effort, enabling scientific validation of modeling tools, and frictionless experimentation with new ideas. Coordinated, multi-project consortia, such as Open Force Field, Open Free Energy, OpenFold, and OpenADMET have formed to collaboratively build shared computational infrastructure and release all methods under permissive licenses. The success of these large-scale efforts requires organizational structures that extend beyond code. The Open Molecular Software Foundation (OMSF), a US nonprofit, serves as a domain-specific institutional home and fiscal sponsor. By providing governance, administrative infrastructure, and dedicated research software engineers, OMSF aligns incentives across academic and industrial stakeholders. This framework enables a synergistic ecosystem where projects interoperate to accelerate innovation, eliminate duplication, and ensure long-term software sustainability, thereby creating durable foundations that elevate the entire molecular modeling community.</description><author>ChemRxiv</author><pubDate>Sat, 03 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-6n5lz?rft_dat=source%3Ddrss</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Tracing Lithophilic Sites: In Situ Nanovisualization of Their Migration and Degradation in All-Solid-State Lithium Batteries</title><link>http://dx.doi.org/10.1021/jacs.5c19144</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c19144/asset/images/medium/ja5c19144_0006.gif" /></p><div><cite>Journal of the American Chemical Society</cite></div><div>DOI: 10.1021/jacs.5c19144</div></description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Fri, 02 Jan 2026 13:23:31 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c19144</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Metal−Organic Framework Ion Conductor‐Based Polymer Solid Electrolytes for Long‐Cycle Lithium Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202511014?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 1, 2 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Fri, 02 Jan 2026 11:53:16 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202511014</guid></item><item><title>[Wiley: Small: Table of Contents] Regulating Interface Chemistry to Construct a Stable Solid Electrolyte Interphase for Long‐Life Zinc Metal Anodes</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202511310?af=R</link><description>Small, Volume 22, Issue 1, 2 January 2026.</description><author>Wiley: Small: Table of Contents</author><pubDate>Fri, 02 Jan 2026 11:26:58 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202511310</guid></item><item><title>[Recent Articles in Phys. Rev. Lett.] Common Sublattice-Pure Van Hove Singularities in the Kagome Superconductors $A{\mathrm{V}}_{3}{\mathrm{Sb}}_{5}$ ($A=\mathrm{K}$, Rb, Cs)</title><link>http://link.aps.org/doi/10.1103/njg9-jpkh</link><description>Author(s): Yujie Lan, Yuhao Lei, Congcong Le, Brenden R. Ortiz, Nicholas C. Plumb, Milan Radovic, Xianxin Wu, Ming Shi, Stephen D. Wilson, and Yong Hu<br /><p>Kagome materials offer a versatile platform for exploring correlated and topological quantum states, where Van Hove singularities (VHSs) play a pivotal role in driving electronic instabilities, exhibiting distinct behaviors depending on electron filling and interaction settings. In the recently disc…</p><br />[Phys. Rev. Lett. 136, 016401] Published Fri Jan 02, 2026</description><author>Recent Articles in Phys. Rev. Lett.</author><pubDate>Fri, 02 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/njg9-jpkh</guid></item><item><title>[Recent Articles in Phys. Rev. Lett.] Half-Quantized Chiral Edge Current in a $C=1/2$ Parity Anomaly State</title><link>http://link.aps.org/doi/10.1103/vxcb-rwbl</link><description>Author(s): Deyi Zhuo, Bomin Zhang, Humian Zhou, Han Tay, Xiaoda Liu, Zhiyuan Xi, Chui-Zhen Chen, and Cui-Zu Chang<br /><p>A single massive Dirac surface band is predicted to exhibit a half-quantized Hall conductance, a hallmark of the $C=1/2$ parity anomaly state in quantum field theory. Experimental signatures of the $C=1/2$ parity anomaly state have been observed in semimagnetic topological insulator (TI) bilayers, y…</p><br />[Phys. Rev. Lett. 136, 016601] Published Fri Jan 02, 2026</description><author>Recent Articles in Phys. Rev. Lett.</author><pubDate>Fri, 02 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/vxcb-rwbl</guid></item><item><title>[Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents] Synergistic Enhancement of Modified‐PVDF Humidity Sensitivity via Chemical Adsorption‐Ionic Conductivity and its Application in Intelligent Powered Air‐Purifying Respirator</title><link>https://onlinelibrary.wiley.com/doi/10.1002/eem2.70119?af=R</link><description>ENERGY &amp;ENVIRONMENTAL MATERIALS, EarlyView.</description><author>Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents</author><pubDate>Fri, 02 Jan 2026 09:41:25 GMT</pubDate><guid isPermaLink="true">10.1002/eem2.70119</guid></item><item><title>[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] In Situ Electric-Field Guided Assembly of Ordered Bilayer Solid Electrolyte Interphase (SEI) Enables High-Current Zinc Metal Anodes</title><link>http://dx.doi.org/10.1021/acs.jpclett.5c03386</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpclett.5c03386/asset/images/medium/jz5c03386_0006.gif" /></p><div><cite>The Journal of Physical Chemistry Letters</cite></div><div>DOI: 10.1021/acs.jpclett.5c03386</div></description><author>The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)</author><pubDate>Fri, 02 Jan 2026 09:07:52 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpclett.5c03386</guid></item><item><title>[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] A neutralizing APOA5 monoclonal antibody reduces amounts of lipoprotein lipase in capillaries and triggers hypertriglyceridemia</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2528664123?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 123, Issue 1, January 2026. <br />SignificanceApolipoprotein AV (APOA5) reduces plasma triglyceride levels by binding to angiopoietin-like protein 3/8 complex (ANGPTL3/8) and suppressing its ability to block lipoprotein lipase, but our understanding of important APOA5 sequences and how ...</description><author>Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents</author><pubDate>Fri, 02 Jan 2026 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2528664123?af=R</guid></item><item><title>[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Antibody responses to a highly conserved peptide in HCV E2 protein correlate with chronicity or spontaneous clearance of HCV infection</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2522340122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 123, Issue 1, January 2026. <br />SignificanceChronicity is a hallmark of hepatitis C virus (HCV) infection, often leading to severe liver diseases such as cirrhosis and hepatocellular carcinoma. Although progression to chronicity or spontaneous clearance is believed to be immune mediated ...</description><author>Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents</author><pubDate>Fri, 02 Jan 2026 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2522340122?af=R</guid></item><item><title>[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Interpretable early warnings using machine learning in an online game-experiment</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2503493122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 123, Issue 1, January 2026. <br />SignificanceCritical transitions can model abrupt regime shifts in socio-ecological systems. While generic early warning signals that apply across systems have been investigated, no universal signal exists. We therefore propose a data-driven and system-...</description><author>Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents</author><pubDate>Fri, 02 Jan 2026 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2503493122?af=R</guid></item><item><title>[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Machine learning reveals hidden dimensions of functional similarity in proteins</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2524802122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 123, Issue 1, January 2026. <br /></description><author>Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents</author><pubDate>Fri, 02 Jan 2026 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2524802122?af=R</guid></item><item><title>[Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents] Correlating the Interfacial Chemistries With Ion Conduction and Lithium Deactivation in Hybrid Solid Electrolytes</title><link>https://onlinelibrary.wiley.com/doi/10.1002/eem2.70196?af=R</link><description>ENERGY &amp;ENVIRONMENTAL MATERIALS, EarlyView.</description><author>Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents</author><pubDate>Fri, 02 Jan 2026 06:03:30 GMT</pubDate><guid isPermaLink="true">10.1002/eem2.70196</guid></item><item><title>[ChemRxiv] Complete Computational Exploration of Eight-Carbon Hydrocarbon Chemical Space</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-qjr5r?rft_dat=source%3Ddrss</link><description>Hydrocarbons are the most fundamental class of chemical species, but even the chemical space of those with eight carbon atoms or less has not been explored exhaustively. Here we report a full enumeration and computational exploration of this space. Density functional theory-based geometry optimisation and energy calculations have identified all stable molecules within this space, forming a new database called CHX8. A universal strain value has been proposed and assigned to each of these molecules, acting as a proxy for synthesisability and providing a clear guideline of how synthetically plausible these molecules could be. This paper explores the limits of chemical space with CHX8, with a focus on trans-fused, unsaturated and anti-Bredt ring systems. We show that, contrary to prevailing wisdom, most of these unconventional structures should be synthetically accessible, with relative strain energies less than that of cubane. It is expected that this dataset will inspire the synthesis of many new molecules with applications in various areas of chemistry, biology and materials science. The resulting dataset also provides a valuable resource for the development of general and robust machine learning models.</description><author>ChemRxiv</author><pubDate>Fri, 02 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-qjr5r?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] A round-robin exercise for the precise prediction of aqueous solubility of organic chemicals using chemometric, machine learning, and stacking ensemble of deep learning models</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-6xb6k?rft_dat=source%3Ddrss</link><description>Aqueous solubility is an important property for assessing the druggability and ecotoxicological effects of molecules. Successful drug candidates should have optimal aqueous solubility to improve bioavailability to target tissues. To effectively screen molecules in a short period of time, reliable predictive models are highly useful. In the present study, we conducted a round-robin exercise using a large, curated dataset of over 6000 compounds to predict aqueous solubility quantitatively. The six participating groups used an array of Machine Learning and Deep Learning algorithms to develop models with strong robustness and external predictive performance. All the models underwent rigorous Leave-One-Out and 10-fold cross-validation. The diversity of training sets and descriptor types used by different groups paved the way for exploring the mechanistic basis for the efficient identification of contributing features. The best-performing model was selected using the statistical Sum of Ranking Differences (SRD) approach, considering the performances on training, cross-validation, and test, as well as the performance difference between the training and test sets. Additionally, a curated, true external set was screened by the six different models. Here, the best-performing model was selected using a consensus ranking strategy based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R_Ext^2. In both approaches, i.e., the inherent model performance in terms of training, test, and cross-validation statistics, and the ability of the model to efficiently predict true external data, the Stacking Ensemble of Deep q-RASPR model emerged as the winner. This model showed comparable predictive performance to the previously reported model, which apparently lacked a proper data curation workflow and contained a significant number of duplicates and mixtures in its dataset, which can inflate model statistics. The insights from the different feature contributions from the different groups identified the useful structural and physicochemical aspects, which can help synthetic chemists to optimize molecules.</description><author>ChemRxiv</author><pubDate>Fri, 02 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-6xb6k?rft_dat=source%3Ddrss</guid></item><item><title>[Joule] Seeing the unseen: Real-time tracking of battery cycling-to-failure via surface strain</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00453-2?rss=yes</link><description>This study proposes a strain-based approach to address passive failures in lithium-ion batteries, which present spontaneous safety risks often indistinguishable from routine degradation using conventional diagnostics. By establishing a strain-failure correlation, we introduce a slope-based threshold and a failure-proximity index to characterize degradation-to-failure transitions. Incorporating strain-informed machine learning, it effectively detects early failure onset and estimates proximity. This scalable approach is suitable for real-time, onboard monitoring, supporting safer and more reliable battery operation.</description><author>Joule</author><pubDate>Fri, 02 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00453-2?rss=yes</guid></item><item><title>[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Understanding and Mitigating Lithium Metal Anode Failure in All-Solid-State Batteries with Inorganic Solid Electrolytes</title><link>http://dx.doi.org/10.1021/acsenergylett.5c03333</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsenergylett.5c03333/asset/images/medium/nz5c03333_0006.gif" /></p><div><cite>ACS Energy Letters</cite></div><div>DOI: 10.1021/acsenergylett.5c03333</div></description><author>ACS Energy Letters: Latest Articles (ACS Publications)</author><pubDate>Thu, 01 Jan 2026 18:39:05 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsenergylett.5c03333</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Accelerating the search for superconductors using machine learning</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007967?dgcid=rss_sd_all</link><description><p>Publication date: February 2026</p><p><b>Source:</b> Computational Materials Science, Volume 263</p><p>Author(s): Suhas Adiga, Umesh V. Waghmare</p></description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Thu, 01 Jan 2026 18:29:38 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007967</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] Machine learning–assisted discovery of chromium bis(2-pyridyl)amine catalysts for ethylene tri-/tetramerization</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725006797?dgcid=rss_sd_all</link><description><p>Publication date: February 2026</p><p><b>Source:</b> Journal of Catalysis, Volume 454</p><p>Author(s): Youcai Zhu, Yue Mu, Xiaoke Shi, Shu Yang, Li Sun, Zhen Liu</p></description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Thu, 01 Jan 2026 12:22:12 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725006797</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] The influence of the organic residue and the solvent in the Schlenk equilibrium for Grignard reagents in THF. A molecular dynamics study with machine learning potentials</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725006852?dgcid=rss_sd_all</link><description><p>Publication date: February 2026</p><p><b>Source:</b> Journal of Catalysis, Volume 454</p><p>Author(s): Marco Bortoli, Sigbjørn Løland Bore, Odile Eisenstein, Michele Cascella</p></description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Thu, 01 Jan 2026 12:22:12 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725006852</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] Protonation dynamics of confined ethanol–water mixtures in H-ZSM-5 from machine learning-driven metadynamics</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725007249?dgcid=rss_sd_all</link><description><p>Publication date: February 2026</p><p><b>Source:</b> Journal of Catalysis, Volume 454</p><p>Author(s): Princy Jarngal, Benjamin A. Jackson, Simuck F. Yuk, Difan Zhang, Mal-Soon Lee, Maria Cristina Menziani, Vassiliki-Alexandra Glezakou, Roger Rousseau, GiovanniMaria Piccini</p></description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Thu, 01 Jan 2026 12:22:12 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725007249</guid></item><item><title>[ScienceDirect Publication: Acta Materialia] Inverse Design of High-Performance Glasses Through an Encoder-Decoder Machine Learning Approach Toward Materials Discovery: Application to Oxynitride Glasses</title><link>https://www.sciencedirect.com/science/article/pii/S1359645425011693?dgcid=rss_sd_all</link><description><p>Publication date: Available online 29 December 2025</p><p><b>Source:</b> Acta Materialia</p><p>Author(s): Alexis Duval, Eric Robin, Patrick Houizot, Tanguy Rouxel</p></description><author>ScienceDirect Publication: Acta Materialia</author><pubDate>Thu, 01 Jan 2026 12:22:12 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1359645425011693</guid></item><item><title>[ScienceDirect Publication: Acta Materialia] DuctGPT: A Generative Transformer for Forward Screening of Ductile Refractory Multi-Principal Element Alloys</title><link>https://www.sciencedirect.com/science/article/pii/S135964542501050X?dgcid=rss_sd_all</link><description><p>Publication date: 1 January 2026</p><p><b>Source:</b> Acta Materialia, Volume 304</p><p>Author(s): Sai Pranav Reddy Guduru, Mkpe O. Kekung, Ryan T. Ott, Sougata Roy, Prashant Singh</p></description><author>ScienceDirect Publication: Acta Materialia</author><pubDate>Thu, 01 Jan 2026 12:22:12 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S135964542501050X</guid></item><item><title>[ScienceDirect Publication: Acta Materialia] On-the-fly machine learning of interatomic potentials for elastic property modeling in Al–Mg–Zr solid solutions</title><link>https://www.sciencedirect.com/science/article/pii/S1359645425011310?dgcid=rss_sd_all</link><description><p>Publication date: 15 February 2026</p><p><b>Source:</b> Acta Materialia, Volume 305</p><p>Author(s): Lukas Volkmer, Leonardo Medrano Sandonas, Philip Grimm, Julia Kristin Hufenbach, Gianaurelio Cuniberti</p></description><author>ScienceDirect Publication: Acta Materialia</author><pubDate>Thu, 01 Jan 2026 12:22:12 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1359645425011310</guid></item><item><title>[ScienceDirect Publication: Journal of Materiomics] Data-augmented machine learning models for oxynitride glasses <em>via</em> Wasserstein generative adversarial network with gradient penalty and content constraint</title><link>https://www.sciencedirect.com/science/article/pii/S2352847825001017?dgcid=rss_sd_all</link><description><p>Publication date: Available online 8 August 2025</p><p><b>Source:</b> Journal of Materiomics</p><p>Author(s): Jing Tian, Yuan Li, Min Guan, Jijie Zheng, Jingyuan Chu, Yong Liu, Gaorong Han</p></description><author>ScienceDirect Publication: Journal of Materiomics</author><pubDate>Thu, 01 Jan 2026 12:21:56 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352847825001017</guid></item><item><title>[ScienceDirect Publication: Journal of Materiomics] Machine learning assisted <em>τ</em><sub>f</sub> value prediction of ABO<sub>3</sub>-type microwave dielectric ceramics</title><link>https://www.sciencedirect.com/science/article/pii/S2352847825001078?dgcid=rss_sd_all</link><description><p>Publication date: Available online 8 August 2025</p><p><b>Source:</b> Journal of Materiomics</p><p>Author(s): Mingyue Yang, Liangyu Mo, Jincheng Qin, Faqiang Zhang, Mingsheng Ma, Yongxiang Li, Zhifu Liu</p></description><author>ScienceDirect Publication: Journal of Materiomics</author><pubDate>Thu, 01 Jan 2026 12:21:56 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352847825001078</guid></item><item><title>[ScienceDirect Publication: Journal of Materiomics] Domain knowledge-assisted materials data anomaly detection towards constructing high-performance machine learning models</title><link>https://www.sciencedirect.com/science/article/pii/S2352847825000565?dgcid=rss_sd_all</link><description><p>Publication date: November 2025</p><p><b>Source:</b> Journal of Materiomics, Volume 11, Issue 6</p><p>Author(s): Yue Liu, Shuchang Ma, Zhengwei Yang, Duo Wu, Yali Zhao, Maxim Avdeev, Siqi Shi</p></description><author>ScienceDirect Publication: Journal of Materiomics</author><pubDate>Thu, 01 Jan 2026 12:21:56 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352847825000565</guid></item><item><title>[ScienceDirect Publication: Journal of Materiomics] PTCDA/CuS cathode enabling stable sulfide-based all-solid-state batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352847825000814?dgcid=rss_sd_all</link><description><p>Publication date: January 2026</p><p><b>Source:</b> Journal of Materiomics, Volume 12, Issue 1</p><p>Author(s): Zhixing Wan, Shuo Wang, Yahao Mu, Ruihua Zhou, Hang Liu, Tingwu Jin, Di Wu, Jianlong Xia, Ce-Wen Nan</p></description><author>ScienceDirect Publication: Journal of Materiomics</author><pubDate>Thu, 01 Jan 2026 12:21:56 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352847825000814</guid></item><item><title>[ScienceDirect Publication: Current Opinion in Solid State and Materials Science] Solid electrolyte-driven suppression of H2–H3 phase transition in Ni-rich cathodes for stable high-voltage cycling</title><link>https://www.sciencedirect.com/science/article/pii/S1359028625000324?dgcid=rss_sd_all</link><description><p>Publication date: December 2025</p><p><b>Source:</b> Current Opinion in Solid State and Materials Science, Volume 39</p><p>Author(s): Hao Chen, Hsiao-Hsuan Wu, Chia-Chen Li</p></description><author>ScienceDirect Publication: Current Opinion in Solid State and Materials Science</author><pubDate>Thu, 01 Jan 2026 12:21:56 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1359028625000324</guid></item><item><title>[ScienceDirect Publication: Current Opinion in Solid State and Materials Science] State-of-the-art review of additive friction stir deposition: microstructural evolution, machine learning applications, and future directions</title><link>https://www.sciencedirect.com/science/article/pii/S1359028625000300?dgcid=rss_sd_all</link><description><p>Publication date: January 2026</p><p><b>Source:</b> Current Opinion in Solid State and Materials Science, Volume 40</p><p>Author(s): Ashish Kumar, Lei Shi, Virendra Pratap Singh, Sudipta Mohapatra, Long Li, Chuansong Wu, Sergey Mironov, Amitava De</p></description><author>ScienceDirect Publication: Current Opinion in Solid State and Materials Science</author><pubDate>Thu, 01 Jan 2026 12:21:56 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1359028625000300</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Machine learning-aided prediction and optimization of specific capacitance in functionalized GO-based Mn/Fe co-doped Bi<sub>2</sub>O<sub>3</sub> nanocomposites</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25048285?dgcid=rss_sd_all</link><description><p>Publication date: 10 February 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 146</p><p>Author(s): Vijay A. Mane, Kartik M. Chavan, Sushant S. Munde, Dnyaneshwar V. Dake, Nita D. Raskar, Ramprasad B. Sonpir, Pravin V. Dhole, Ketan P. Gattu, Sandeep B. Somvanshi, Pavan R. Kayande, Jagruti S. Pawar, Babasaheb N. Dole</p></description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Thu, 01 Jan 2026 12:21:56 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25048285</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Time-resolved impedance spectroscopy analysis of stable lithium iron phosphate cathode with enhanced electronic/ionic conductivity and ion diffusion characteristics</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25049035?dgcid=rss_sd_all</link><description><p>Publication date: 10 February 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 146</p><p>Author(s): Jiguo Tu, Yan Li, Libo Chen, Dongbai Sun</p></description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Thu, 01 Jan 2026 12:21:56 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25049035</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Hollow nanofiber ion conductor protective layer on Zn metal anode for long-term stable zinc battery</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25049953?dgcid=rss_sd_all</link><description><p>Publication date: 10 February 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 146</p><p>Author(s): Mengfei Sun, Zumin Zhang, Yang Su, Wensheng Yu, Xiangting Dong, Dongtao Liu, Xinlu Wang, Gaopeng Li, Jinxian Wang</p></description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Thu, 01 Jan 2026 12:21:56 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25049953</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Alkaline-compatible polyaniline/graphene negative electrode for ultrahigh-energy all-solid-state asymmetric supercapacitors</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25048844?dgcid=rss_sd_all</link><description><p>Publication date: 10 February 2026</p><p><b>Source:</b> Journal of Energy Storage, Volume 146</p><p>Author(s): Aizhen Xu, Li Yin, Shaoqing Zhang, Zhiyi Zhao, Wenna Lv, Yuanyu Zhu, Yujun Qin</p></description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Thu, 01 Jan 2026 12:21:56 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25048844</guid></item><item><title>[ScienceDirect Publication: Solid State Ionics] Crossover from insulating into solid electrolyte behavior in bulk CaSO<sub>4</sub>⋅0.5H<sub>2</sub>O material due to ion exchange processes induced by high-temperature treatment with orthophosphoric acid</title><link>https://www.sciencedirect.com/science/article/pii/S0167273825003170?dgcid=rss_sd_all</link><description><p>Publication date: 1 February 2026</p><p><b>Source:</b> Solid State Ionics, Volume 434</p><p>Author(s): Ivan Nikulin, Tatiana Nikulicheva, Vitaly Vyazmin, Oleg Ivanov, Nikita Anosov, Olga Telpova</p></description><author>ScienceDirect Publication: Solid State Ionics</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0167273825003170</guid></item><item><title>[ScienceDirect Publication: Solid State Ionics] First-principles elucidation of the effects of Al-doping on Li-ion diffusion in LiCoO<sub>2</sub></title><link>https://www.sciencedirect.com/science/article/pii/S0167273825003224?dgcid=rss_sd_all</link><description><p>Publication date: 1 February 2026</p><p><b>Source:</b> Solid State Ionics, Volume 434</p><p>Author(s): Jordan A. Barr, Scott P. Beckman, Brandon C. Wood, Liwen F. Wan</p></description><author>ScienceDirect Publication: Solid State Ionics</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0167273825003224</guid></item><item><title>[ScienceDirect Publication: Solid State Ionics] Anion engineering enhances the electrochemical performance of Li<sub>7</sub>P<sub>3</sub>S<sub>11</sub> solid electrolyte</title><link>https://www.sciencedirect.com/science/article/pii/S0167273825003236?dgcid=rss_sd_all</link><description><p>Publication date: 15 February 2026</p><p><b>Source:</b> Solid State Ionics, Volume 435</p><p>Author(s): D. Narsimulu, Ramkumar Balasubramaniam, Kwang-Sun Ryu</p></description><author>ScienceDirect Publication: Solid State Ionics</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0167273825003236</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Machine learning assisted local descriptors predicate oxygen reduction activity of transition metal@Ti<sub>1−<em>x</em></sub>Zn<sub><em>x</em></sub> alloys</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625006883?dgcid=rss_sd_all</link><description><p>Publication date: 30 January 2026</p><p><b>Source:</b> Computational Materials Science, Volume 262</p><p>Author(s): Tian-Zhe Wan, Shou-Heng Guo, Guang-Qiang Yu, Jun-Zhe Li, Ya-Nan Zhu, Xi-Bo Li</p></description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625006883</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] PyVUMAT: A package to develop and deploy machine learning material models in finite element analysis simulations</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007207?dgcid=rss_sd_all</link><description><p>Publication date: 30 January 2026</p><p><b>Source:</b> Computational Materials Science, Volume 262</p><p>Author(s): Joshua C. Crone</p></description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007207</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Predicting hydrogen storage capacity of metal hydrides using novel imputation techniques and tree-based machine learning models</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007335?dgcid=rss_sd_all</link><description><p>Publication date: 30 January 2026</p><p><b>Source:</b> Computational Materials Science, Volume 262</p><p>Author(s): Zaid Allal, Hassan N. Noura, Flavien Vernier, Ola Salman, Khaled Chahine</p></description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007335</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Accelerating magnetic materials discovery using interaction matrix-based machine learning descriptors</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007384?dgcid=rss_sd_all</link><description><p>Publication date: 30 January 2026</p><p><b>Source:</b> Computational Materials Science, Volume 262</p><p>Author(s): Apoorv Verma, Junaid Jami, Amrita Bhattacharya</p></description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007384</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Effects of training machine-learning potentials for radiation damage simulations using different pseudopotentials</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007414?dgcid=rss_sd_all</link><description><p>Publication date: 30 January 2026</p><p><b>Source:</b> Computational Materials Science, Volume 262</p><p>Author(s): A. Fellman, J. Byggmästar, F. Granberg, F. Djurabekova, K. Nordlund</p></description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007414</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Structure-driven prediction and mechanism insights into piezoelectric performance of potassium sodium niobate via interpretable machine learning</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007530?dgcid=rss_sd_all</link><description><p>Publication date: February 2026</p><p><b>Source:</b> Computational Materials Science, Volume 263</p><p>Author(s): Hui Li, WenKe Lu, Chunlei Li, Jinyi Liu, Zihui Feng, Jiaming Liu, Lan Yang</p></description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007530</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Band structure modulation of germanium under arbitrary strain directions: A combined approach of strain matrix theory, first-principles calculation and machine learning</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007694?dgcid=rss_sd_all</link><description><p>Publication date: February 2026</p><p><b>Source:</b> Computational Materials Science, Volume 263</p><p>Author(s): Hai Wang, Wenqi Huang, Mengjiang Jia</p></description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007694</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Machine learning-driven prognostication of multifunctional properties of (K,Na)NbO<sub>3</sub>-based lead-free ceramics for optimized materials design</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007803?dgcid=rss_sd_all</link><description><p>Publication date: February 2026</p><p><b>Source:</b> Computational Materials Science, Volume 263</p><p>Author(s): Manisha Kumari, Alok Shukla</p></description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007803</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Machine learning for domain transfer between simulated and experimental 2D X-ray diffraction patterns using generative adversarial networks</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007724?dgcid=rss_sd_all</link><description><p>Publication date: February 2026</p><p><b>Source:</b> Computational Materials Science, Volume 263</p><p>Author(s): Samantha J. Brozak, David Montes de Oca Zapiain, Brendan Donohoe, Tommy Ao, Nathan P. Brown, Marcus D. Knudson, Carianne Martinez, J. Matthew D. Lane</p></description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007724</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Machine learning interatomic potentials for monolayer hexagonal boron nitride: Thermal transport and defect effects via deep potential molecular dynamics</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007955?dgcid=rss_sd_all</link><description><p>Publication date: February 2026</p><p><b>Source:</b> Computational Materials Science, Volume 263</p><p>Author(s): Guanghao Zhang, Zicheng Wang, Caihua Shi, Ye Han</p></description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007955</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] An enhanced machine learning and computational screening framework for synthesizable single-phase high-entropy spinel oxides</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625008110?dgcid=rss_sd_all</link><description><p>Publication date: February 2026</p><p><b>Source:</b> Computational Materials Science, Volume 263</p><p>Author(s): Mahalaxmi Chandramohan, Hridhya Vinod, Meenal Deo</p></description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625008110</guid></item><item><title>[ScienceDirect Publication: Artificial Intelligence Chemistry] Machine Learning (ML)-driven quantitative structure-pharmacokinetic relationship (QSPKR) 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electrolyte: Phosphorus-free sulfide glass of LiSbGe<sub>(4-x)/4</sub>S<sub>4-x</sub>Cl<sub>x</sub></title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009620?dgcid=rss_sd_all</link><description><p>Publication date: January 2026</p><p><b>Source:</b> Nano Energy, Volume 147</p><p>Author(s): Yuna Kim, Woojung Lee, Jiyun Han, Yeong Mu Seo, Dokyung Kim, Young Joo Lee, Byung Gon Kim, Munseok S. Chae, Sung Jin Kim, In Young Kim</p></description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Thu, 01 Jan 2026 12:21:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009620</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Advancing high-safety and low-cost all-solid-state batteries with polyanion cathodes: Challenges and recent progress</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009978?dgcid=rss_sd_all</link><description><p>Publication date: February 2026</p><p><b>Source:</b> Nano Energy, Volume 148</p><p>Author(s): Ali Yaghtin, Atiyeh Nekahi, Jeremy I.G. Dawkins, Xia Li, Karim Zaghib, Sixu Deng</p></description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Thu, 01 Jan 2026 12:21:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009978</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Enabling high-accuracy lithium-ion battery status prediction via machine learning-integrated perovskite sensors</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009851?dgcid=rss_sd_all</link><description><p>Publication date: February 2026</p><p><b>Source:</b> Nano Energy, Volume 148</p><p>Author(s): Jiaxin Sun, Xianjie Xu, Zhefu Mu, Zijun Huang, Guo Chen, Xinkai Qi, Hongwei Liu, Lei Zhu, Xiuquan Gu, Xinjian He, Sheng Huang</p></description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Thu, 01 Jan 2026 12:21:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009851</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Phase boundary engineering in micro-sized Sn/SnSb anode enabling superior sodium storage: Synergistic stress relief and fast ion transport</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525010249?dgcid=rss_sd_all</link><description><p>Publication date: February 2026</p><p><b>Source:</b> Nano Energy, Volume 148</p><p>Author(s): Yuhong Liang, Chengcheng He, Zhengyang Zhao, Longqing Zhang, Rui Sun, Qian Ning, Huibing He, Yang Ren, Jing Xu, Qiang Zhang, Yajie Song, Xucai Yin</p></description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Thu, 01 Jan 2026 12:21:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525010249</guid></item><item><title>[ScienceDirect Publication: Matter] Machine learning-driven ligand engineering decodes and controls structural distortions in 2D perovskites</title><link>https://www.sciencedirect.com/science/article/pii/S2590238525005259?dgcid=rss_sd_all</link><description><p>Publication date: Available online 10 October 2025</p><p><b>Source:</b> Matter</p><p>Author(s): Zhipeng Miao, Yahui Han, Qi Pan, Yipei Wang, Haibin Wang, Yunhang Xie, Jie Yu, Yapeng Shi, Rui Zhang, Yanlin Song, Pengwei Li</p></description><author>ScienceDirect Publication: Matter</author><pubDate>Thu, 01 Jan 2026 12:21:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2590238525005259</guid></item><item><title>[ScienceDirect Publication: Matter] SpectroGen: A physically informed generative artificial intelligence for accelerated cross-modality spectroscopic materials characterization</title><link>https://www.sciencedirect.com/science/article/pii/S2590238525004771?dgcid=rss_sd_all</link><description><p>Publication date: Available online 14 October 2025</p><p><b>Source:</b> Matter</p><p>Author(s): Yanmin Zhu, Loza F. Tadesse</p></description><author>ScienceDirect Publication: Matter</author><pubDate>Thu, 01 Jan 2026 12:21:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2590238525004771</guid></item><item><title>[ScienceDirect Publication: Matter] Precisely deciphering solid electrolyte interphase</title><link>https://www.sciencedirect.com/science/article/pii/S2590238525004114?dgcid=rss_sd_all</link><description><p>Publication date: 5 November 2025</p><p><b>Source:</b> Matter, Volume 8, Issue 11</p><p>Author(s): Enhui Wang, Shaohua Ge, Wenbin Li, Beibei Fu, Fangyi Zhou, Weihua Chen</p></description><author>ScienceDirect Publication: Matter</author><pubDate>Thu, 01 Jan 2026 12:21:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2590238525004114</guid></item><item><title>[ScienceDirect Publication: Matter] Rapid scalable plasma processing of thin-film Li–La–Zr–O solid-state electrolytes</title><link>https://www.sciencedirect.com/science/article/pii/S2590238525005119?dgcid=rss_sd_all</link><description><p>Publication date: 5 November 2025</p><p><b>Source:</b> Matter, Volume 8, Issue 11</p><p>Author(s): Gabriel Badillo Crane, Thomas W. Colburn, Sarah E. Holmes, Justus Just, Yi Cui, Reinhold H. Dauskardt</p></description><author>ScienceDirect Publication: Matter</author><pubDate>Thu, 01 Jan 2026 12:21:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2590238525005119</guid></item><item><title>[ScienceDirect Publication: Joule] Impact of metallic interlayers at the lithium-Li<sub>6</sub>PS<sub>5</sub>Cl solid electrolyte interface</title><link>https://www.sciencedirect.com/science/article/pii/S2542435125003563?dgcid=rss_sd_all</link><description><p>Publication date: 19 November 2025</p><p><b>Source:</b> Joule, Volume 9, Issue 11</p><p>Author(s): Souhardh Kotakadi, Jack Aspinall, Matthew Burton, Yi Liang, Yuichi Aihara, Mauro Pasta</p></description><author>ScienceDirect Publication: Joule</author><pubDate>Thu, 01 Jan 2026 12:21:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125003563</guid></item><item><title>[ScienceDirect Publication: Joule] Li–Si compound anodes enabling high-performance all-solid-state Li-ion batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2542435125003769?dgcid=rss_sd_all</link><description><p>Publication date: 17 December 2025</p><p><b>Source:</b> Joule, Volume 9, Issue 12</p><p>Author(s): Do-Hyeon Kim, Young-Han Lee, Jeong-Myeong Yoon, Pugalenthiyar Thondaiman, Byung Chul Kim, In-Chul Choi, Jeong-Hee Choi, Ki-Joon Jeon, Cheol-Min Park</p></description><author>ScienceDirect Publication: Joule</author><pubDate>Thu, 01 Jan 2026 12:21:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125003769</guid></item><item><title>[ScienceDirect Publication: Joule] Boosting ionic conductivity of fluoride electrolytes by polyanion coordination chemistry enabling 5 V-Class all-solid-state batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2542435125004143?dgcid=rss_sd_all</link><description><p>Publication date: Available online 19 December 2025</p><p><b>Source:</b> Joule</p><p>Author(s): Huaimin Jin, Xingyu Wang, Simeng Zhang, Xiangzhen Zhu, Chong Liu, Junyi Yue, Jie Qu, Bei Wu, Xu Han, Yueyue Wang, Yang Xu, Han Wu, Liyu Zhou, Mingying Zhang, Hao Lai, Shuo Wang, Jiangwen Liang, Xueliang Sun, Xiaona Li</p></description><author>ScienceDirect Publication: Joule</author><pubDate>Thu, 01 Jan 2026 12:21:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125004143</guid></item><item><title>[ScienceDirect Publication: Joule] Machine learning-driven interface material design for high-performance perovskite solar cells with scalability and band-gap universality</title><link>https://www.sciencedirect.com/science/article/pii/S2542435125004453?dgcid=rss_sd_all</link><description><p>Publication date: Available online 23 December 2025</p><p><b>Source:</b> Joule</p><p>Author(s): Chenyang Zhang, Yuteng Jia, Bingqian Zhang, Qiangqiang Zhao, Ruida Xu, Shuping Pang, Han Wang, Stefaan De Wolf, Kai Wang</p></description><author>ScienceDirect Publication: Joule</author><pubDate>Thu, 01 Jan 2026 12:21:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125004453</guid></item><item><title>[cond-mat updates on arXiv.org] Atomic-scale visualization of d-wave altermagnetism</title><link>https://arxiv.org/abs/2512.24114</link><description>arXiv:2512.24114v1 Announce Type: new
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Abstract: Altermagnetism is a newly discovered fundamental form of magnetic order, distinct from conventional ferromagnetism and antiferromagnetism. It uniquely exhibits no net magnetization while simultaneously breaking time-reversal symmetry, a combination previously thought to be mutually exclusive. Although its existence and signatures in momentum space have been established, the direct real-space visualization of its defining rotational symmetry breaking has remained a missing cornerstone. Here, using scanning tunnelling microscopy, we present atomic-scale imaging of electronic states in the candidate material CsV2Se2O. We directly visualize the hallmark symmetry breaking in the form of unidirectional electronic patterns tied to magnetic domain walls and spin defects, as well as elliptical charging rings surrounding those defects. These observed electronic states are all linked to the underlying alternating spin texture. Our work provides the foundational real-space evidence for altermagnetism, moving the field from theoretical and momentum-space probes to direct visual confirmation; thereby opening a path to explore how this unconventional magnetic order couples to and controls other quantum electronic states.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.24114v1</guid></item><item><title>[cond-mat updates on arXiv.org] Insights Into Radiation Damage in YBa$_2$Cu$_3$O$_{7-\delta}$ From Machine-Learned Interatomic Potentials</title><link>https://arxiv.org/abs/2512.24430</link><description>arXiv:2512.24430v1 Announce Type: new
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Abstract: Accurate prediction of radiation damage in YBa$_2$Cu$3$O${7-\delta}$ (YBCO) is essential for assessing the performance of high-temperature superconducting (HTS) tapes in compact fusion reactors. Existing empirical interatomic potentials have been used to model radiation damage in stoichiometric YBCO, but fail to describe oxygen-deficient compositions, which are ubiquitous in industrial Rare-Earth Barium Copper Oxide conductors and strongly influence superconducting properties. In this work, we demonstrate that modern machine-learned interatomic potentials enable predictive modelling of radiation damage in YBCO across a wide range of oxygen stoichiometries, with higher fidelity than previous empirical models. We employ two recently developed approaches: an Atomic Cluster Expansion (ACE) potential and a tabulated Gaussian Approximation Potential (tabGAP). Both models accurately reproduce Density Functional Theory (DFT) energies, forces, and threshold displacement energy distributions, providing a reliable description of atomic-scale collision processes. Molecular dynamics simulations of 5 keV cascades predict enhanced peak defect production and recombination relative to a widely used empirical potential, indicating different cascade evolution. By explicitly varying oxygen deficiency, we show that total defect production depends only weakly on stoichiometry, offering insight into the robustness of radiation damage processes in oxygen-deficient YBCO. Finally, fusion-relevant 300 keV cascade simulations reveal amorphous regions with dimensions comparable to the superconducting coherence length, consistent with electron microscopy observations of neutron-irradiated HTS tapes. These results establish machine-learned interatomic potentials as efficient and predictive tools for investigating radiation damage in YBCO across relevant compositions and irradiation conditions.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.24430v1</guid></item><item><title>[cond-mat updates on arXiv.org] Upscaling from ab initio atomistic simulations to electrode scale: The case of manganese hexacyanoferrate, a cathode material for Na-ion batteries</title><link>https://arxiv.org/abs/2512.24816</link><description>arXiv:2512.24816v1 Announce Type: new
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Abstract: We present a generalizable scale-bridging computational framework that enables predictive modeling of insertion-type electrode materials from atomistic to device scales. Applied to sodium manganese hexacyanoferrate, a promising cathode material for grid-scale sodium-ion batteries, our methodology employs an active-learning strategy to train a Moment Tensor Potential through iterative hybrid grand-canonical Monte Carlo--molecular dynamics sampling, robustly capturing configuration spaces at all sodiation levels. The resulting machine learning interatomic potential accurately reproduces experimental properties including volume expansion, operating voltage, and sodium concentration-dependent structural transformations, while revealing a four-order-of-magnitude difference in sodium diffusivity between the rhombohedral (sodium-rich) and tetragonal (sodium-poor) phases at 300 K. We directly compute all critical parameters -- temperature- and concentration-dependent diffusivities, interfacial and strain energies, and complete free-energy landscapes -- to feed them into pseudo-2D phase-field simulations that predict phase-boundary propagation and rate-dependent performances across electrode length scales. This multiscale workflow establishes a blueprint for rational computational design of next-generation insertion-type materials, such as battery electrode materials, demonstrating how atomistic insights can be systematically translated into continuum-scale predictions.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.24816v1</guid></item><item><title>[cond-mat updates on arXiv.org] SSCHA-based evolutionary crystal structure prediction at finite temperatures with account for quantum nuclear motion</title><link>https://arxiv.org/abs/2512.24849</link><description>arXiv:2512.24849v1 Announce Type: new
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Abstract: Accurate crystal structure prediction (CSP) at finite temperatures with quantum anharmonic effects remains challenging but very prominent in systems with lightweight atoms such as superconducting hydrides. In this work, we integrate machine-learned interatomic potentials (MLIPs) with the stochastic self-consistent harmonic approximation (SSCHA) to enable evolutionary CSP on the quantum anharmonic free-energy landscape. Using LaH$_{10}$ at 150 GPa and 300 K as a test case, we compare two approaches for SSCHA-based CSP: using light-weight active-learning MLIPs (AL-MLIPs) trained on-the-fly from scratch, and foundation models or universal MLIPs (uMLIPs) from the Matbench project. We demonstrate that AL-MLIPs allow to correctly predict the experimentally known cubic Fm$\bar{3}$m phase as the most stable polymorph at 150 GPa but require corrections within the thermodynamic perturbation theory to get consistent results. The uMLIP Mattersim-5m allow to conduct SSCHA-based CSP without requiring per-structure training and even get correct structure ranking near the global minimum, though fine-tuning may be needed for higher accuracy. Our results show that including quantum anharmonicity simplifies the free-energy landscape and is essential for correct stability rankings, that is especially important for high-temperature phases that could be missed in classical 0 K CSP. The proposed approach extends the reach of CSP to systems where quantum nuclear motion and anharmonicity dominate.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.24849v1</guid></item><item><title>[cond-mat updates on arXiv.org] Large language models and the entropy of English</title><link>https://arxiv.org/abs/2512.24969</link><description>arXiv:2512.24969v1 Announce Type: new
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Abstract: We use large language models (LLMs) to uncover long-ranged structure in English texts from a variety of sources. The conditional entropy or code length in many cases continues to decrease with context length at least to $N\sim 10^4$ characters, implying that there are direct dependencies or interactions across these distances. A corollary is that there are small but significant correlations between characters at these separations, as we show from the data independent of models. The distribution of code lengths reveals an emergent certainty about an increasing fraction of characters at large $N$. Over the course of model training, we observe different dynamics at long and short context lengths, suggesting that long-ranged structure is learned only gradually. Our results constrain efforts to build statistical physics models of LLMs or language itself.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.24969v1</guid></item><item><title>[cond-mat updates on arXiv.org] Emergence of 3D Superconformal Ising Criticality on the Fuzzy Sphere</title><link>https://arxiv.org/abs/2512.25054</link><description>arXiv:2512.25054v1 Announce Type: new
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Abstract: Supersymmetric conformal field theories (SCFTs) form a unique subset of quantum field theories which provide powerful insights into strongly coupled critical phenomena. Here, we present a microscopic and non-perturbative realization of the three-dimensional $\mathcal{N}=1$ superconformal Ising critical point, based on a Yukawa-type coupling between a 3D Ising CFT and a gauged Majorana fermion. Using the recently developed fuzzy sphere regularization, we directly extract the scaling dimensions of low-lying operators via the state-operator correspondence. At the critical point, we demonstrate conformal multiplet structure together with the hallmark of emergent spacetime supersymmetry through characteristic relations between fermionic and bosonic operators. Moreover, by tuning the Yukawa coupling, we explicitly track the evolution of operator spectra from the decoupled Ising-Majorana fixed point to the interacting superconformal fixed point, revealing renormalization-group flow at the operator level. Our results establish a controlled, non-perturbative microscopic route to 3D SCFTs.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.25054v1</guid></item><item><title>[cond-mat updates on arXiv.org] Learning Density Functionals to Bridge Particle and Continuum Scales</title><link>https://arxiv.org/abs/2512.23840</link><description>arXiv:2512.23840v1 Announce Type: cross
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Abstract: Predicting interfacial thermodynamics across molecular and continuum scales remains a central challenge in computational science. Classical density functional theory (cDFT) provides a first-principles route to connect microscopic interactions with macroscopic observables, but its predictive accuracy depends on approximate free-energy functionals that are difficult to generalize. Here we introduce a physics-informed learning framework that augments cDFT with neural corrections trained directly against molecular-dynamics data through adjoint optimization. Rather than replacing the theory with a black-box surrogate, we embed compact neural networks within the Helmholtz free-energy functional, learning local and nonlocal corrections that preserve thermodynamic consistency while capturing missing correlations. Applied to Lennard-Jones fluids, the resulting augmented excess free-energy functional quantitatively reproduces equilibrium density profiles, coexistence curves, and surface tensions across a broad temperature range, and accurately predicts contact angles and droplet shapes far beyond the training regime. This approach combines the interpretability of statistical mechanics with the adaptability of modern machine learning, establishing a general route to learned thermodynamic functionals that bridge molecular simulations and continuum-scale models.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.23840v1</guid></item><item><title>[cond-mat updates on arXiv.org] CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution</title><link>https://arxiv.org/abs/2512.23880</link><description>arXiv:2512.23880v1 Announce Type: cross
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Abstract: Large language model (LLM) agents currently depend on predefined tools or brittle tool generation, constraining their capability and adaptability to complex scientific tasks. We introduce CASCADE, a self-evolving agentic framework representing an early instantiation of the transition from "LLM + tool use" to "LLM + skill acquisition". CASCADE enables agents to master complex external tools and codify knowledge through two meta-skills: continuous learning via web search and code extraction, and self-reflection via introspection and knowledge graph exploration, among others. We evaluate CASCADE on SciSkillBench, a benchmark of 116 materials science and chemistry research tasks. CASCADE achieves a 93.3% success rate using GPT-5, compared to 35.4% without evolution mechanisms. We further demonstrate real-world applications in computational analysis, autonomous laboratory experiments, and selective reproduction of published papers. Along with human-agent collaboration and memory consolidation, CASCADE accumulates executable skills that can be shared across agents and scientists, moving toward scalable AI-assisted scientific research.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.23880v1</guid></item><item><title>[cond-mat updates on arXiv.org] Assessment of First-Principles Methods in Modeling the Melting Properties of Water</title><link>https://arxiv.org/abs/2512.23940</link><description>arXiv:2512.23940v1 Announce Type: cross
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Abstract: First-principles simulations have played a crucial role in deepening our understanding of the thermodynamic properties of water, and machine learning potentials (MLPs) trained on these first-principles data widen the range of accessible properties. However, the capabilities of different first-principles methods are not yet fully understood due to the lack of systematic benchmarks, the underestimation of the uncertainties introduced by MLPs, and the neglect of nuclear quantum effects (NQEs). Here, we systematically assess first-principles methods by calculating key melting properties using path integral molecular dynamics (PIMD) driven by Deep Potential (DP) models trained on data from density functional theory (DFT) with SCAN, revPBE0-D3, SCAN0 and revPBE-D3 functionals, as well as from the MB-pol potential. We find that MB-pol is in qualitatively good agreement with the experiment in all properties tested, whereas the four DFT functionals incorrectly predict that NQEs increase the melting temperature. SCAN and SCAN0 slightly underestimate the density change between water and ice upon melting, but revPBE-D3 and revPBE0-D3 severely underestimate it. Moreover, SCAN and SCAN0 correctly predict that the maximum liquid density occurs at a temperature higher than the melting point, while revPBE-D3 and revPBE0-D3 predict the opposite behavior. Our results highlight limitations in widely used first-principles methods and call for a reassessment of their predictive power in aqueous systems.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.23940v1</guid></item><item><title>[cond-mat updates on arXiv.org] Testing Noise Correlations by an AI-Assisted Two-Qubit Quantum Sensor</title><link>https://arxiv.org/abs/2512.24135</link><description>arXiv:2512.24135v1 Announce Type: cross
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Abstract: We introduce and validate a machine learning-assisted protocol to classify time and space correlations of classical noise acting on a quantum system, using two interacting qubits as probe. We consider different classes of noise, according to their Markovianity and spatial correlations. Leveraging the sensitivity of a coherent population transfer protocol under three distinct driving conditions, the various noises are discriminated by only measuring the final transfer efficiencies. This approach reaches around 90% accuracy with a minimal experimental overhead.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.24135v1</guid></item><item><title>[cond-mat updates on arXiv.org] Unsupervised Topological Phase Discovery in Periodically Driven Systems via Floquet-Bloch State</title><link>https://arxiv.org/abs/2512.24822</link><description>arXiv:2512.24822v1 Announce Type: cross
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Abstract: Floquet engineering offers an unparalleled platform for realizing novel non-equilibrium topological phases. However, the unique structure of Floquet systems, which includes multiple quasienergy gaps, poses a significant challenge to classification using conventional analytical methods. We propose a novel unsupervised machine learning framework that employs a kernel defined in momentum-time ($\boldsymbol{k},t$) space, constructed directly from Floquet-Bloch eigenstates. This approach is intrinsically data-driven and requires no prior knowledge of the underlying topological invariants, providing a fundamental advantage over prior methods that rely on abstract concepts like the micromotion operator or homotopic transformations. Crucially, this work successfully reveals the intrinsic topological characteristics encoded within the Floquet eigenstates themselves. We demonstrate that our method robustly and simultaneously identifies the topological invariants associated with both the $0$-gap and the $\pi$-gap across various symmetry classes (1D AIII, 1D D, and 2D A), establishing a robust methodology for the systematic classification and discovery of complex non-equilibrium topological matter.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.24822v1</guid></item><item><title>[cond-mat updates on arXiv.org] Dynamic breaking of axial symmetry of acoustic waves in crystals as the origin of nonlinear elasticity and chaos: Analytical model and MD simulations</title><link>https://arxiv.org/abs/2510.04175</link><description>arXiv:2510.04175v2 Announce Type: replace
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Abstract: A Chain of Springs and Masses (CSM) model is used in the interpretation of molecular dynamics (MD) simulations of movement of atoms in orientated FCC crystals. A force of dynamic origin is found that is perpendicular to the direction of the external shear pressure. It is proportional to the square of the applied pressure; It causes breaking of axial symmetry for propagation of transverse acoustic waves. It leads to a non-linear elastic response of crystals and to chaotic patterns in the motion of atoms. We provide an analytical derivation of an effective atomistic 3D potential for interaction between crystallographic layers. The potential is found to possess a component that has an anharmonic threefold axial symmetry around one direction. It reduces to the H{\'e}non-Heinen potential in a 2D cross-section, leading to mathematically rich, complex dynamic features. Results of simulation predict displacements of atoms that are inconsistent with the static theory of elasticity that may have been overlooked in experiments.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2510.04175v2</guid></item><item><title>[cond-mat updates on arXiv.org] GoodRegressor: A General-Purpose Symbolic Regression Framework for Physically Interpretable Materials Modeling</title><link>https://arxiv.org/abs/2510.18325</link><description>arXiv:2510.18325v4 Announce Type: replace
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Abstract: Symbolic regression offers a promising route toward interpretable machine learning, yet existing methods suffer from poor predictability and computational intractability when exploring large expression spaces. I introduce GoodRegressor, a general-purpose C++-based framework that resolves these limitations while preserving full physical interpretability. By combining hierarchical descriptor construction, interaction discovery, nonlinear transformations, statistically rigorous model selection, and stacking ensemble, GoodRegressor efficiently explores symbolic model spaces such as $1.44 \times 10^{457}$, $5.99 \times 10^{124}$, and $4.20 \times 10^{430}$ possible expressions for oxygen-ion conductors, NASICONs, and superconducting oxides, respectively. Across these systems, it produces compact equations that surpass state-of-the-art black-box models and symbolic regressors, improving $R^2$ by $4 \sim 40$ %. The resulting expressions reveal physical insights, for example, into oxygen-ion transport through coordination environment and lattice flexibility. Independent ensemble runs yield nearly identical regressed values and the identical top-ranked candidate, demonstrating high reproducibility. With scalability up to $10^{4392}$ choices without interaction terms, GoodRegressor provides a foundation for general-purpose interpretable machine intelligence.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2510.18325v4</guid></item><item><title>[cond-mat updates on arXiv.org] Thermodynamic Phase Stability, Structural, Mechanical, Optoelectronic, and Thermoelectric Properties of the III-V Semiconductor AlSb for Energy Conversion Applications</title><link>https://arxiv.org/abs/2512.22277</link><description>arXiv:2512.22277v2 Announce Type: replace
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Abstract: This study presents a first principles investigation of the structural, thermodynamic, electronic, optical and thermoelectric properties of aluminum antimonide (AlSb) in its cubic (F-43m) and hexagonal (P63mc) phases. Both structures are dynamically and mechanically stable, as confirmed by phonon calculations and the Born Huang criteria. The lattice constants obtained using the SCAN and PBEsol functionals show good agreement with experimental data. The cubic phase exhibits a direct band gap of 1.66 to 1.78 eV, while the hexagonal phase shows a band gap of 1.48 to 1.59 eV, as confirmed by mBJ and HSE06 calculations. Under external pressure, the band gap decreases in the cubic phase and increases in the hexagonal phase due to different s p orbital hybridization mechanisms. The optical absorption coefficient reaches 1e6 cm-1, which is comparable to or higher than values reported for other III V semiconductors. The Seebeck coefficient exceeds 1500 microV per K under intrinsic conditions, and the thermoelectric performance improves above 600 K due to enhanced phonon scattering and lattice anharmonicity. The calculated formation energies (-1.316 eV for F-43m and -1.258 eV for P63mc) confirm that the cubic phase is thermodynamically more stable. The hexagonal phase exhibits higher anisotropy and lower lattice stiffness, which is favorable for thermoelectric applications. These results demonstrate the strong interplay between crystal symmetry, phonon behavior and charge transport, and provide useful guidance for the design of AlSb based materials for optoelectronic and energy conversion technologies.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.22277v2</guid></item><item><title>[cond-mat updates on arXiv.org] CrystalDiT: A Diffusion Transformer for Crystal Generation</title><link>https://arxiv.org/abs/2508.16614</link><description>arXiv:2508.16614v3 Announce Type: replace-cross
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Abstract: We present CrystalDiT, a diffusion transformer for crystal structure generation that achieves state-of-the-art performance by challenging the trend of architectural complexity. Instead of intricate, multi-stream designs, CrystalDiT employs a unified transformer that imposes a powerful inductive bias: treating lattice and atomic properties as a single, interdependent system. Combined with a periodic table-based atomic representation and a balanced training strategy, our approach achieves 8.78% SUN (Stable, Unique, Novel) rate on MP-20, substantially outperforming recent methods including FlowMM (4.21%) and MatterGen (3.66%). Notably, CrystalDiT generates 63.28% unique and novel structures while maintaining comparable stability rates, demonstrating that architectural simplicity can be more effective than complexity for materials discovery. Our results suggest that in data-limited scientific domains, carefully designed simple architectures outperform sophisticated alternatives that are prone to overfitting.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2508.16614v3</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Machine Learning‐Augmented Piezoelectric‐Ferroelectret Nanogenerators for Highly Sensitive Respiration Monitoring in Wearable Healthcare</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202522897?af=R</link><description>Advanced Functional Materials, EarlyView.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Wed, 31 Dec 2025 14:25:09 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202522897</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Ultralong‐Cycling‐Life Sodium Metal Capacitors Enabled by Hetero‐Salt Additive Strategy with NaF/LiF Hybrid Solid Electrolyte Interphase</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202525494?af=R</link><description>Advanced Functional Materials, EarlyView.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Wed, 31 Dec 2025 13:54:32 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202525494</guid></item><item><title>[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Challenges in Transitioning from Pellet to Practical Argyrodite-Based All-Solid-State Batteries</title><link>http://dx.doi.org/10.1021/acsenergylett.5c03368</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsenergylett.5c03368/asset/images/medium/nz5c03368_0004.gif" /></p><div><cite>ACS Energy Letters</cite></div><div>DOI: 10.1021/acsenergylett.5c03368</div></description><author>ACS Energy Letters: Latest Articles (ACS Publications)</author><pubDate>Wed, 31 Dec 2025 12:54:38 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsenergylett.5c03368</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Machine Learning–Guided Solvation Engineering of Chiral Viologens for Durable Neutral Aqueous Organic Flow Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202522442?af=R</link><description>Angewandte Chemie International Edition, EarlyView.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Wed, 31 Dec 2025 06:56:15 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202522442</guid></item><item><title>[Nature Communications] Domain oriented universal machine learning potential enables fast exploration of chemical space of battery electrolytes</title><link>https://www.nature.com/articles/s41467-025-67982-0</link><description><p>Nature Communications, Published online: 31 December 2025; <a href="https://www.nature.com/articles/s41467-025-67982-0">doi:10.1038/s41467-025-67982-0</a></p>Efficient modeling of battery electrolytes is limited by the accuracy-cost trade-off. Here, authors develop a universal machine learning potential to accurately calculate transport and solvation properties across a broad chemical space.</description><author>Nature Communications</author><pubDate>Wed, 31 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41467-025-67982-0</guid></item><item><title>[Nature Machine Intelligence] Harnessing the power of single-cell large language models with parameter-efficient fine-tuning using scPEFT</title><link>https://www.nature.com/articles/s42256-025-01170-z</link><description><p>Nature Machine Intelligence, Published online: 31 December 2025; <a href="https://www.nature.com/articles/s42256-025-01170-z">doi:10.1038/s42256-025-01170-z</a></p>He et al. present a parameter-efficient fine-tuning method for single-cell language models that improves performance on unseen diseases, treatments and cell types.</description><author>Nature Machine Intelligence</author><pubDate>Wed, 31 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s42256-025-01170-z</guid></item><item><title>[Nature Machine Intelligence] Assessing the potential of deep learning for protein–ligand docking</title><link>https://www.nature.com/articles/s42256-025-01160-1</link><description><p>Nature Machine Intelligence, Published online: 31 December 2025; <a href="https://www.nature.com/articles/s42256-025-01160-1">doi:10.1038/s42256-025-01160-1</a></p>Morehead et al. introduce the benchmark PoseBench and evaluate the strengths and limitations of current AI-based protein–ligand docking and structure prediction methods.</description><author>Nature Machine Intelligence</author><pubDate>Wed, 31 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s42256-025-01160-1</guid></item><item><title>[ChemRxiv] A Review on Computational Insights into
|
||
Anion Exchange Membranes for Water
|
||
Electrolysis to Generate Green Hydrogen</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-5jcw1?rft_dat=source%3Ddrss</link><description>Anion exchange membranes (AEMs) have received a lot of attention in electrochemical energy storage
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and conversion systems and have become a better choice to generate green hydrogen than their proton
|
||
exchange membrane counterparts owing to the non-acidic working conditions as well as the use of nonprecious metal catalytic electrodes. Albeit the safe operating conditions as well as the use of non-precious
|
||
metals, the ion conductivity and technology readiness level of AEMs are significantly lower than their PEM
|
||
counterparts. It is well accepted that the key factors that drive their performance are anion conductivity,
|
||
water uptake and chemical stability. However, there exist several other parameters that influence not
|
||
only the KPI’s but also the overall electrochemical performance of AEMs. The objective of this study is to
|
||
compile the various physical processes in an anion exchange membrane water electrolyser and focus on
|
||
the dominant ones that define the performance of these membranes. We further propose appropriate
|
||
methods to predict the KPIs using multiscale approach. In this report, we elaborately discuss the abovementioned points with a note that, this area still requires substantial research and profound
|
||
understanding from both experimental and computational point of view. In this article, a comprehensive
|
||
review on molecular dynamics simulation methods for anion exchange membranes is extensively
|
||
discussed. We also briefly touch upon the data analytics-based approaches to predict ion conductivity in these
|
||
membranes.</description><author>ChemRxiv</author><pubDate>Wed, 31 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-5jcw1?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Revealing amyloid-β peptide isoforms, including post-translationally modified species, using electrochemical profiling with a dual-electrode set-up</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-j5v38?rft_dat=source%3Ddrss</link><description>The amyloid-β (Aβ) peptides are crucial biomarkers for the diagnosis of Alzheimer's disease (AD), the most common neurodegenerative disease. The high diversity of the Aβ family provides a significant challenge for recognizing various Aβ forms, which may differ by a single amino acid or a post-translational modification. Such variation at the N-terminus of Aβ peptides leads to changes in their properties associated with typical AD biomolecular mechanisms, such as aggregation or generation of reactive oxygen species (ROS). In this work, a novel method for discriminating Aβ peptides with physiologically occurring truncations and modifications at their N-termini, based on the electrochemical profiling of their Cu(II) complexes, is presented. A dual-electrode set-up incorporating both glassy carbon and gold electrodes, together with Differential Pulse Voltammetry (DPV), was employed to generate unique electrochemical profiles, which were subsequently analyzed using chemometric techniques, including Principal Component Analysis (PCA) for data exploration, and Partial Least Squares Discriminant Analysis (PLS-DA) for classification. By combining electrochemical measurements with machine learning algorithms for pattern recognition, we successfully differentiated the studied Aβ forms, Aβ1-16, Aβ3-16, Aβpyr3-16, Aβ4-16, Aβ5-16, Aβ11-16, and Aβpyr11-16. The integration of machine learning not only enhances detection accuracy but also identifies subtle patterns that could support early-stage diagnostics. These findings support the ongoing development of analytical strategies that seek to improve the detection range and accuracy of Aβ peptides identification in Alzheimer’s disease research.</description><author>ChemRxiv</author><pubDate>Wed, 31 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-j5v38?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Sensing the Acidity of Hydrogen Bond Networks</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-twv66?rft_dat=source%3Ddrss</link><description>The reactivity of hydrogen bond networks (HBNs) is critical to many chemical and biological scenarios. When the HBNs are under constraint, hydrogen bond strength and acidity are affected significantly. HBNs exhibit cooperativity, where connections formed in one part of the HBN influence its behavior elsewhere. We combined experimental and computational approaches to examine the growth of the HBNs of water and hexafluoroisopropanol (HFIP), constrained by an aprotic cosolvent. We independently employed vibrational frequency shift of an acetonitrile probe, 1H NMR chemical shift of an aniline probe, and molecular dynamics with machine learning interatomic potentials, to demonstrate the increase in the hydrogen bond strength with the growth of the HBNs. Finally, using vibrational spectroscopy of a titratable probe, we established that not only the hydrogen bond strength, but also the acidity of HFIP is affected by the changes in the network geometry. These results enable the engineering and measurement of HBNs in confined environments with tailored acidity.</description><author>ChemRxiv</author><pubDate>Wed, 31 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-twv66?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Thiol-bearing tertiary alkylammonium chloride for regulation of PbI2 excess in FAPbI3 perovskite solar cells</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-0wscb-v2?rft_dat=source%3Ddrss</link><description>One of the key strategies for record photovoltaic efficiencies in metal halide perovskite solar cells is the addition of PbI2 excess in a stoichiometric perovskite solution which controls crystallization, passivates defects and induces a preferred orientation in the perovskite layer. However, residual PbI2, typically found in the perovskite layer after crystallization, generates non-radiative recombination centres and promotes ion migration under light and heating stress, thus accelerating performance loss. To mitigate the above issues, a common strategy is the post-deposition of organic ammonium salts which interact in situ with residual PbI2. Here, we adopt a multifunctional alkylammonium salt, 2-diethylaminoethanethiol hydrochloride (DEAET), in which both the thiol (–SH) and protonated tertiary amine groups can strongly bind to PbI₂. Upon deposition of DEAET on top of FAPbI3 film, we show that DEAT decreases the percentage of residual PbI2 by 40% and totally eliminates Pb0. These two effects lead to enhanced radiative recombination, proving a net passivation effect, while chemical analysis (FTIR and liquid-state NMR) explains that this is due to strong interactions between tertiary protonated ammonium (-NH+) and thiol (-SH) groups of DEAT with under-coordinated Pb2+. The stabilization of FAPbI3 black phase along with the establishment of a solid barrier to impede the infiltration of moisture into the perovskite layer over time lead to enhanced operational stability for the as-fabricated solar cells. The encouraging findings of this study lay the foundation for the utilization of tertiary ammonium thiol-based salts as efficient agents for interface engineering in perovskite solar cells.</description><author>ChemRxiv</author><pubDate>Wed, 31 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-0wscb-v2?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] LAMMPS-ANI: Large Scale Molecular Dynamics Simulations
|
||
with ANI Neural Network Potential</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-8v03m?rft_dat=source%3Ddrss</link><description>Machine Learning Interatomic Potentials (MLIPs), trained with Quantum Mechanics data, can model
|
||
potential energy surfaces for molecular systems with very high accuracy and extreme speedups compared
|
||
to reference quantum calculations, offering a powerful tool for studying complex chemical and biological
|
||
systems. This work presents the LAMMPS-ANI interface, which scales our ANI neural network potential
|
||
models for large systems, demonstrated with up to 100 million atoms across up to 128 NVIDIA A100
|
||
GPUs. The high performance of LAMMPS-ANI was achieved through a comprehensive code redesign,
|
||
in-depth performance profiling, and advanced GPU performance optimizations. Our benchmarks show
|
||
that ANI is 30 to 60 times faster than the state-of-the-art Allegro Model, emphasizing its speed and
|
||
efficiency. We highlight our work in large-scale molecular dynamics using ANI potentials, presenting
|
||
benchmark results for water boxes (up to 100 million atoms) and a solvated HIV capsid (44 million
|
||
atoms). We also present results for accurately simulating complex reaction processes at unprecedented
|
||
scales, such as methane combustion (300 thousand atoms) and early Earth chemistry experiment (228
|
||
thousand atoms) demonstrating the spontaneous formation of glycine.</description><author>ChemRxiv</author><pubDate>Wed, 31 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-8v03m?rft_dat=source%3Ddrss</guid></item><item><title>[Cell Reports Physical Science] Hierarchical optimization of perovskite solar cell fabrication via step-by-step machine learning</title><link>https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00642-3?rss=yes</link><description>Pu et al. report a hierarchical multi-target Bayesian optimization (MTBO) framework that optimizes the electrospray deposition process for perovskite solar cells. By integrating adaptive constraints and prioritizing thin-film quality across multiple fabrication stages, MTBO efficiently identifies feasible, high-performance conditions, enabling 1.63 eV FA0.82Cs0.18Pb(I0.86Br0.11Cl0.03)3 devices with a champion efficiency of 21.95%.</description><author>Cell Reports Physical Science</author><pubDate>Wed, 31 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00642-3?rss=yes</guid></item><item><title>[ACS Materials Letters: Latest Articles (ACS Publications)] [ASAP] Machine Learning Applications in Predicting Friction Properties of Bearing Steel: A Review</title><link>http://dx.doi.org/10.1021/acsmaterialslett.5c01047</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsmaterialslett.5c01047/asset/images/medium/tz5c01047_0009.gif" /></p><div><cite>ACS Materials Letters</cite></div><div>DOI: 10.1021/acsmaterialslett.5c01047</div></description><author>ACS Materials Letters: Latest Articles (ACS Publications)</author><pubDate>Tue, 30 Dec 2025 19:59:57 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsmaterialslett.5c01047</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Machine Learning-Guided Discovery of Sterically Protected High Triplet Exciplex Hosts for Ultra-Bright Green OLEDs</title><link>http://dx.doi.org/10.1021/jacs.5c16369</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c16369/asset/images/medium/ja5c16369_0007.gif" /></p><div><cite>Journal of the American Chemical Society</cite></div><div>DOI: 10.1021/jacs.5c16369</div></description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Tue, 30 Dec 2025 19:03:11 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c16369</guid></item><item><title>[Wiley: Small Methods: Table of Contents] Standardization and Machine Learning Prediction of Tafel Slope of Pt‐Based Nanocatalysts for High‐Performance HER Catalyst Development</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smtd.202501909?af=R</link><description>Small Methods, EarlyView.</description><author>Wiley: Small Methods: Table of Contents</author><pubDate>Tue, 30 Dec 2025 12:06:41 GMT</pubDate><guid isPermaLink="true">10.1002/smtd.202501909</guid></item><item><title>[npj Computational Materials] Toward high entropy material discovery for energy applications using computational and machine learning methods</title><link>https://www.nature.com/articles/s41524-025-01918-6</link><description><p>npj Computational Materials, Published online: 30 December 2025; <a href="https://www.nature.com/articles/s41524-025-01918-6">doi:10.1038/s41524-025-01918-6</a></p>Toward high entropy material discovery for energy applications using computational and machine learning methods</description><author>npj Computational Materials</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41524-025-01918-6</guid></item><item><title>[APL Machine Learning Current Issue] AI agents for photonic integrated circuit design automation</title><link>https://pubs.aip.org/aip/aml/article/3/4/046113/3375781/AI-agents-for-photonic-integrated-circuit-design</link><description><span class="paragraphSection">We present photonics intelligent design and optimization, a proof-of-concept multi-agent framework that converts natural-language photonic integrated circuit (PIC) design requests into layout mask files. This work demonstrates end-to-end PIC design automation using large language models (LLMs), with the goal of achieving structurally valid rather than performance-qualified layouts. We compare seven reasoning LLMs using a testbench of 102 design descriptions that ranged from single devices to 112-component PICs. The success rate for single-device designs was up to 91%. For design queries with ≤15 components, o1, Gemini-2.5-pro, and Claude Opus 4 achieved the highest end-to-end pass@5 success rates of ∼57%, with Gemini-2.5-pro requiring the fewest output tokens and the lowest cost. Future work will extend this framework toward performance qualification through expanded datasets, tighter simulation and optimization loops, and fabrication feedback integration.</span></description><author>APL Machine Learning Current Issue</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/046113/3375781/AI-agents-for-photonic-integrated-circuit-design</guid></item><item><title>[Applied Physics Letters Current Issue] Rattling-induced anharmonicity and multi-valley enhanced thermoelectric performance in layered SmZnSbO material</title><link>https://pubs.aip.org/aip/apl/article/127/26/261902/3375853/Rattling-induced-anharmonicity-and-multi-valley</link><description><span class="paragraphSection">Layered rare-earth oxides have become promising candidates for high-performance thermoelectric (TE) materials on account of the distinctive electronic structures and anisotropic transport properties. In this work, the phonon dynamics, carrier transport, and TE performance of the layered SmZnSbO compound are comprehensively evaluated using first-principles calculations, machine learning interatomic potentials, Boltzmann transport theory, and the two-channel model. The coexistence of weak interlayer van der Waals interactions, robust intralayer covalent bonding interactions, and rattling-like vibrations of Zn atoms synergistically induces significant lattice anharmonicity, resulting in a decreased lattice thermal conductivity (0.84 W/mK@900 K within the framework of the two-channel model) for the SmZnSbO compound. The natural quantum well architecture formed by the alternative conductive [Zn<sub>2</sub>Sb<sub>2</sub>]<sup>2−</sup> layer and the insulated [Sm<sub>2</sub>O<sub>2</sub>]<sup>2+</sup> layer endows quasi-two-dimensional transport characteristics, enabling a high carrier mobility of 34.1 cm<sup>2</sup>/Vs. Moreover, the multi-valley electronic band structure with an indirect bandgap of 0.80 eV simultaneously optimizes electrical conductivity (<span style="font-style: italic;">σ</span>) and Seebeck coefficient (<span style="font-style: italic;">S</span>), resulting in an enhanced power factor. Benefiting from these synergistic features, the layered SmZnSbO compound achieves optimal dimensionless figures of merit (<span style="font-style: italic;">ZT</span>s) of 1.47 and 1.40 for the <span style="font-style: italic;">p</span>-type and <span style="font-style: italic;">n</span>-type doping circumstances at 900 K. The current work not only elucidates the thermal and electronic transport mechanisms for the SmZnSbO compound but also establishes a paradigm for designing high-efficiency layered oxide TE materials through combined strategies of quantum confinement, phonon engineering, and multi-valley band convergence.</span></description><author>Applied Physics Letters Current Issue</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apl/article/127/26/261902/3375853/Rattling-induced-anharmonicity-and-multi-valley</guid></item><item><title>[Applied Physics Letters Current Issue] Magneto-ionic control of perpendicular anisotropy in epitaxial Mn 4 N films</title><link>https://pubs.aip.org/aip/apl/article/127/26/262405/3375847/Magneto-ionic-control-of-perpendicular-anisotropy</link><description><span class="paragraphSection">We report reversible control of the magnetism and perpendicular magnetic anisotropy (PMA) in Mn<sub>4</sub>N thin films through solid-state magneto-ionic gating. We grow Mn<sub>4</sub>N on MgO(100) substrates, exhibiting bulk-like magnetization and strain-induced PMA, also promoted by capping the film with material with large spin–orbit coupling. We demonstrate that the interfacial anisotropy can be reversibly tuned through voltage-driven nitrogen ion migration when Mn<sub>4</sub>N is in contact with a nitrogen-affine metal, such as Ta and V. We also show that solid-state gating effectively enhances the spin–orbit torque switching efficiency by reducing the coercive field without compromising the interface transparency. Finally, we demonstrate that gate-tunable devices can be harnessed for efficient nonvolatile memory functionality.</span></description><author>Applied Physics Letters Current Issue</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apl/article/127/26/262405/3375847/Magneto-ionic-control-of-perpendicular-anisotropy</guid></item><item><title>[Applied Physics Letters Current Issue] Predicting anode coatings for solid-state lithium metal batteries via first-principles thermodynamic calculations and hierarchical ion-transport algorithms</title><link>https://pubs.aip.org/aip/apl/article/127/26/263904/3375845/Predicting-anode-coatings-for-solid-state-lithium</link><description><span class="paragraphSection">Solid-state lithium metal batteries (SSLMBs) are promising for next-generation energy storage devices due to their superior energy density and excellent safety. Among solid-state electrolytes, garnet-type Li<sub>7</sub>La<sub>3</sub>Zr<sub>2</sub>O<sub>12</sub> (LLZO) exhibits a wide electrochemical window and high lithium-ion conductivity, but poor electrode contact and Li dendrite growth restrict its practical application. To address these challenges, this study explores the application of thin film coatings composed of Li<sub>8</sub><span style="font-style: italic;">M</span>P<sub>4</sub> (<span style="font-style: italic;">M</span> = Si, Ge) at the lithium metal anode/LLZO interface. Through comprehensive first-principles thermodynamic calculations and hierarchical ion-transport algorithms, the phase stability, electrochemical stability, chemical stability, ionic transport, Li wettability, and mechanical properties of the candidate materials were systematically predicted and analyzed. Results indicate that the candidate coatings are thermodynamically stable at 0 K, with superior reduction stability against the lithium metal anode and good chemical compatibility with LLZO. Their Li-ion migration barriers are as low as 0.32 eV, enabling room-temperature ionic conductivity of approximately 10<sup>−5</sup> S/cm. Moreover, the predicted works of adhesion for Li/Li<sub>8</sub><span style="font-style: italic;">M</span>P<sub>4</sub> (<span style="font-style: italic;">M</span> = Si, Ge) are 0.99 and 0.76 J/m<sup>2</sup>, respectively, corresponding to the contact angles of 0° and 49.3°, indicating that metallic Li shows good wettability on Li<sub>8</sub><span style="font-style: italic;">M</span>P<sub>4</sub> (<span style="font-style: italic;">M</span> = Si, Ge) materials. This work provides a comprehensive understanding of the thermodynamic and dynamic behaviors of Li<sub>8</sub><span style="font-style: italic;">M</span>P<sub>4</sub> (<span style="font-style: italic;">M</span> = Si, Ge) coatings and will guide the experimental design for desired SSLMB anode coatings.</span></description><author>Applied Physics Letters Current Issue</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apl/article/127/26/263904/3375845/Predicting-anode-coatings-for-solid-state-lithium</guid></item><item><title>[APL Materials Current Issue] Lithography-free fabrication of transparent, durable surfaces with embedded functional materials in glass nanoholes</title><link>https://pubs.aip.org/aip/apm/article/13/12/121112/3375772/Lithography-free-fabrication-of-transparent</link><description><span class="paragraphSection">Touch-enabled technologies, from smartphones to public kiosks, are ubiquitous, yet frequent use turns their surfaces into reservoirs for microbial contamination. Routine alcohol-based cleaning can be impractical on high-touch optical surfaces due to damage risk and usability concerns. Here, we present a scalable approach to transparent, mechanically robust glass surfaces by embedding materials with <span style="font-style: italic;">ad hoc</span> functionality into surface glass nanoholes. We demonstrate the concept with copper nanodisks: copper is an established antimicrobial agent, but its wear susceptibility pose challenges for use on transparent displays. Our design shields the functional material from lateral wear while allowing ion diffusion for antimicrobial efficacy. Fabrication uses only wafer-compatible, lithography-free steps: thermal dewetting of a thin silver film to create a nanosized mask; inverting it to a polymer nanoholes mask by etching the silver nanoparticles; wet etching of the glass to form nanoholes; selective copper deposition inside these holes; and liftoff of excess material. The resulting surfaces exhibit mean transmission of 80%–85% in the 380–750 nm range with haze &lt;1% and minimal color shift, compared to uncoated glass. Antimicrobial efficacy, assessed against <span style="font-style: italic;">Escherichia coli</span> OP50 under a modified U.S. EPA protocol, shows ≈99% bacterial reduction within one hour. Abrasion tests with a crockmeter simulating finger swipes confirm that the embedded copper remains intact, with no measurable change in optical performance. This embedded design provides a scalable route to integrate antimicrobial functionality into high-touch transparent systems while preserving optical clarity and wear resistance, with potential relevance for medical, consumer, and transportation interfaces.</span></description><author>APL Materials Current Issue</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apm/article/13/12/121112/3375772/Lithography-free-fabrication-of-transparent</guid></item><item><title>[ChemRxiv] Augmenting Large Language Models for Automated Discovery of f-Element Extractants</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-b1m2q-v2?rft_dat=source%3Ddrss</link><description>Efficient separation of f-elements is a critical challenge for a wide range of emerging technologies. The chemical similarity among these elements makes the development of selective solvent extraction reagents both slow and difficult. Here, we present a quasi-autonomous, AI-enabled workflow for the design and computational screening of selective extractant ligands. Molecular design is guided by SAFE-MolGen, a large language model-based agentic system that leverages curated extraction data to propose new ligands and preliminarily rank their performance using a supervised machine learning model trained on experimental datasets to consider the impact of realistic experimental conditions. Promising human-approved ligands are then passed to a second automated pipeline that constructs three-dimensional metal-ligand complexes and performs quantum mechanical free energy calculations to directly assess metal selectivity. We demonstrate this approach for Am(III)/Eu(III) separations and report several newly designed ligands predicted to exhibit higher Am(III)/Eu(III) selectivity than the benchmark extractant CyMe4BTBP. This workflow accelerates computational exploration of the molecular space in this data-sparse field and provides a general strategy for the rapid generation and evaluation of novel lanthanide (Ln) and actinide (An) extractants.</description><author>ChemRxiv</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-b1m2q-v2?rft_dat=source%3Ddrss</guid></item><item><title>[RSC - Chem. Sci. latest articles] Solid Dual-Salt Plastic Crystal Electrolyte Enabling Rapid Ion Transfer and Stable Interphases for High-Performance Solid-State Sodium Ion Batteries</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC09186A</link><description><div><i><b>Chem. Sci.</b></i>, 2026, Accepted Manuscript<br /><b>DOI</b>: 10.1039/D5SC09186A, Edge Article</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Yang Jiang, Rui Wang, Peng Xiong, Yangyang Liu, Hongbao Li, Longhai Zhang, Ya You, Chaofeng Zhang<br />As promising next-generation energy storage systems, solid-state sodium ion batteries (SIBs) are hindered by the low ionic conductivity of their solid electrolytes and poor interfacial compatibility. Here, we developed a...<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Chem. Sci. latest articles</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC09186A</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Intelligent Sensing: The Emerging Integration of Machine Learning and Soft Sensors Based on Hydrogels and Ionogels</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202517851?af=R</link><description>Advanced Science, Volume 12, Issue 48, December 29, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Mon, 29 Dec 2025 21:01:21 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202517851</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Pre‐Constructed Mechano‐Electrochemical Adaptive Solid Electrolyte Interphase to Enhance Li+ Diffusion Kinetics and Interface Stability for Chemically Prelithiated SiO Anodes</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202515555?af=R</link><description>Advanced Science, Volume 12, Issue 48, December 29, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Mon, 29 Dec 2025 21:01:21 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202515555</guid></item><item><title>[Wiley: Small: Table of Contents] Unraveling A‐Site Cation Control of Hot Carrier Relaxation in Vacancy‐Ordered Halide Perovskites Through Quantum Dynamics and Interpretable Machine Learning</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202507018?af=R</link><description>Small, Volume 21, Issue 52, December 29, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Mon, 29 Dec 2025 20:38:41 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202507018</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Rational Design of Metal–Organic Frameworks for Pancreatic Cancer Therapy: from Machine Learning Screening to In Vivo Efficacy (Adv. Mater. 52/2025)</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.71868?af=R</link><description>Advanced Materials, Volume 37, Issue 52, December 29, 2025.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Mon, 29 Dec 2025 19:50:02 GMT</pubDate><guid isPermaLink="true">10.1002/adma.71868</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Rational Design of Metal–Organic Frameworks for Pancreatic Cancer Therapy: from Machine Learning Screening to In Vivo Efficacy</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202412757?af=R</link><description>Advanced Materials, Volume 37, Issue 52, December 29, 2025.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Mon, 29 Dec 2025 19:50:02 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202412757</guid></item><item><title>[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Chloride-Based Solid Electrolytes from Crystal Structure to Electrochemical Performance</title><link>http://dx.doi.org/10.1021/acsenergylett.5c03415</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsenergylett.5c03415/asset/images/medium/nz5c03415_0017.gif" /></p><div><cite>ACS Energy Letters</cite></div><div>DOI: 10.1021/acsenergylett.5c03415</div></description><author>ACS Energy Letters: Latest Articles (ACS Publications)</author><pubDate>Mon, 29 Dec 2025 19:20:24 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsenergylett.5c03415</guid></item><item><title>[Wiley: Small: Table of Contents] Enhancing Cycling Stability and Suppressing Lithium Dendrite Formation With A Hierarchical Artificial Solid Electrolyte Interphase Layer on Lithium Anodes for High‐Voltage Lithium Metal Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202512973?af=R</link><description>Small, EarlyView.</description><author>Wiley: Small: Table of Contents</author><pubDate>Mon, 29 Dec 2025 09:13:44 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202512973</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Gradient‐Heterojunction in Solid Electrolytes for Fast‐Charging Dendrite‐Free Solid‐State Lithium Metal Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202519284?af=R</link><description>Advanced Materials, EarlyView.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Mon, 29 Dec 2025 07:59:12 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202519284</guid></item><item><title>[APL Machine Learning Current Issue] Synthetic images from generative AI for compositional analysis of dried solution patterns</title><link>https://pubs.aip.org/aip/aml/article/3/4/046112/3375778/Synthetic-images-from-generative-AI-for</link><description><span class="paragraphSection">Image-based identification of solutes from dried drop stains offers a low-cost, scalable alternative to traditional chemical analysis, enabled by artificial intelligence and annotated image libraries. While recent advances in robotic droplet imaging have facilitated the collection of such datasets, experimental acquisition remains a key bottleneck. Here, we explore whether synthetic image generation can supplement or replace real data in the context of salt classification. Using Stable Diffusion with low-rank adaptation, we generate 133 000 synthetic images from as few as <span style="font-style: italic;">N</span> = 1–128 experimental images per salt across seven common inorganic solutes. Synthetic images become visually indistinguishable from real ones for <span style="font-style: italic;">N</span> ≥ 6; however, quantitative analysis based on 47 image metrics reveals subtle differences that vanish around <span style="font-style: italic;">N</span> = 128. When used to train random forest, XGBoost, and multilayer perceptron (MLP) models, synthetic data alone achieve classification accuracies of up to 90% in some low-data regimes. Synthetic augmentation offers a powerful strategy for expanding training sets and enabling image-based chemical classification in data-scarce scenarios, even if it seldom outperforms models trained on real data. The realism of synthetic images also highlights the growing need for tools that detect AI-generated scientific images to ensure data integrity.</span></description><author>APL Machine Learning Current Issue</author><pubDate>Mon, 29 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/046112/3375778/Synthetic-images-from-generative-AI-for</guid></item><item><title>[iScience] An Interpretable Machine Learning Model for Predicting the Effectiveness of Low-Dose Methylprednisolone in Treating Long COVID: A Retrospective Study</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02835-4?rss=yes</link><description>Long COVID is a chronic, multisystem disease with limited response to conventional treatments. While low-dose methylprednisolone has shown effectiveness in some patients, individual responses vary, and accurate predictive tools are lacking. This retrospective study included 330 Long COVID patients who received low-dose methylprednisolone treatment across three hospitals. Patients were divided into training (n=202), test (n=33), and external validation sets (n=53, n=42). Using Least Absolute Shrinkage and Selection Operator (LASSO) regression, 38 variables were analyzed to develop six machine learning models.</description><author>iScience</author><pubDate>Mon, 29 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02835-4?rss=yes</guid></item><item><title>[iScience] River plastic hotspot detection from space</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02831-7?rss=yes</link><description>Plastic pollution threatens terrestrial and aquatic ecosystems, and rivers play a central role in transporting and retaining plastics across landscapes. Effective mitigation requires scalable methods to identify riverine plastic accumulation hotspots. Here, we present a semi-automated, cloud-based pipeline that integrates satellite remote sensing and machine learning to detect river plastic hotspots. High-resolution PlanetScope imagery is used to annotate training regions, which are transferred to Sentinel-2 multispectral data to train Random Forest classifiers within Google Earth Engine.</description><author>iScience</author><pubDate>Mon, 29 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02831-7?rss=yes</guid></item><item><title>[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Machine Learning−Accelerated Discovery of Proton-Conducting 2D Materials for Proton Exchange Membranes</title><link>http://dx.doi.org/10.1021/acsnano.5c15161</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsnano.5c15161/asset/images/medium/nn5c15161_0006.gif" /></p><div><cite>ACS Nano</cite></div><div>DOI: 10.1021/acsnano.5c15161</div></description><author>ACS Nano: Latest Articles (ACS Publications)</author><pubDate>Sat, 27 Dec 2025 14:37:43 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsnano.5c15161</guid></item><item><title>[Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)] [ASAP] Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Data set Generation for Training Machine-Learned Interatomic Potentials</title><link>http://dx.doi.org/10.1021/acs.jctc.5c01610</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jctc.5c01610/asset/images/medium/ct5c01610_0010.gif" /></p><div><cite>Journal of Chemical Theory and Computation</cite></div><div>DOI: 10.1021/acs.jctc.5c01610</div></description><author>Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)</author><pubDate>Fri, 26 Dec 2025 18:25:53 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jctc.5c01610</guid></item><item><title>[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Machine Learning Photodynamics Unveils a Controlled H2 Loss Channel in the Methaniminium Cation</title><link>http://dx.doi.org/10.1021/acs.jpclett.5c03196</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpclett.5c03196/asset/images/medium/jz5c03196_0006.gif" /></p><div><cite>The Journal of Physical Chemistry Letters</cite></div><div>DOI: 10.1021/acs.jpclett.5c03196</div></description><author>The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)</author><pubDate>Fri, 26 Dec 2025 17:51:53 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpclett.5c03196</guid></item><item><title>[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Solvent Structure and Dynamics Controlled Memristive Ion Transport in Å-Scale Channels</title><link>http://dx.doi.org/10.1021/acs.jpclett.5c03397</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpclett.5c03397/asset/images/medium/jz5c03397_0005.gif" /></p><div><cite>The Journal of Physical Chemistry Letters</cite></div><div>DOI: 10.1021/acs.jpclett.5c03397</div></description><author>The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)</author><pubDate>Fri, 26 Dec 2025 16:50:38 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpclett.5c03397</guid></item><item><title>[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Design of Open Ion Transport Channels in Thick Electrodes through Directional Freezing-Assisted 3D Printing: Enhancing the Electrochemical Performance of High-Load Electrodes</title><link>http://dx.doi.org/10.1021/acs.jpclett.5c02968</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpclett.5c02968/asset/images/medium/jz5c02968_0007.gif" /></p><div><cite>The Journal of Physical Chemistry Letters</cite></div><div>DOI: 10.1021/acs.jpclett.5c02968</div></description><author>The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)</author><pubDate>Fri, 26 Dec 2025 16:49:57 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpclett.5c02968</guid></item><item><title>[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Machine Learning Analysis of Dimensionality Effects on Bandgap Prediction</title><link>http://dx.doi.org/10.1021/acs.jpcc.5c05232</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpcc.5c05232/asset/images/medium/jp5c05232_0011.gif" /></p><div><cite>The Journal of Physical Chemistry C</cite></div><div>DOI: 10.1021/acs.jpcc.5c05232</div></description><author>The Journal of Physical Chemistry C: Latest Articles (ACS Publications)</author><pubDate>Fri, 26 Dec 2025 16:06:02 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpcc.5c05232</guid></item><item><title>[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Machine Learning-Accelerated Development of Li-Rich Layered Oxides with High Reversible Capacity and Coulombic Efficiency</title><link>http://dx.doi.org/10.1021/acsnano.5c16117</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsnano.5c16117/asset/images/medium/nn5c16117_0006.gif" /></p><div><cite>ACS Nano</cite></div><div>DOI: 10.1021/acsnano.5c16117</div></description><author>ACS Nano: Latest Articles (ACS Publications)</author><pubDate>Fri, 26 Dec 2025 09:21:05 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsnano.5c16117</guid></item><item><title>[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] A complete spatial map of mouse retinal ganglion cells reveals density and gene expression specializations</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2515449122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 52, December 2025. <br />SignificanceRetinal ganglion cells (RGCs) transmit visual signals to the brain, and their diversity supports specialized visual functions. Using gene expression mapping and machine learning, we charted the spatial distribution of all known RGC types in ...</description><author>Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents</author><pubDate>Fri, 26 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2515449122?af=R</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Mendelian Randomization and Double Machine Learning Modeling Reveal Brain Imaging‐Derived Phenotypes as Functional Contributors to 18 Autoimmune Inflammatory Diseases</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202515675?af=R</link><description>Advanced Science, EarlyView.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Fri, 26 Dec 2025 06:23:13 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202515675</guid></item><item><title>[Wiley: Carbon Energy: Table of Contents] Data‐Driven Design of Scalable Perovskite Film Fabrication via Machine Learning–Guided Processing</title><link>https://onlinelibrary.wiley.com/doi/10.1002/cey2.70164?af=R</link><description>Carbon Energy, EarlyView.</description><author>Wiley: Carbon Energy: Table of Contents</author><pubDate>Fri, 26 Dec 2025 06:22:51 GMT</pubDate><guid isPermaLink="true">10.1002/cey2.70164</guid></item><item><title>[iScience] Entropy-based byte patching transformer for self-supervised pretraining of SMILES data</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02816-0?rss=yes</link><description>Efficient molecular representations are critical for improving the performance and generalization of large language models in chemical learning. Transformer-based architectures have advanced molecular representation learning, yet capturing localized and hierarchical chemical structures remains challenging. We introduce the SMILES Byte-Patch Transformer (SMiBPT), an adaptive model that dynamically segments SMILES and DeepSMILES strings into chemically meaningful substructures through entropy-based byte patching.</description><author>iScience</author><pubDate>Fri, 26 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02816-0?rss=yes</guid></item><item><title>[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Navigating the Catholyte Landscape in All-Solid-State Batteries</title><link>http://dx.doi.org/10.1021/acsenergylett.5c03429</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsenergylett.5c03429/asset/images/medium/nz5c03429_0005.gif" /></p><div><cite>ACS Energy Letters</cite></div><div>DOI: 10.1021/acsenergylett.5c03429</div></description><author>ACS Energy Letters: Latest Articles (ACS Publications)</author><pubDate>Wed, 24 Dec 2025 16:14:16 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsenergylett.5c03429</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Printing Nacre‐Mimetic MXene‐Based E‐Textile Devices for Sensing and Breathing‐Pattern Recognition Using Machine Learning</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202508370?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 52, December 23, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Wed, 24 Dec 2025 15:52:36 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202508370</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Role of Crosslinking and Backbone Segmental Dynamics on Ion Transport in Hydrated Anion‐Conducting Polyelectrolytes</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202514589?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 52, December 23, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Wed, 24 Dec 2025 15:52:36 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202514589</guid></item><item><title>[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Conjunctive population coding integrates sensory evidence to guide adaptive behavior</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2520444122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 52, December 2025. <br />SignificanceContext-dependent behavior, i.e., the appropriate action selection according to current circumstances, long-term goals, and recent experiences, hallmarks human cognitive flexibility. But which neural mechanisms integrate prior knowledge with ...</description><author>Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents</author><pubDate>Wed, 24 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2520444122?af=R</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Hyperquaternized Biomass‐Derived Solid Electrolytes: Architecting Superionic Conduction for Sustainable Flexible Zinc‐Air Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202505711?af=R</link><description>Advanced Energy Materials, EarlyView.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Wed, 24 Dec 2025 07:08:52 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202505711</guid></item><item><title>[npj Computational Materials] High-throughput computational framework for high-order anharmonic thermal transport in cubic and tetragonal crystals</title><link>https://www.nature.com/articles/s41524-025-01920-y</link><description><p>npj Computational Materials, Published online: 24 December 2025; <a href="https://www.nature.com/articles/s41524-025-01920-y">doi:10.1038/s41524-025-01920-y</a></p>High-throughput computational framework for high-order anharmonic thermal transport in cubic and tetragonal crystals</description><author>npj Computational Materials</author><pubDate>Wed, 24 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41524-025-01920-y</guid></item><item><title>[Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)] [ASAP] Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials</title><link>http://dx.doi.org/10.1021/acs.jctc.5c01712</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jctc.5c01712/asset/images/medium/ct5c01712_0007.gif" /></p><div><cite>Journal of Chemical Theory and Computation</cite></div><div>DOI: 10.1021/acs.jctc.5c01712</div></description><author>Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)</author><pubDate>Tue, 23 Dec 2025 19:20:50 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jctc.5c01712</guid></item><item><title>[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Tuning ORR Activity of N-Doped Biphenylene-Based Single-Atom Catalysts via DFT and Machine Learning Synergy</title><link>http://dx.doi.org/10.1021/acs.jpcc.5c06757</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpcc.5c06757/asset/images/medium/jp5c06757_0007.gif" /></p><div><cite>The Journal of Physical Chemistry C</cite></div><div>DOI: 10.1021/acs.jpcc.5c06757</div></description><author>The Journal of Physical Chemistry C: Latest Articles (ACS Publications)</author><pubDate>Tue, 23 Dec 2025 18:21:52 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpcc.5c06757</guid></item><item><title>[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Concomitant Enhancement of the Reorientational Dynamics of the BH4– Anions and Mg2+ Ionic Conductivity in Mg(BH4)2·NH3 upon Ligand Incorporation</title><link>http://dx.doi.org/10.1021/acs.jpcc.5c07031</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpcc.5c07031/asset/images/medium/jp5c07031_0012.gif" /></p><div><cite>The Journal of Physical Chemistry C</cite></div><div>DOI: 10.1021/acs.jpcc.5c07031</div></description><author>The Journal of Physical Chemistry C: Latest Articles (ACS Publications)</author><pubDate>Tue, 23 Dec 2025 13:34:12 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpcc.5c07031</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Interpretable Machine Learning for Battery Prognosis: Retrospect and Prospect</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202503067?af=R</link><description>Advanced Energy Materials, Volume 15, Issue 48, December 23, 2025.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 23 Dec 2025 10:15:25 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202503067</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Novel Sodium‐Rare‐Earth‐Silicate‐Based Solid Electrolytes for All‐Solid‐State Sodium Batteries: Structure, Synthesis, Conductivity, and Interface</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202503468?af=R</link><description>Advanced Energy Materials, Volume 15, Issue 48, December 23, 2025.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 23 Dec 2025 10:15:25 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202503468</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Ambipolar Ion Transport Membranes Enable Stable Noble‐Metal‐Free CO2 Electrolysis in Neutral Media</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202504286?af=R</link><description>Advanced Energy Materials, Volume 15, Issue 48, December 23, 2025.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 23 Dec 2025 10:15:25 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202504286</guid></item><item><title>[Wiley: Small: Table of Contents] Supersaturation‐Driven Co‐Precipitation Enables Scalable Wet‐Chemical Synthesis of High‐Purity Na3InCl6 Solid Electrolyte for Sodium‐Ion Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202509165?af=R</link><description>Small, Volume 21, Issue 51, December 23, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Tue, 23 Dec 2025 07:06:10 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202509165</guid></item><item><title>[Wiley: Small: Table of Contents] Synergistic Co‐Optimization Strategy for Electron‐Ion Transport Kinetics in all‐Solid‐State Sulfurized Polyacrylonitrile Cathodes</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202507810?af=R</link><description>Small, Volume 21, Issue 51, December 23, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Tue, 23 Dec 2025 07:06:10 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202507810</guid></item><item><title>[RSC - Chem. Sci. latest articles] Robust Janus-Faced Quasi-Solid-State Electrolytes Mimicking Honeycomb for Fast Transport and Adequate Supply of Sodium Ions</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC08536E</link><description><div><i><b>Chem. Sci.</b></i>, 2026, Accepted Manuscript<br /><b>DOI</b>: 10.1039/D5SC08536E, Edge Article</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Fang Chen, Yadan Xie, Zhoubin Yu, Na Li, Xiang Ding, Yu Qiao<br />Quasi-solid-state electrolytes are one of the most promising alternative candidate for traditional liquid state electrolytes with fast ion transport rate, high mechanical strength and wide temperature adaptation. Here we designed...<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Chem. Sci. latest articles</author><pubDate>Tue, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC08536E</guid></item><item><title>[RSC - Chem. Sci. latest articles] Automated Closed-Loop Continuous Flow Block Copolymer Synthesizer</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07307C</link><description><div><i><b>Chem. Sci.</b></i>, 2026, Accepted Manuscript<br /><b>DOI</b>: 10.1039/D5SC07307C, Edge Article</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution 3.0 Unported Licence.</a></div><div>WeiNian Wong, Daniel Phillips, MD Taifur Rahman, Tanja Junkers<br />A fully automated continuous flow synthesizer for diblock copolymer (BCP) synthesis was constructed comprising elements of flow chemistry, automation, machine learning and in-line monitoring. A new method using in-line FTIR...<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Chem. Sci. latest articles</author><pubDate>Tue, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07307C</guid></item><item><title>[RSC - Chem. Sci. latest articles] Node-Equivariant Message Passing for Efficient and Accurate Machine Learning Interatomic Potentials</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07248D</link><description><div><i><b>Chem. Sci.</b></i>, 2026, Accepted Manuscript<br /><b>DOI</b>: 10.1039/D5SC07248D, Edge Article</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Yaolong Zhang, Hua Guo<br />Machine learned interatomic potentials, particularly equivariant message-passing (MP) models, have demonstrated high fidelity in representing first-principles data, revolutionizing computational studies in materials science, biophysics, and catalysis. However, these equivariant MP...<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Chem. Sci. latest articles</author><pubDate>Tue, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07248D</guid></item><item><title>[iScience] A Multicenter Multimodel Habitat Radiomics Model for Predicting Immunotherapy Response in Advanced NSCLC</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02783-X?rss=yes</link><description>Robust predictive biomarker is critical for identifying NSCLC patients who may benefit from immunotherapy. This study developed a CT-based habitat model using 590 advanced NSCLC cases. The model was constructed in contrast-enhanced CT images and validated on an independent cohort with non-contrast CT. Tumor volumes were segmented into three subregions via K-means clustering. Radiomic features were extracted from each habitat and used to build predictive models with six machine learning classifiers.The ExtraTrees-based habitat model demonstrated superior predictive performance in the test cohort(AUC = 0.814).</description><author>iScience</author><pubDate>Tue, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02783-X?rss=yes</guid></item><item><title>[Joule] Machine learning-driven interface material design for high-performance perovskite solar cells with scalability and band-gap universality</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00445-3?rss=yes</link><description>Machine learning-driven molecular design integrating correlation analysis, clustering, and LASSO regression discovers BIPA, an efficient interface modifier that concurrently passivates defects, optimizes band alignment, and enhances perovskite crystallinity. This strategy enables high-efficiency, scalable, and stable perovskite solar cells across a wide band-gap range (1.55–1.85 eV).</description><author>Joule</author><pubDate>Tue, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00445-3?rss=yes</guid></item><item><title>[Cell Reports Physical Science] A global thermodynamic-kinetic model capturing the hallmarks of liquid-liquid phase separation and amyloid aggregation</title><link>https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00630-7?rss=yes</link><description>Bhandari et al. develop a unified thermodynamic-kinetic framework that integrates liquid-liquid phase separation (LLPS) with amyloid aggregation. By considering oligomerization and fibrillization in both protein-poor and protein-rich phases, the model reproduces concentration-dependent aggregation kinetics and rationalizes the seemingly contradictory reports on whether LLPS accelerates or suppresses fibril formation.</description><author>Cell Reports Physical Science</author><pubDate>Tue, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00630-7?rss=yes</guid></item><item><title>[RSC - Chem. Sci. latest articles] Chemically-informed active learning enables data-efficient multi-objective optimization of self-healing polyurethanes</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07752D</link><description><div><p><img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5SC07752D" /></p></div><div><i><b>Chem. Sci.</b></i>, 2026, Advance Article<br /><b>DOI</b>: 10.1039/D5SC07752D, Edge Article</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Kang Liang, Xinke Qi, Xu Xiao, Li Wang, Jinglai Zhang<br />A chemically-informed active learning (CIAL) framework synergizes chemical knowledge with machine learning to achieve multi-objective optimization of self-healing polyurethanes with only 20 samples, overcoming traditional material design trade-offs.<br />To cite this article before page numbers are assigned, use the DOI form of citation above.<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Chem. Sci. latest articles</author><pubDate>Tue, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07752D</guid></item><item><title>[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Regulating Solvation Structure and Ion Transport via Lewis-Base Dual-Functional Covalent Organic Polymer Separators for Dendrite-Free Li-Metal Anodes</title><link>http://dx.doi.org/10.1021/acsnano.5c14722</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsnano.5c14722/asset/images/medium/nn5c14722_0007.gif" /></p><div><cite>ACS Nano</cite></div><div>DOI: 10.1021/acsnano.5c14722</div></description><author>ACS Nano: Latest Articles (ACS Publications)</author><pubDate>Mon, 22 Dec 2025 20:52:05 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsnano.5c14722</guid></item><item><title>[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Highly Selective Lithium-Ion Separation by Regulating Ion Transport Energy Barriers of Vermiculite Membranes</title><link>http://dx.doi.org/10.1021/acsnano.5c17718</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsnano.5c17718/asset/images/medium/nn5c17718_0005.gif" /></p><div><cite>ACS Nano</cite></div><div>DOI: 10.1021/acsnano.5c17718</div></description><author>ACS Nano: Latest Articles (ACS Publications)</author><pubDate>Mon, 22 Dec 2025 18:30:41 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsnano.5c17718</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500092?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Mon, 22 Dec 2025 17:43:04 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500092</guid></item><item><title>[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Multianion Synergism Boosts High-Performance All-Solid-State Lithium Batteries</title><link>http://dx.doi.org/10.1021/acsnano.5c12987</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsnano.5c12987/asset/images/medium/nn5c12987_0008.gif" /></p><div><cite>ACS Nano</cite></div><div>DOI: 10.1021/acsnano.5c12987</div></description><author>ACS Nano: Latest Articles (ACS Publications)</author><pubDate>Mon, 22 Dec 2025 14:37:35 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsnano.5c12987</guid></item><item><title>[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Density Effects on the Thermal Decomposition of LLM-105 Explored by Neural Network Potential</title><link>http://dx.doi.org/10.1021/acs.jpcc.5c06140</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpcc.5c06140/asset/images/medium/jp5c06140_0016.gif" /></p><div><cite>The Journal of Physical Chemistry C</cite></div><div>DOI: 10.1021/acs.jpcc.5c06140</div></description><author>The Journal of Physical Chemistry C: Latest Articles (ACS Publications)</author><pubDate>Mon, 22 Dec 2025 14:01:00 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpcc.5c06140</guid></item><item><title>[Accounts of Chemical Research: Latest Articles (ACS Publications)] [ASAP] Accelerating Materials Discovery Through Sparse Gaussian Process Machine Learning Potentials</title><link>http://dx.doi.org/10.1021/acs.accounts.5c00667</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.accounts.5c00667/asset/images/medium/ar5c00667_0009.gif" /></p><div><cite>Accounts of Chemical Research</cite></div><div>DOI: 10.1021/acs.accounts.5c00667</div></description><author>Accounts of Chemical Research: Latest Articles (ACS Publications)</author><pubDate>Mon, 22 Dec 2025 13:59:15 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.accounts.5c00667</guid></item><item><title>[Recent Articles in Phys. Rev. Lett.] Gate-Tunable Spectrum and Charge Dispersion Mitigation in a Graphene Superconducting Qubit</title><link>http://link.aps.org/doi/10.1103/3gy7-2r3n</link><description>Author(s): Nicolas Aparicio, Simon Messelot, Edgar Bonet-Orozco, Eric Eyraud, Kenji Watanabe, Takashi Taniguchi, Johann Coraux, and Julien Renard<br /><p>Controlling the energy spectrum of quantum-coherent superconducting circuits, i.e., the energies of excited states, the circuit anharmonicity, and the states’ charge dispersion, is essential for designing performant qubits. This control is usually achieved by adjusting the circuit’s geometry. <i>In sit…</i></p><br />[Phys. Rev. Lett. 135, 266001] Published Mon Dec 22, 2025</description><author>Recent Articles in Phys. Rev. Lett.</author><pubDate>Mon, 22 Dec 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/3gy7-2r3n</guid></item><item><title>[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Maladaptive immunity to the microbiota promotes neuronal hyperinnervation and itch via IL-17A</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2525146122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 52, December 2025. <br />SignificancePruritus (itch), a phenomenon associated with various inflammatory skin diseases including psoriasis and atopic dermatitis, remains a major unmet clinical need with few effective treatments. While sensory hyperinnervation is a hallmark of ...</description><author>Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents</author><pubDate>Mon, 22 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2525146122?af=R</guid></item><item><title>[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] SR-LLM: An incremental symbolic regression framework driven by LLM-based retrieval-augmented generation</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2516995122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 52, December 2025. <br />SignificanceScientists have long sought to derive models from extensive observational input–output data, ensuring these models accurately capture the underlying mapping from inputs to outputs while remaining interpretable to humans through clear meanings. ...</description><author>Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents</author><pubDate>Mon, 22 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2516995122?af=R</guid></item><item><title>[Applied Physics Reviews Current Issue] Thermal conductivity limits of MoS 2 and MoSe 2 : Revisiting high-order anharmonic lattice dynamics with machine learning potentials</title><link>https://pubs.aip.org/aip/apr/article/12/4/041424/3375425/Thermal-conductivity-limits-of-MoS2-and-MoSe2</link><description><span class="paragraphSection">Group-VI transition metal dichalcogenides (TMDs), MoS<sub>2</sub> and MoSe<sub>2</sub>, have emerged as prototypical low-dimensional systems with distinctive phononic and electronic properties, making them attractive for applications in nanoelectronics, optoelectronics, and thermoelectrics. However, their reported lattice thermal conductivities ( κ) remain highly inconsistent, with experimental values and theoretical predictions differing by more than an order of magnitude. These discrepancies stem from uncertainties in measurement techniques, variations in computational protocols, and ambiguities in the treatment of higher-order anharmonic processes. In this study, we critically review these inconsistencies, first by mapping the spread of experimental and modeling results, and then by identifying the methodological origins of divergence. To this end, we bridge first-principles calculations, molecular dynamics simulations, and state-of-the-art machine learning force fields (MLFFs), including recently developed foundation models. We train and benchmark GAP, MACE, NEP, and HIPHIVE against density functional theory and rigorously evaluate the impact of third- and fourth-order phonon scattering processes on κ. The computational efficiency of MLFFs enables us to extend convergence tests beyond conventional limits and to validate predictions through homogeneous nonequilibrium molecular dynamics as well. Our analysis demonstrates that, contrary to some recent claims, fully converged four-phonon processes contribute negligibly to the intrinsic thermal conductivity of both MoS<sub>2</sub> and MoSe<sub>2</sub>. These findings not only refine the intrinsic transport limits of 2D TMDs but also establish MLFF-based approaches as a robust and scalable framework for predictive modeling of phonon-mediated thermal transport in low-dimensional materials.</span></description><author>Applied Physics Reviews Current Issue</author><pubDate>Mon, 22 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/12/4/041424/3375425/Thermal-conductivity-limits-of-MoS2-and-MoSe2</guid></item><item><title>[iScience] Widely Targeted Metabolomics and Machine Learning Identify Succinate as a Key Metabolite in Sepsis-Associated Encephalopathy</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02781-6?rss=yes</link><description>Sepsis-associated encephalopathy (SAE) is a common and serious complication of sepsis that leads to acute brain dysfunction and long-term cognitive impairment. We used widely targeted LC–MS/MS plasma metabolomics in 29 healthy controls, 32 sepsis patients, and 27 SAE patients, combined with machine learning, to define metabolic patterns across these groups. This approach identified 12 discriminatory metabolites, with succinate showing a stepwise increase from health to sepsis to SAE and associations with clinical severity scores.</description><author>iScience</author><pubDate>Mon, 22 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02781-6?rss=yes</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Interpreting X-ray Diffraction Patterns of Metal–Organic Frameworks via Generative Artificial Intelligence</title><link>http://dx.doi.org/10.1021/jacs.5c16416</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c16416/asset/images/medium/ja5c16416_0007.gif" /></p><div><cite>Journal of the American Chemical Society</cite></div><div>DOI: 10.1021/jacs.5c16416</div></description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Sat, 20 Dec 2025 15:03:45 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c16416</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] A Transformative Molecular Muscle Solid Electrolyte</title><link>http://dx.doi.org/10.1021/jacs.5c18427</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c18427/asset/images/medium/ja5c18427_0006.gif" /></p><div><cite>Journal of the American Chemical Society</cite></div><div>DOI: 10.1021/jacs.5c18427</div></description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Fri, 19 Dec 2025 20:12:03 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c18427</guid></item><item><title>[Wiley: Small Structures: Table of Contents] Li6−xFe1−xAlxCl8 Solid Electrolytes for Cost‐Effective All‐Solid‐State LiFePO4 Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/sstr.202500728?af=R</link><description>Small Structures, EarlyView.</description><author>Wiley: Small Structures: Table of Contents</author><pubDate>Fri, 19 Dec 2025 18:40:34 GMT</pubDate><guid isPermaLink="true">10.1002/sstr.202500728</guid></item><item><title>[Joule] Boosting ionic conductivity of fluoride electrolytes by polyanion coordination chemistry enabling 5 V-Class all-solid-state batteries</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00414-3?rss=yes</link><description>This study developed a novel class of highly conductive amorphous fluoride solid-state electrolytes (SSEs) LixTi(PO4)x/3F4, using a polyanion coordination strategy. The optimized Li1.3Ti(PO4)1.3/3F4 achieves a Li+ conductivity of 1.16 × 10−5 S cm−1, two orders of magnitude higher than that of analogous Li2TiF6. Combining the inherent high-voltage stability of fluoride and the excellent ionic conductivity of Li1.3Ti(PO4)1.3/3F4, this material enables superior 5 V-class all-solid-state battery performance. This work opens a new avenue for designing high-conductivity fluoride SSEs and advancing the performance of 5 V-class all-solid-state batteries.</description><author>Joule</author><pubDate>Fri, 19 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00414-3?rss=yes</guid></item><item><title>[RSC - Digital Discovery latest articles] Adsorb-Agent: autonomous identification of stable adsorption configurations via a large language model agent</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00298B</link><description><div><p><img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00298B" /></p></div><div><i><b>Digital Discovery</b></i>, 2026, Advance Article<br /><b>DOI</b>: 10.1039/D5DD00298B, Paper</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Janghoon Ock, Radheesh Sharma Meda, Tirtha Vinchurkar, Yayati Jadhav, Amir Barati Farimani<br />Adsorb-Agent: an LLM-powered agent for determining the most stable adsorption configurations using reasoning and prior knowledge.<br />To cite this article before page numbers are assigned, use the DOI form of citation above.<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Digital Discovery latest articles</author><pubDate>Fri, 19 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00298B</guid></item><item><title>[Recent Articles in Phys. Rev. Lett.] Variational Machine Learning Model for Electronic Structure Optimization via the Density Matrix</title><link>http://link.aps.org/doi/10.1103/wl9w-8g8r</link><description>Author(s): Luqi Dong, Shuxiang Yang, Su-Huai Wei, and Yunhao Lu<br /><p>We present a novel approach that combines machine learning with direct variational energy optimization via the density matrix to solve the Kohn-Sham equation in density functional theory. Instead of relying on the conventional self-consistent field method, our approach directly optimizes the ground …</p><br />[Phys. Rev. Lett. 135, 256403] Published Thu Dec 18, 2025</description><author>Recent Articles in Phys. Rev. Lett.</author><pubDate>Thu, 18 Dec 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/wl9w-8g8r</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Polymorphism Crystal Structure Prediction with Adaptive Space Group Diversity Control</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202510792?af=R</link><description>Advanced Science, Volume 12, Issue 47, December 18, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 18 Dec 2025 09:38:21 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202510792</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Computationally‐Guided Development of Sulfide Solid Electrolyte Powder Coatings for Enhanced Stability and Performance of Solid‐State Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202513191?af=R</link><description>Advanced Science, Volume 12, Issue 47, December 18, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 18 Dec 2025 09:38:21 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202513191</guid></item><item><title>[Proceedings of the National Academy of Sciences: Physical Sciences] Uncovering inequalities in new knowledge learning by large language models across different languages</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2514626122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 51, December 2025. <br />SignificanceLarge language models (LLMs) are transforming daily life, yet users across different languages may not benefit equally. Our study shows that LLMs face greater challenges in learning new knowledge and resisting incorrect or misleading ...</description><author>Proceedings of the National Academy of Sciences: Physical Sciences</author><pubDate>Thu, 18 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2514626122?af=R</guid></item><item><title>[AAAS: Science: Table of Contents] State-independent ionic conductivity</title><link>https://www.science.org/doi/abs/10.1126/science.adk0786?af=R</link><description>Science, Volume 390, Issue 6779, Page 1254-1258, December 2025. <br /></description><author>AAAS: Science: Table of Contents</author><pubDate>Thu, 18 Dec 2025 07:00:11 GMT</pubDate><guid isPermaLink="true">https://www.science.org/doi/abs/10.1126/science.adk0786?af=R</guid></item><item><title>[AAAS: Science: Table of Contents] Scientific production in the era of large language models</title><link>https://www.science.org/doi/abs/10.1126/science.adw3000?af=R</link><description>Science, Volume 390, Issue 6779, Page 1240-1243, December 2025. <br /></description><author>AAAS: Science: Table of Contents</author><pubDate>Thu, 18 Dec 2025 07:00:11 GMT</pubDate><guid isPermaLink="true">https://www.science.org/doi/abs/10.1126/science.adw3000?af=R</guid></item><item><title>[ACS Nano: Latest Articles (ACS Publications)] [ASAP] van Hove Source for Ultralow Power Field-Effect Transistors</title><link>http://dx.doi.org/10.1021/acsnano.5c17157</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsnano.5c17157/asset/images/medium/nn5c17157_0005.gif" /></p><div><cite>ACS Nano</cite></div><div>DOI: 10.1021/acsnano.5c17157</div></description><author>ACS Nano: Latest Articles (ACS Publications)</author><pubDate>Wed, 17 Dec 2025 18:12:49 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsnano.5c17157</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Machine Learning‐assisted Ultrasensitive SERS Immunoassays Across Wide Concentration Ranges Toward Clinical Ovarian Cancer Diagnosis</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202509813?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 51, December 16, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Wed, 17 Dec 2025 14:49:25 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202509813</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Prediction and Fine Screening of Small Molecular Passivation Materials for High‐Efficiency Perovskite Solar Cells Via an Enhanced Machine Learning Workflow</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202511549?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 51, December 16, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Wed, 17 Dec 2025 14:49:25 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202511549</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Tailoring Graphite Interlayers with Electron‐Acceptor Bridges Raises Ion Diffusion Kinetics for Ultrafast Charging Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202509207?af=R</link><description>Advanced Materials, Volume 37, Issue 50, December 17, 2025.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Wed, 17 Dec 2025 14:13:34 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202509207</guid></item><item><title>[Wiley: Small: Table of Contents] Interfacial Catalysis Engineering of Solid Electrolyte Interphase Toward High‐Performance Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202509725?af=R</link><description>Small, Volume 21, Issue 50, December 17, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Wed, 17 Dec 2025 12:51:53 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202509725</guid></item><item><title>[Wiley: Small: Table of Contents] In Situ Construction of Dual‐Functional UiO‐66‐NH2 Coated Li1.3Al0.3Ti1.7(PO4)3 to Achieve Lithium Metal Cells with Efficient Ion Transport in Quasi‐Solid Electrolytes</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202506170?af=R</link><description>Small, Volume 21, Issue 50, December 17, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Wed, 17 Dec 2025 12:51:53 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202506170</guid></item><item><title>[Wiley: Small: Table of Contents] 1D Lithium‐Ion Transport in a LiMn2O4 Nanowire Cathode during Charge–Discharge Cycles</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202507305?af=R</link><description>Small, Volume 21, Issue 50, December 17, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Wed, 17 Dec 2025 12:51:53 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202507305</guid></item><item><title>[Wiley: Small: Table of Contents] A Reversible Zinc Metal Anode with an Inorganic/Organic Solid Electrolyte Interphase Enriched for Epitaxial Deposition Along the Zn (101) Plane</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202510895?af=R</link><description>Small, Volume 21, Issue 50, December 17, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Wed, 17 Dec 2025 12:51:53 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202510895</guid></item><item><title>[Nature Materials] Probing frozen solid electrolyte interphases</title><link>https://www.nature.com/articles/s41563-025-02443-z</link><description><p>Nature Materials, Published online: 17 December 2025; <a href="https://www.nature.com/articles/s41563-025-02443-z">doi:10.1038/s41563-025-02443-z</a></p>Probing frozen solid electrolyte interphases</description><author>Nature Materials</author><pubDate>Wed, 17 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41563-025-02443-z</guid></item><item><title>[Cell Reports Physical Science] Accelerated inorganic materials design with generative AI agents</title><link>https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00618-6?rss=yes</link><description>Takahara et al. introduce MatAgent, a generative AI agent for inorganic materials design that integrates large language model reasoning with diffusion-based generation and property prediction. The human-inspired reasoning process facilitates interpretable and property-directed discovery of inorganic materials.</description><author>Cell Reports Physical Science</author><pubDate>Wed, 17 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00618-6?rss=yes</guid></item><item><title>[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Fluorinated Halide Solid Electrolytes for High-Voltage All-Solid-State Sodium-Ion Batteries Enabling Reversible Oxygen Redox</title><link>http://dx.doi.org/10.1021/acsenergylett.5c03248</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsenergylett.5c03248/asset/images/medium/nz5c03248_0005.gif" /></p><div><cite>ACS Energy Letters</cite></div><div>DOI: 10.1021/acsenergylett.5c03248</div></description><author>ACS Energy Letters: Latest Articles (ACS Publications)</author><pubDate>Tue, 16 Dec 2025 20:00:00 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsenergylett.5c03248</guid></item><item><title>[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Ionic Liquids in Quasi-Solid-State Li–S Batteries with Sulfide-Based Solid Electrolytes: A Density Functional Theory and Ab Initio Molecular Dynamics Study</title><link>http://dx.doi.org/10.1021/acs.jpcc.5c05916</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpcc.5c05916/asset/images/medium/jp5c05916_0019.gif" /></p><div><cite>The Journal of Physical Chemistry C</cite></div><div>DOI: 10.1021/acs.jpcc.5c05916</div></description><author>The Journal of Physical Chemistry C: Latest Articles (ACS Publications)</author><pubDate>Tue, 16 Dec 2025 14:13:16 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpcc.5c05916</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] How Machine Learning Has Driven the Development of Rechargeable Ion Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202504095?af=R</link><description>Advanced Energy Materials, Volume 15, Issue 47, December 16, 2025.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 16 Dec 2025 10:18:19 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202504095</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Interplay Between the Dissolved Mn2+ and Solid Electrolyte Interphases of Graphite Anode</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202503489?af=R</link><description>Advanced Energy Materials, Volume 15, Issue 47, December 16, 2025.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 16 Dec 2025 10:18:19 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202503489</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] From HF Scavenging to Li‐Ion Transport Enhancement: Multifunctional Separator Enabling Stable Li Metal Batteries in Carbonate‐Based Electrolytes</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202505601?af=R</link><description>Advanced Energy Materials, EarlyView.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 16 Dec 2025 09:58:08 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202505601</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Insight Into All‐Solid‐State Lithium‐Sulfur Batteries: Challenges and Interface Engineering at the Electrode‐Sulfide Solid Electrolyte Interface</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202504926?af=R</link><description>Advanced Energy Materials, EarlyView.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 16 Dec 2025 09:45:18 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202504926</guid></item><item><title>[Proceedings of the National Academy of Sciences: Physical Sciences] Designing strongly coupled polaritonic structures via statistical machine learning</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2526690122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 51, December 2025. <br />SignificanceStrong coupling photonics enables precise control of light at subwavelength scales, offering transformative potential in energy conversion and optical information processing. However, designing these systems remains a significant challenge due ...</description><author>Proceedings of the National Academy of Sciences: Physical Sciences</author><pubDate>Tue, 16 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2526690122?af=R</guid></item><item><title>[iScience] What Makes a Scent Trigger a Memory? A Cognitive Decomposition of Odor-Evoked Retrieval</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02728-2?rss=yes</link><description>A single scent can unlock vivid memories. This study investigates the factors that make some odors more evocative than others. We examined odor-evoked episodic memory in 106 participants who experienced odors embedded in distinct visuospatial contexts, and whose memory was tested 24–72 hours later. The protocol empirically dissociates odor recognition (“I’ve already smelled this scent”) and associative memory (“It evokes a memory”) processes. Using machine learning with SHapley Additive exPlanations, we identified distinct predictors for each process.</description><author>iScience</author><pubDate>Tue, 16 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02728-2?rss=yes</guid></item><item><title>[iScience] Combining DNA Methylation Features and Clinical Characteristics Predicts Ketamine Treatment Response for PTSD</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02706-3?rss=yes</link><description>Post-traumatic stress disorder (PTSD) exhibits extensive clinical and biological variability, making treatment challenging. The Consortium to Alleviate PTSD (CAP)-ketamine trial, the largest randomized study of ketamine for PTSD, found no overall benefit of ketamine over placebo, underscoring the necessity to identify responsive subgroups. Using pre-treatment blood DNA methylation profiles and clinical measures from the CAP-ketamine trial, we applied machine learning to predict treatment response.</description><author>iScience</author><pubDate>Tue, 16 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02706-3?rss=yes</guid></item><item><title>[Chem] In situ cryogenic X-ray photoelectron spectroscopy unveils metastable components of the solid electrolyte interphase in Li-ion batteries</title><link>https://www.cell.com/chem/fulltext/S2451-9294(25)00428-0?rss=yes</link><description>Nguyen et al. integrate cryogenic X-ray photoelectron spectroscopy (XPS) with residual gas analysis to study the solid electrolyte interphase (SEI) on graphite anodes in Li-ion batteries. The cryo-state preserves metastable SEI components, such as LiPOxFy, which decompose into stable products, such as LiF, with gas release upon warming. Discussions highlight critical XPS conditions, including ultrahigh vacuum exposure, X-ray-induced damage, and neutralizer settings, which could alter the detection and characterization of SEI components.</description><author>Chem</author><pubDate>Tue, 16 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/chem/fulltext/S2451-9294(25)00428-0?rss=yes</guid></item><item><title>[RSC - Digital Discovery latest articles] Deep learning based SEM image analysis for predicting ionic conductivity in LiZr2(PO4)3-based solid electrolytes</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00232J</link><description><div><p><img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00232J" /></p></div><div><i><b>Digital Discovery</b></i>, 2026, Advance Article<br /><b>DOI</b>: 10.1039/D5DD00232J, Paper</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Kento Murakami, Yudai Yamaguchi, Yo Kato, Kazuki Ishikawa, Naoto Tanibata, Hayami Takeda, Masanobu Nakayama, Masayuki Karasuyama<br />Lithium-ion-conductive oxide materials have attracted considerable attention as solid electrolytes for all-solid-state batteries.<br />To cite this article before page numbers are assigned, use the DOI form of citation above.<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Digital Discovery latest articles</author><pubDate>Mon, 15 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00232J</guid></item><item><title>[RSC - Digital Discovery latest articles] Hierarchical Attention Graph Learning with LLMs Enhancement for Molecular Solubility Prediction</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00407A</link><description><div><i><b>Digital Discovery</b></i>, 2025, Accepted Manuscript<br /><b>DOI</b>: 10.1039/D5DD00407A, Paper</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Yangxin Fan, Yinghui Wu, Roger French, Danny Perez, Michael Taylor, Ping Yang<br />Solubility quantifies the concentration of a molecule that can dissolve in a given solvent. Accurate prediction of solubility is essential for optimizing drug efficacy, improving chemical and separation processes, waste...<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Digital Discovery latest articles</author><pubDate>Mon, 15 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00407A</guid></item><item><title>[iScience] Interpretable machine learning for accessible dysphagia screening and staging in older adults</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02712-9?rss=yes</link><description>Gastroenterology; Health sciences; Internal medicine; Medical specialty; Medicine</description><author>iScience</author><pubDate>Mon, 15 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02712-9?rss=yes</guid></item><item><title>[Joule] Dendrite suppression in garnet electrolytes via thermally induced compressive stress</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00413-1?rss=yes</link><description>Thermal gradients induce compressive stress in garnet solid electrolytes, mechanically toughening them against lithium penetration. The resulting 3-fold increase in critical current density demonstrates that stress engineering can increase critical current densities in solid-state batteries and isolates the role of mechanical stress as a dominant factor in dendrite suppression.</description><author>Joule</author><pubDate>Mon, 15 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00413-1?rss=yes</guid></item><item><title>[RSC - Digital Discovery latest articles] Hierarchical attention graph learning with LLM enhancement for molecular solubility prediction</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00407A</link><description><div><p><img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00407A" /></p></div><div><i><b>Digital Discovery</b></i>, 2026, Advance Article<br /><b>DOI</b>: 10.1039/D5DD00407A, Paper</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Yangxin Fan, Yinghui Wu, Roger H. French, Danny Perez, Michael G. Taylor, Ping Yang<br />Solubility quantifies the concentration of a molecule that can dissolve in a given solvent.<br />To cite this article before page numbers are assigned, use the DOI form of citation above.<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Digital Discovery latest articles</author><pubDate>Mon, 15 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00407A</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Machine Learning Descriptors for Mapping Structure‐Property‐Performance Relationships of Perovskite Solar Cells</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202505294?af=R</link><description>Advanced Energy Materials, EarlyView.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Sat, 13 Dec 2025 07:01:43 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202505294</guid></item><item><title>[ACS Materials Letters: Latest Articles (ACS Publications)] [ASAP] Reaction Pathway Informed Strategy for Fast Solid-State Synthesis of Garnet-Type Solid Electrolyte</title><link>http://dx.doi.org/10.1021/acsmaterialslett.5c01262</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsmaterialslett.5c01262/asset/images/medium/tz5c01262_0005.gif" /></p><div><cite>ACS Materials Letters</cite></div><div>DOI: 10.1021/acsmaterialslett.5c01262</div></description><author>ACS Materials Letters: Latest Articles (ACS Publications)</author><pubDate>Fri, 12 Dec 2025 14:10:55 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsmaterialslett.5c01262</guid></item><item><title>[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Structural Aspects, Ionic Conductivity, and Electrochemical Properties of New Bromine-Substituted Alkali-Based Crystalline Phases MTa(Nb)X6–yBry (M = Li, Na, K; X = Cl, F)</title><link>http://dx.doi.org/10.1021/acsenergylett.5c02904</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsenergylett.5c02904/asset/images/medium/nz5c02904_0007.gif" /></p><div><cite>ACS Energy Letters</cite></div><div>DOI: 10.1021/acsenergylett.5c02904</div></description><author>ACS Energy Letters: Latest Articles (ACS Publications)</author><pubDate>Fri, 12 Dec 2025 13:47:45 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsenergylett.5c02904</guid></item><item><title>[APL Machine Learning Current Issue] Smart detection of plant nutrient deficiencies using machine learning and image fusion</title><link>https://pubs.aip.org/aip/aml/article/3/4/046111/3374702/Smart-detection-of-plant-nutrient-deficiencies</link><description><span class="paragraphSection">Plant nutrient deficiencies are a continual challenge for enhancing global crop output and food security. Historically, manual inspections have been plagued by subjectivity, inefficiency, and restricted scalability, necessitating the development of enhanced detection algorithms. This research introduces a novel approach utilizing image processing and machine learning to enhance detection accuracy and practical applicability. This strategy promotes classification stability by integrating machine learning classifiers, including k-Nearest Neighbors, Artificial Neural Networks, Decision Trees, and Linear Discriminant Analysis, with fusion techniques such as Majority Voting and Mean Fusion. The experiments utilize Leave-One-Out Cross-Validation for model evaluation to address dataset variability and deliver thorough assessments. The study’s results indicate that the suggested system surpasses existing systems in accuracy, precision, recall, and F1 score, attaining an overall accuracy of 98.17%. The method is effective across various noise and resolution parameters, allowing for scalability in precision agriculture applications. This discovery not only enhances the diagnosis of plant nutrient deficiencies but also enables further investigations into real-time plant health monitoring.</span></description><author>APL Machine Learning Current Issue</author><pubDate>Fri, 12 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/046111/3374702/Smart-detection-of-plant-nutrient-deficiencies</guid></item><item><title>[RSC - Digital Discovery latest articles] PEMD: a high-throughput simulation and analysis framework for solid polymer electrolytes</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00454C</link><description><div><p><img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00454C" /></p></div><div><i><b>Digital Discovery</b></i>, 2026, Advance Article<br /><b>DOI</b>: 10.1039/D5DD00454C, Paper</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Shendong Tan, Bochun Liang, Dexin Lu, Chaoyuan Ji, Wenke Ji, Zihui Li, Tingzheng Hou<br />PEMD is an open-source Python framework that integrates polymer construction, force-field parameterization, multiscale simulation, and property analysis, with standardized workflows for screening and data-driven design of solid polymer electrolytes.<br />To cite this article before page numbers are assigned, use the DOI form of citation above.<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Digital Discovery latest articles</author><pubDate>Fri, 12 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00454C</guid></item><item><title>[AI for Science - latest papers] Investigating CO adsorption on Cu(111) and Rh(111) surfaces using machine learning exchange-correlation functionals</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae21fa</link><description>The ‘CO adsorption puzzle’, a persistent failure of utilizing generalized gradient approximations in density functional theory to replicate CO’s experimental preference for top-site adsorption on transition-metal surfaces, remains a critical barrier in surface chemistry. While hybrid functionals such as HSE06 partially resolve this discrepancy, their prohibitive computational cost limits broader applications. We tackle this issue by adopting the Deep Kohn–Sham (DeePKS) method to train machine-learned exchange-correlation functionals. Principal component analysis reveals that the input descriptors for electronic structures separate distinctly across different chemical environments, enabling the DeePKS models to generalize to multi-element systems. We train system-specific DeePKS models for transition-metal surfaces Cu(111) and Rh(111). These models successfully recover experimental site preferences, yielding adsorption energy differences of about 10 meV compared to HSE06, and can be applied to study unseen adsorbate coverages. Furthermore, a single model for the two surfaces is trained, and the model achieves comparable accuracy in predicting not only adsorption energies and site preference but also potential energy surfaces and relaxed surface adsorption structures. The work demonstrates a promising path towards universal models, enabling catalyst exploration with hybrid functional accuracy at substantially reduced cost.</description><author>AI for Science - latest papers</author><pubDate>Fri, 12 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae21fa</guid></item><item><title>[iScience] Consensus machine learning identifies cell death gene signature for carotid artery stenosis diagnosis</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02658-6?rss=yes</link><description>Carotid artery stenosis (CAS) is a major contributor to ischemic stroke, and molecular tools for its early detection remain limited. To address this need, we integrated one in-house RNA-seq cohort with eight public datasets comprising 696 samples, together with proteomic profiling, qRT-PCR, single-cell sequencing, and FYCO1 silencing experiments. From 1,258 curated cell death–related genes, candidates were filtered by logistic regression across cohorts, and ten machine learning algorithms were combined into 105 model configurations to derive a consensus diagnostic classifier.</description><author>iScience</author><pubDate>Fri, 12 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02658-6?rss=yes</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] A Cost‐Effective and Scalable Machine Learning Approach for Quality Assessment of Fresh Maize Kernel Using NIR Spectroscopy</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202512750?af=R</link><description>Advanced Science, Volume 12, Issue 46, December 11, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 11 Dec 2025 09:23:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202512750</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] High‐Performance Zinc–Bromine Rechargeable Batteries Enabled by In‐Situ Formed Solid Electrolyte Interphase</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202508646?af=R</link><description>Advanced Science, Volume 12, Issue 46, December 11, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 11 Dec 2025 09:23:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202508646</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Nonalcoholic Fatty Liver Disease Exacerbates the Advancement of Renal Fibrosis by Modulating Renal CCR2+PIRB+ Macrophages Through the ANGPTL8/PIRB/ALOX5AP Axis</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202509351?af=R</link><description>Advanced Science, Volume 12, Issue 46, December 11, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 11 Dec 2025 09:23:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202509351</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Inverse Design of Metal‐Organic Frameworks for CH4/N2 Separation Enabled by Coupled Machine Learning and Genetic Algorithms</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202513146?af=R</link><description>Advanced Science, Volume 12, Issue 46, December 11, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 11 Dec 2025 09:23:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202513146</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Synergistic Effect of Dual‐Functional Groups in MOF‐Modified Separators for Efficient Lithium‐Ion Transport and Polysulfide Management of Lithium‐Sulfur Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202515034?af=R</link><description>Advanced Science, Volume 12, Issue 46, December 11, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 11 Dec 2025 09:23:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202515034</guid></item><item><title>[Proceedings of the National Academy of Sciences: Physical Sciences] Evaluating large language models in biomedical data science challenges through a classroom experiment</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2521062122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 50, December 2025. <br />SignificanceLarge language models (LLMs) are increasingly used in science and engineering, yet their real-world effectiveness in data analysis remains unclear. In this study, graduate students used LLMs to tackle biomedical data challenges on Kaggle, a ...</description><author>Proceedings of the National Academy of Sciences: Physical Sciences</author><pubDate>Thu, 11 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2521062122?af=R</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Red Phosphorus@SnSe0.5S0.5 Core‐Shell Heterostructure Enables Superior Sodium Storage via Synergistic Ion Diffusion and Polyphosphides Trapping</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202510369?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 50, December 9, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Thu, 11 Dec 2025 06:58:45 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202510369</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Dual‐Site Ni Nanoparticles‐Ru Clusters Anchored on Hierarchical Carbon with Decoupled Gas and Ion Diffusion Channels Enabling Low‐Overpotential, Highly Stable Li‐CO2 Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202514453?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 50, December 9, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Thu, 11 Dec 2025 06:58:45 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202514453</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] IR-Bot: An Autonomous Robotic System for Real-Time Chemical Mixture Analysis via Infrared Spectroscopy and Machine Learning</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505768?af=R</link><description><p><img src="https://www.chinesechemsoc.org/cms/asset/428955b4-1da1-4cbf-b55d-e1bca5f92bd3/keyimage.png" /></p><div><cite>CCS Chemistry, Ahead of Print.</cite></div><div>DOI:10.31635/ccschem.025.202505768</div>Rapid and accurate quantification of chemical mixtures is vital in autonomous chemical experimentation, providing real-time feedback that guides decision-making and reduces resource consumption. Here, we present IR-Bot, an intelligent system that ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Thu, 11 Dec 2025 04:43:30 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505768?af=R</guid></item><item><title>[RSC - Digital Discovery latest articles] Toward smart CO2 capture by the synthesis of metal organic frameworks using large language models</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00446B</link><description><div><p><img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00446B" /></p></div><div><i><b>Digital Discovery</b></i>, 2026, Advance Article<br /><b>DOI</b>: 10.1039/D5DD00446B, Paper</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Hossein Mashhadimoslem, Mohammad Ali Abdol, Kourosh Zanganeh, Ahmed Shafeen, Encheng Liu, Sohrab Zendehboudi, Ali Elkamel, Aiping Yu<br />This research focuses on collecting experimental CO<small><sub>2</sub></small> adsorption data from 433 scientific papers to address the challenges of MOF synthesis methods and the correlation of MOF structure and the effect of their structure on CO<small><sub>2</sub></small> adsorption using LLMs.<br />To cite this article before page numbers are assigned, use the DOI form of citation above.<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Digital Discovery latest articles</author><pubDate>Thu, 11 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00446B</guid></item><item><title>[Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents] Tailoring Thermophysical Properties and Multiscale Machine Learning Modeling of 2D Nanomaterial‐Infused Beeswax as a Green NePCM for Sustainable Thermal Management Systems</title><link>https://onlinelibrary.wiley.com/doi/10.1002/eem2.70194?af=R</link><description>ENERGY &amp;ENVIRONMENTAL MATERIALS, EarlyView.</description><author>Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents</author><pubDate>Wed, 10 Dec 2025 09:54:56 GMT</pubDate><guid isPermaLink="true">10.1002/eem2.70194</guid></item><item><title>[RSC - Digital Discovery latest articles] Optimizing Data Extraction from Materials Science Literature: A Study of Tools Using Large Language Models</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00482A</link><description><div><i><b>Digital Discovery</b></i>, 2025, Accepted Manuscript<br /><b>DOI</b>: 10.1039/D5DD00482A, Paper</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Wenkai Ning, Jeffrey Robert Reimers, Musen Li, Rika Kobayashi<br />Large Language Models (LLMs) are increasingly utilized for large-scale extraction and organization of unstructured data owing to their exceptional Natural Language Processing (NLP) capabilities. Empowering materials design, vast amounts of...<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Digital Discovery latest articles</author><pubDate>Wed, 10 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00482A</guid></item><item><title>[RSC - Digital Discovery latest articles] Optimizing data extraction from materials science literature: a study of tools using large language models</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00482A</link><description><div><p><img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00482A" /></p></div><div><i><b>Digital Discovery</b></i>, 2026, Advance Article<br /><b>DOI</b>: 10.1039/D5DD00482A, Paper</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Wenkai Ning, Musen Li, Jeffrey R. Reimers, Rika Kobayashi<br />Benchmarking five AI tools on materials science literature shows promising capabilities, but performance remains inadequate for large-scale data extraction. Our analysis offers detailed insight and guidance for future methodological improvements.<br />To cite this article before page numbers are assigned, use the DOI form of citation above.<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Digital Discovery latest articles</author><pubDate>Wed, 10 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00482A</guid></item><item><title>[RSC - Chem. Sci. latest articles] Anion-based electrolyte chemistry for sodium-ion batteries: fundamentals, advances and perspectives</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC08154H</link><description><div><p><img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5SC08154H" /></p></div><div><i><b>Chem. Sci.</b></i>, 2026, <b>17</b>,137-150<br /><b>DOI</b>: 10.1039/D5SC08154H, Review Article</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Shu-Yu Li, Yong-Li Heng, Zhen-Yi Gu, Xiao-Tong Wang, Yan Liu, Xin-Ru Zhang, Zhong-Hui Sun, Dai-Huo Liu, Bao Li, Xing-Long Wu<br />This review examines anion-regulated electrolytes for sodium-ion batteries, including solvation structure and mechanism to enhance interfacial stability, ion transport, and extreme-temperature performance, while also outlining future directions.<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Chem. Sci. latest articles</author><pubDate>Tue, 09 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC08154H</guid></item><item><title>[RSC - Chem. Sci. latest articles] A solid composite electrolyte based on three-dimensional structured zeolite networks for high-performance solid-state lithium metal batteries</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC05786H</link><description><div><p><img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5SC05786H" /></p></div><div><i><b>Chem. Sci.</b></i>, 2026, Advance Article<br /><b>DOI</b>: 10.1039/D5SC05786H, Edge Article</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Zhaodi Luo, Yuxin Cui, Zixuan Zhang, Malin Li, Jihong Yu<br />We report a composite solid electrolyte, 3D Zeo/PEO, constructed by integrating a 3D zeolite network into a LiTFSI–PEO matrix, which boosts the performance of batteries by regulating the Li<small><sup>+</sup></small> conduction and deposition, as well as SEI formation.<br />To cite this article before page numbers are assigned, use the DOI form of citation above.<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Chem. Sci. latest articles</author><pubDate>Sun, 07 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC05786H</guid></item><item><title>[Proceedings of the National Academy of Sciences: Physical Sciences] Local equations describe unreasonably efficient stochastic algorithms in random K-SAT</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2510153122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 49, December 2025. <br />SignificanceThe difficulties of algorithmic dynamics in highly nonconvex landscapes are central in several research areas, from hard combinatorial optimization to machine learning. However, it is unclear why and how some particular algorithms find ...</description><author>Proceedings of the National Academy of Sciences: Physical Sciences</author><pubDate>Fri, 05 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2510153122?af=R</guid></item><item><title>[ACS Materials Letters: Latest Articles (ACS Publications)] [ASAP] Beyond Conventional Sodium Superionic Conductor: Fe-Substituted Na3V2(PO4)2F3 Cathodes with Accelerated Charge Transport via Polyol Reflux for Sodium-Ion Batteries</title><link>http://dx.doi.org/10.1021/acsmaterialslett.5c01502</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsmaterialslett.5c01502/asset/images/medium/tz5c01502_0006.gif" /></p><div><cite>ACS Materials Letters</cite></div><div>DOI: 10.1021/acsmaterialslett.5c01502</div></description><author>ACS Materials Letters: Latest Articles (ACS Publications)</author><pubDate>Thu, 04 Dec 2025 13:33:58 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsmaterialslett.5c01502</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Non‐Monotonic Ion Conductivity in Lithium‐Aluminum‐Chloride Glass Solid‐State Electrolytes Explained by Cascading Hopping</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202509205?af=R</link><description>Advanced Science, Volume 12, Issue 45, December 4, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 04 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202509205</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Re‐Purposing a Modular Origami Manipulator Into an Adaptive Physical Computer for Machine Learning and Robotic Perception</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202509389?af=R</link><description>Advanced Science, Volume 12, Issue 45, December 4, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 04 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202509389</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Ultrastable Calcium Metal Anodes Enabled by a Strongly Coordinated Electrolyte Derived Bilayer Solid Electrolyte Interphase</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202510711?af=R</link><description>Advanced Materials, Volume 37, Issue 48, December 3, 2025.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Thu, 04 Dec 2025 07:04:36 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202510711</guid></item><item><title>[RSC - Digital Discovery latest articles] Understanding and mitigating distribution shifts for universal machine learning interatomic potentials</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00260E</link><description><div><p><img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00260E" /></p></div><div><i><b>Digital Discovery</b></i>, 2026, Advance Article<br /><b>DOI</b>: 10.1039/D5DD00260E, Paper</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Tobias Kreiman, Aditi S. Krishnapriyan<br />We find common distribution shifts that pose challenges for universal machine learning interatomic potentials (MLIPs). We develop test-time refinement strategies that mitigate the shifts and provide insights into why MLIPs struggle to generalize.<br />To cite this article before page numbers are assigned, use the DOI form of citation above.<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Digital Discovery latest articles</author><pubDate>Thu, 04 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00260E</guid></item><item><title>[iScience] Physical Cognition in Altered Gravity: Link Between Sensorimotor and Cognitive Adaptability</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02604-5?rss=yes</link><description>A hallmark of human intelligence is rapid adaptation to changing environments. Yet the link between sensorimotor recalibration to new physical conditions and cognitive updating of internal models remains unclear. We addressed this using altered gravity as a model system. In a within-subject study, 25 adults completed a virtual-reality task requiring motor adjustment to non-terrestrial gravities and an online problem-solving task requiring physical reasoning under matched gravity manipulations. Adaptability in each domain was computed relative to performance under terrestrial gravity.</description><author>iScience</author><pubDate>Thu, 04 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02604-5?rss=yes</guid></item><item><title>[Wiley: Small: Table of Contents] Label‐Free Detection of Nuclear Envelope Nucleoporation using 2D Morphological Embeddings and Machine Learning</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202504402?af=R</link><description>Small, Volume 21, Issue 48, December 3, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Wed, 03 Dec 2025 15:24:49 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202504402</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Machine Learning‐Enabled Polymer Discovery for Enhanced Pulmonary siRNA Delivery</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202502805?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 49, December 2, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Wed, 03 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202502805</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Enhanced Potassium Ion Diffusion and Interface Stability Enabled by Potassiophilic rGO/CNTs/NaF Micro‐Lattice Aerogel for High‐Performance Potassium Metal Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202508586?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 49, December 2, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Wed, 03 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202508586</guid></item><item><title>[Nature Reviews Physics] Predicting high-entropy alloy phases with machine learning</title><link>https://www.nature.com/articles/s42254-025-00903-8</link><description><p>Nature Reviews Physics, Published online: 03 December 2025; <a href="https://www.nature.com/articles/s42254-025-00903-8">doi:10.1038/s42254-025-00903-8</a></p>Omokhuwele Umoru explains how generative adversarial networks can help to predict the phases of high-entropy alloys.</description><author>Nature Reviews Physics</author><pubDate>Wed, 03 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s42254-025-00903-8</guid></item><item><title>[iScience] AI enhancing differential diagnosis of acute chronic obstructive pulmonary disease and acute heart failure</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02458-7?rss=yes</link><description>Cardiovascular medicine; Respiratory medicine; Machine learning</description><author>iScience</author><pubDate>Wed, 03 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02458-7?rss=yes</guid></item><item><title>[iScience] United multi-omics and machine learning refine regulatory T cell-defined hepatocellular carcinoma subtypes</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02589-1?rss=yes</link><description>Hepatocellular carcinoma (HCC) is highly heterogeneous and aggressive, and the absence of precision individual treatment regimen enables repeated immune escape. Exploiting Treg-marker genes as key classifiers, we used 10 clustering algorithms to integrate the multi-omics HCC patients data and combined with 10 machine learning (ML) algorithms to delineate molecular subtypes to define molecular subtypes predictive of prognosis and immune response. We identified two cancer subtypes (CSs) that are associated with prognosis, with the second subtype (CS2) showing the most favourable prognostic outcomes.</description><author>iScience</author><pubDate>Wed, 03 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02589-1?rss=yes</guid></item><item><title>[Matter] Unknowium, beyond the banana, and AI discovery in materials science</title><link>https://www.cell.com/matter/fulltext/S2590-2385(25)00557-0?rss=yes</link><description>Recently, the proportion of papers implementing some sort of artificial intelligence (AI) or machine learning (ML) methods in materials science has been growing. It’s hard to ignore such a powerful and exciting tool. Relatedly, I have just returned from the Pujiang Innovation Forum held in Shanghai, China, where I participated in the “AI for Materials Science” session (Figure 1A), speaking as a lowly editor among global experts in the field.</description><author>Matter</author><pubDate>Wed, 03 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/matter/fulltext/S2590-2385(25)00557-0?rss=yes</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Taming Metal–Solid Electrolyte Interface Instability via Metal Strain Hardening</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202303500?af=R</link><description>Advanced Energy Materials, Volume 15, Issue 45, December 2, 2025.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 02 Dec 2025 13:55:59 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202303500</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Self‐Liquefying Conformal Nanocoatings via Phase‐Convertible Ion Conductors for Stable All‐Solid‐State Batteries (Adv. Energy Mater. 45/2025)</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.70345?af=R</link><description>Advanced Energy Materials, Volume 15, Issue 45, December 2, 2025.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 02 Dec 2025 13:55:59 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.70345</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Multiscale Design Strategies of Interface‐Stabilized Solid Electrolytes and Dynamic Interphase Decoding from Atomic‐to‐Macroscopic Perspectives</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202502938?af=R</link><description>Advanced Energy Materials, Volume 15, Issue 45, December 2, 2025.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 02 Dec 2025 13:55:59 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202502938</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Self‐Liquefying Conformal Nanocoatings via Phase‐Convertible Ion Conductors for Stable All‐Solid‐State Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202503562?af=R</link><description>Advanced Energy Materials, Volume 15, Issue 45, December 2, 2025.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 02 Dec 2025 13:55:59 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202503562</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] LoRA-Chem: Modular Machine Learning for Multitask Prediction in Organic Reactions</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202506542?af=R</link><description><p><img src="https://www.chinesechemsoc.org/cms/asset/e0757683-26df-454a-a9ca-b322a8b7935e/keyimage.jpg" /></p><div><cite>CCS Chemistry, Ahead of Print.</cite></div><div>DOI:10.31635/ccschem.025.202506542</div>The prediction of organic chemical reactions has historically presented significant challenges owing to the inherent complexity and mechanistic diversity of reaction processes. In this work, we present LoRA-Chem, an innovative modular framework that ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Tue, 02 Dec 2025 04:48:31 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202506542?af=R</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] LoRA-Chem: Modular Machine Learning for Multitask Prediction in Organic Reactions</title><link>https://ccs-stag.literatumonline.com.localhost.literatumonline.com:25116/doi/abs/10.31635/ccschem.025.202506542?af=R</link><description><p><img src="https://www.chinesechemsoc.org/cms/asset/e0757683-26df-454a-a9ca-b322a8b7935e/keyimage.jpg" /></p><div><cite>CCS Chemistry, Ahead of Print.</cite></div><div>DOI:10.31635/ccschem.025.202506542</div>The prediction of organic chemical reactions has historically presented significant challenges owing to the inherent complexity and mechanistic diversity of reaction processes. In this work, we present LoRA-Chem, an innovative modular framework that ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Tue, 02 Dec 2025 04:48:31 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com.localhost.literatumonline.com:25116/doi/abs/10.31635/ccschem.025.202506542?af=R</guid></item><item><title>[iScience] Dimensionality modulated generative AI for safe biomedical dataset augmentation</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02582-9?rss=yes</link><description>Generative artificial intelligence can expand small biomedical datasets but may amplify noise and distort statistical relationships. We developed genESOM, a framework integrating an error control system into a generative AI method based on emergent self-organizing maps. By separating structure learning from data synthesis, genESOM enables dimensionality modulation and injection of engineered diagnostic features, i.e., permuted versions of real variables, as negative controls that track feature importance stability.</description><author>iScience</author><pubDate>Tue, 02 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02582-9?rss=yes</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500147?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Mon, 01 Dec 2025 22:39:43 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500147</guid></item><item><title>[APL Machine Learning Current Issue] RTNinja : A generalized machine learning framework for analyzing random telegraph noise signals in nanoelectronic devices</title><link>https://pubs.aip.org/aip/aml/article/3/4/046109/3373556/RTNinja-A-generalized-machine-learning-framework</link><description><span class="paragraphSection">Random telegraph noise is a prevalent variability phenomenon in nanoelectronic devices, arising from stochastic carrier exchange at defect sites and critically impacting device reliability and performance. Conventional analysis techniques often rely on restrictive assumptions or manual interventions, limiting their applicability to complex, noisy datasets. Here, we introduce <span style="font-style: italic;">RTNinja</span>, a generalized, fully automated machine learning framework for the unsupervised analysis of random telegraph noise signals. <span style="font-style: italic;">RTNinja</span> deconvolves complex signals to identify the number and characteristics of hidden individual sources without requiring prior knowledge of the system. The framework comprises two modular components: <span style="font-style: italic;">LevelsExtractor</span>, which uses Bayesian inference and model selection to denoise and discretize the signal, and <span style="font-style: italic;">SourcesMapper</span>, which infers source configurations through probabilistic clustering and optimization. To evaluate performance, we developed a Monte Carlo simulator that generates labeled datasets spanning broad signal-to-noise ratios and source complexities; across 7000 such datasets, <span style="font-style: italic;">RTNinja</span> consistently demonstrated high-fidelity signal reconstruction and accurate extraction of source amplitudes and activity patterns. Our results demonstrate that <span style="font-style: italic;">RTNinja</span> offers a robust, scalable, and device-agnostic tool for random telegraph noise characterization, enabling large-scale statistical benchmarking, reliability-centric technology qualification, predictive failure modeling, and device physics exploration in next-generation nanoelectronics.</span></description><author>APL Machine Learning Current Issue</author><pubDate>Mon, 01 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/046109/3373556/RTNinja-A-generalized-machine-learning-framework</guid></item><item><title>[iScience] A pilot study: Incorporating Treponema pallidum antigens into machine learning models for accurate syphilis treatment outcome assessment</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02536-2?rss=yes</link><description>Health informatics; disease; artificial intelligence applications</description><author>iScience</author><pubDate>Mon, 01 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02536-2?rss=yes</guid></item><item><title>[iScience] Bathymetry of the Philippine sea with convolution neural network from multisource marine geodetic data</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02546-5?rss=yes</link><description>Earth sciences; oceanography; geodesy; machine learning</description><author>iScience</author><pubDate>Fri, 28 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02546-5?rss=yes</guid></item><item><title>[iScience] Interpretable machine learning for urothelial cells classification and risk scoring in urine cytology</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02520-9?rss=yes</link><description>Health sciences; Cancer; Machine learning</description><author>iScience</author><pubDate>Thu, 27 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02520-9?rss=yes</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Machine Learning‐Assisted Second‐Order Perturbation Theory for Chemical Potential Correction Toward Hubbard U Determination</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500160?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Wed, 26 Nov 2025 03:49:32 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500160</guid></item><item><title>[RSC - Chem. Sci. latest articles] Data-driven approach to elucidate the correlation between photocatalytic activity and rate constants from excited states</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC06465A</link><description><div><p><img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5SC06465A" /></p></div><div><i><b>Chem. Sci.</b></i>, 2026, <b>17</b>,176-186<br /><b>DOI</b>: 10.1039/D5SC06465A, Edge Article</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution 3.0 Unported Licence.</a></div><div>Ryuga Kunisada, Manami Hayashi, Tabea Rohlfs, Taiki Nagano, Koki Sano, Naoto Inai, Naoki Noto, Takuya Ogaki, Yasunori Matsui, Hiroshi Ikeda, Olga García Mancheño, Takeshi Yanai, Susumu Saito<br />A data-driven framework integrating machine learning and quantum chemical calculations enables elucidation of how rate constants from excited states govern the photocatalytic activity of organic photosensitizers.<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Chem. Sci. latest articles</author><pubDate>Tue, 25 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC06465A</guid></item><item><title>[Wiley: Chinese Journal of Chemistry: Table of Contents] Locked Coplanar Conformation Boosts Rapid Electron/Ion Transport in Linear Polyimide Cathodes for Sodium‐Ion Storage</title><link>https://onlinelibrary.wiley.com/doi/10.1002/cjoc.70366?af=R</link><description>Chinese Journal of Chemistry, EarlyView.</description><author>Wiley: Chinese Journal of Chemistry: Table of Contents</author><pubDate>Mon, 24 Nov 2025 07:33:36 GMT</pubDate><guid isPermaLink="true">10.1002/cjoc.70366</guid></item><item><title>[APL Machine Learning Current Issue] A hybrid neural architecture: Online attosecond x-ray characterization</title><link>https://pubs.aip.org/aip/aml/article/3/4/046107/3373060/A-hybrid-neural-architecture-Online-attosecond-x</link><description><span class="paragraphSection">The emergence of high-repetition-rate x-ray free-electron lasers (XFELs), such as SLAC’s LCLS-II, serves as our canonical example for autonomous controls that necessitate high-throughput diagnostics paired with streaming computational pipelines capable of single-shot analysis with extremely low latency. We present the deterministic characterization with an integrated parallelizable hybrid resolver architecture, a hybrid machine learning framework designed for fast, accurate analysis of XFEL diagnostics using angular streaking-based sinogram images. This architecture integrates convolutional neural networks and bidirectional long short-term memory models to denoise input, identify x-ray sub-spike features, and extract sub-spike relative delays with sub-30 attosecond temporal resolution. Deployed on low-latency hardware, it achieves over 10 kHz throughput with 168.3 <span style="font-style: italic;">μ</span>s inference latency, indicating scalability to 14 kHz with field-programmable gate array integration. By transforming regression tasks into classification problems and leveraging optimized error encoding, we achieve high precision with low-latency performance that is critical for real-time streaming event selection and experimental control feedback signals. This represents a key development in real-time control pipelines for next-generation autonomous science, generally, and high repetition-rate x-ray experiments in particular.</span></description><author>APL Machine Learning Current Issue</author><pubDate>Fri, 21 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/046107/3373060/A-hybrid-neural-architecture-Online-attosecond-x</guid></item><item><title>[Joule] Accelerated discovery of CO2-to-C3-hydrocarbon electrocatalysts with human-in-the-loop</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00394-0?rss=yes</link><description>Despite advances in automation and AI, accelerating discovery in heterogeneous electrocatalysts remains hindered by the experimental challenges of building integrated platforms for synthesis and evaluation, as well as limited performance-relevant data. This work integrates accelerated experimentation, machine learning, and domain expertise to efficiently explore CO2-to-C3 electrocatalysts, adding new mechanistic and data-driven insights to energy science.</description><author>Joule</author><pubDate>Fri, 21 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00394-0?rss=yes</guid></item><item><title>[Joule] Redox-mediated solid-state doping of Spiro-OMeTAD for efficient and robust perovskite photovoltaics</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00398-8?rss=yes</link><description>The volatile and unstable nature of conventional dopants severely limits the performance and operational lifetime of perovskite solar cells. Here, we demonstrate a solid-state doping strategy, enabling a uniform nanoscale doping profile while effectively suppressing ion migration. This strategy yields perovskite solar cells with a certified efficiency of 26.34% and high device stability.</description><author>Joule</author><pubDate>Fri, 21 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00398-8?rss=yes</guid></item><item><title>[AI for Science - latest papers] Learning to be simple</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae1d98</link><description>In this work we employ machine learning to understand structured mathematical data involving finite groups and derive a theorem about necessary properties of generators of finite simple groups. We create a database of all two-generated subgroups of the symmetric group on n-objects and conduct a classification of finite simple groups among them using shallow feed-forward neural networks. We show that this neural network classifier can decipher the property of simplicity with varying accuracies depending on the features. Our neural network model leads to a natural conjecture concerning the generators of a finite simple group. We subsequently prove this conjecture. This new toy theorem comments on the necessary properties of generators of finite simple groups. We show this explicitly for a class of sporadic groups for which the result holds. Our work further makes the case for a machine motivated study of algebraic structures in pure mathematics and highlights the possibility of generating new conjectures and theorems in mathematics with the aid of machine learning.</description><author>AI for Science - latest papers</author><pubDate>Thu, 20 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae1d98</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Taguchi–Bayesian Sampling: A Roadmap for Polymer Database Construction Toward Small Representative Machine Learning</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500150?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Wed, 19 Nov 2025 05:00:22 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500150</guid></item><item><title>[iScience] An explainable machine learning model predicts 30-day readmission after vertebral augmentation</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02400-9?rss=yes</link><description>Orthopedics; Machine learning</description><author>iScience</author><pubDate>Wed, 19 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02400-9?rss=yes</guid></item><item><title>[RSC - Chem. Sci. latest articles] The agentic age of predictive chemical kinetics</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07692G</link><description><div><p><img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5SC07692G" /></p></div><div><i><b>Chem. Sci.</b></i>, 2026, <b>17</b>,27-35<br /><b>DOI</b>: 10.1039/D5SC07692G, Perspective</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Alon Grinberg Dana<br />From LLM reasoning to action: specialized agents coordinate kinetic modeling to produce transparent, uncertainty-aware, reproducible mechanisms.<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Chem. Sci. latest articles</author><pubDate>Wed, 19 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07692G</guid></item><item><title>[Wiley: SmartMat: Table of Contents] Machine Learning Design of Soft Magnetoresponsive Materials With Multidirectional Fields</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smm2.70051?af=R</link><description>SmartMat, Volume 6, Issue 6, December 2025.</description><author>Wiley: SmartMat: Table of Contents</author><pubDate>Tue, 18 Nov 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">10.1002/smm2.70051</guid></item><item><title>[RSC - Digital Discovery latest articles] Advancing metal organic framework and covalent organic framework design via the digital-intelligent paradigm</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00401B</link><description><div><p><img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00401B" /></p></div><div><i><b>Digital Discovery</b></i>, 2026, Advance Article<br /><b>DOI</b>: 10.1039/D5DD00401B, Review Article</div><div><img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /> Open Access</div><div><a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"> <img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /></a>&nbsp; This article is licensed under a <a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window">Creative Commons Attribution-NonCommercial 3.0 Unported Licence.</a></div><div>Bing Ma, Na Qin, Qianqian Yan, Wei Zhou, Sheng Zhang, Xiao Wang, Lipiao Bao, Xing Lu<br />AI and machine learning combined with multiscale simulations accelerate framework materials design. This review summarizes AI-assisted strategies for synthesis prediction, condition optimization, and inverse functional design.<br />To cite this article before page numbers are assigned, use the DOI form of citation above.<br />The content of this RSS Feed (c) The Royal Society of Chemistry</div></description><author>RSC - Digital Discovery latest articles</author><pubDate>Tue, 18 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00401B</guid></item><item><title>[Applied Physics Reviews Current Issue] Synergistic integration of metasurfaces and quantum photonics: Pathways to next-generation technologies</title><link>https://pubs.aip.org/aip/apr/article/12/4/041318/3372590/Synergistic-integration-of-metasurfaces-and</link><description><span class="paragraphSection">The convergence of metamaterials and quantum optics heralds a transformative era in photonic technologies, poised to revolutionize applications ranging from information processing and imaging to sensing and beyond. This review explores the synergistic integration of metasurfaces—engineered sub-wavelength planar structures—and quantum optics, which exploits quantum mechanical principles to manipulate light at the most granular level. We outline the design principles, fabrication processes, and computational challenges involved in creating quantum metasurfaces, discussing both forward and inverse design approaches. Advances in nanofabrication and intelligent optimization techniques, such as machine learning and topology optimization, have enabled the development of metasurfaces with unparalleled control over electromagnetic waves. We examine recent progress in using quantum metasurfaces for single-photon and multi-photon generation, quantum imaging, and quantum sensing, showcasing how these innovations achieve unprecedented precision and novel functionalities. Additionally, we highlight the integration of metasurfaces into quantum light manipulation, emphasizing their role in enhancing wavefront shaping and entanglement control. By providing a comprehensive survey of current advancements and future research directions, this review highlights the vast potential of metasurfaces and quantum optics at the crossroads, setting the stage for next-generation technological innovations that will define the forthcoming decade.</span></description><author>Applied Physics Reviews Current Issue</author><pubDate>Mon, 17 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/12/4/041318/3372590/Synergistic-integration-of-metasurfaces-and</guid></item><item><title>[AI for Science - latest papers] Universal machine learning potentials for systems with reduced dimensionality</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae1208</link><description>We present a benchmark designed to evaluate the predictive capabilities of universal machine learning interatomic potentials (MLIPs) across systems of varying dimensionality. Specifically, our benchmark tests zero- (molecules, atomic clusters, etc), one- (nanowires, nanoribbons, nanotubes, etc), two- (atomic layers and slabs) and three-dimensional (3D) (bulk materials) compounds. The benchmark reveals that while all tested models demonstrate excellent performance for 3D systems, accuracy degrades progressively for lower-dimensional structures. The best performing models for geometry optimization are orbital version 2, equiformerV2, and the equivariant Smooth Energy Network, with the equivariant Smooth Energy Network also providing the most accurate energies. Our results indicate that the best models yield, on average, errors in the atomic positions in the range of 0.01–0.02 Å and errors in the energy below 10 meV atom−1 across all dimensionalities. These results demonstrate that state-of-the-art universal MLIPs have reached sufficient accuracy to serve as direct replacements for density functional theory calculations, at a small fraction of the computational cost, in simulations spanning the full range from isolated atoms to bulk solids. More significantly, the best performing models already enable efficient simulations of complex systems containing subsystems of mixed dimensionality, opening new possibilities for modeling realistic materials and interfaces.</description><author>AI for Science - latest papers</author><pubDate>Mon, 17 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae1208</guid></item><item><title>[Cell Reports Physical Science] Conjugated polyelectrolyte-aptamer hybrid for organic-electrochemical-transistor-based sensing</title><link>https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00564-8?rss=yes</link><description>Jiang et al. present biofunctionalized organic mixed ionic-electronic conductors (OMIECs), specifically single-component materials that integrate high specificity with semiconducting properties, exemplified by p(NDI-T-ZI/EG)-aptamer. This hybrid design enables covalent attachment of diverse functional units, thereby expanding the library of sensory OMIECs for future diagnostic applications.</description><author>Cell Reports Physical Science</author><pubDate>Mon, 17 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00564-8?rss=yes</guid></item><item><title>[Cell Reports Physical Science] CatBench framework for benchmarking machine learning interatomic potentials in adsorption energy predictions for heterogeneous catalysis</title><link>https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00567-3?rss=yes</link><description>Moon et al. introduce CatBench, a framework that systematically evaluates machine learning interatomic potentials for predicting molecular adsorption from small to large molecules on catalyst surfaces. Testing 13 state-of-the-art models across ≥47,000 reactions, they identify optimal accuracy-speed trade-offs and provide quantitative guidance for selecting models for practical catalyst discovery.</description><author>Cell Reports Physical Science</author><pubDate>Mon, 17 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00567-3?rss=yes</guid></item><item><title>[Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents] Liquid‐Phase Synthesis of Halide Solid Electrolytes for All‐Solid‐State Batteries Using Organic Solvents</title><link>https://onlinelibrary.wiley.com/doi/10.1002/eem2.70184?af=R</link><description>ENERGY &amp;ENVIRONMENTAL MATERIALS, EarlyView.</description><author>Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents</author><pubDate>Fri, 14 Nov 2025 14:05:17 GMT</pubDate><guid isPermaLink="true">10.1002/eem2.70184</guid></item><item><title>[AI for Science - latest papers] TorchSim: an efficient atomistic simulation engine in PyTorch</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae1799</link><description>We introduce TorchSim, an open-source atomistic simulation engine tailored for the machine learned interatomic potential (MLIP) era. By rewriting core atomistic simulation primitives in PyTorch, TorchSim can achieve orders of magnitude acceleration for popular MLIPs. Unlike existing molecular dynamics (MD) packages, which simulate one system at a time, TorchSim performs batched simulations that efficiently utilize modern GPUs by evolving multiple systems concurrently. TorchSim supports MD integrators, structural relaxation optimizers, both machine-learned and classical interatomic potentials (such as Lennard–Jones, Morse, soft-sphere), batching with automatic memory management, differentiable simulation, and integration with popular materials informatics tools.</description><author>AI for Science - latest papers</author><pubDate>Fri, 14 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae1799</guid></item><item><title>[AI for Science - latest papers] Graph learning metallic glass discovery from Wikipedia</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae1b20</link><description>Synthesizing new materials efficiently is highly demanded in various research fields. However, this process is usually slow and expensive, especially for metallic glasses, whose formation strongly depends on the optimal combinations of multiple elements to resist crystallization. This constraint renders only several thousands of candidates explored in the vast material space since 1960. Recently, data-driven approaches armed by advanced machine learning techniques provided alternative routes for intelligent materials design. Due to data scarcity and immature material encoding, the conventional tabular data is usually mined by statistical learning algorithms, giving limited model predictability and generalizability. Here, we propose sophisticated data learning from material network representations. The node elements are encoded from the Wikipedia by a language model. Graph neural networks with versatile architectures are designed to serve as recommendation systems to explore hidden relationships among materials. By employing Wikipedia embeddings from different languages, we assess the capability of natural languages in materials design. Our study proposes a new paradigm to harvesting new amorphous materials and beyond with artificial intelligence.</description><author>AI for Science - latest papers</author><pubDate>Fri, 14 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae1b20</guid></item><item><title>[ACS Materials Au: Latest Articles (ACS Publications)] [ASAP] Molecular Modeling-Based Machine Learning for Accurate Prediction of Gas Diffusivity and Permeability in Metal–Organic Frameworks</title><link>http://dx.doi.org/10.1021/acsmaterialsau.5c00111</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsmaterialsau.5c00111/asset/images/medium/mg5c00111_0007.gif" /></p><div><cite>ACS Materials Au</cite></div><div>DOI: 10.1021/acsmaterialsau.5c00111</div></description><author>ACS Materials Au: Latest Articles (ACS Publications)</author><pubDate>Wed, 12 Nov 2025 18:15:35 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsmaterialsau.5c00111</guid></item><item><title>[Recent Articles in PRX Energy] Dynamic Vacancy Levels in ${\mathrm{Cs}\mathrm{Pb}\mathrm{Cl}}_{3}$ Obey Equilibrium Defect Thermodynamics</title><link>http://link.aps.org/doi/10.1103/dxmb-8s96</link><description>Author(s): Irea Mosquera-Lois and Aron Walsh<br /><p>This study of halide perovskites uses advanced molecular dynamics simulations with machine learning force fields to identify dynamic defect levels and their impact on the material’s optoelectronic properties.</p><img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/dxmb-8s96.png" width="200" /><br />[PRX Energy 4, 043008] Published Wed Nov 12, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Wed, 12 Nov 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/dxmb-8s96</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Accelerating Primary Screening of USP8 Inhibitors from Drug Repurposing Databases with Tree‐Based Machine Learning</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500108?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Tue, 11 Nov 2025 04:16:54 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500108</guid></item><item><title>[Joule] Entropy-guided discovery of denary trirutile antimonates for electrocatalytic chlorine evolution</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00381-2?rss=yes</link><description>Entropy-guided trirutile antimonates are developed as highly efficient catalysts for the chlorine evolution reaction. By integrating machine learning, DFT calculations, and operando experiments, this work uncovers atomic-level mechanisms governing catalytic activity and stability.</description><author>Joule</author><pubDate>Tue, 11 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00381-2?rss=yes</guid></item><item><title>[AI for Science - latest papers] Surface stability modeling with universal machine learning interatomic potentials: a comprehensive cleavage energy benchmarking study</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae1408</link><description>Machine learning interatomic potentials (MLIPs) have revolutionized computational materials science by bridging the gap between quantum mechanical accuracy and classical simulation efficiency, enabling unprecedented exploration of materials properties across the periodic table. Despite their remarkable success in predicting bulk properties, no systematic evaluation has assessed how well these universal MLIPs (uMLIPs) can predict cleavage energies, a critical property governing fracture, catalysis, surface stability, and interfacial phenomena. Here, we present a comprehensive benchmark of 19 state-of-the-art uMLIPs for cleavage energy prediction using our previously established density functional theory database of 36 718 slab structures spanning elemental, binary, and ternary metallic compounds. We evaluate diverse architectural paradigms, analyzing their performance across chemical compositions, crystal systems, thickness, and surface orientations. Our zero-shot evaluation results reveal that training data composition dominates architectural sophistication: models trained on the Open Materials 2024 (OMat24) dataset, which emphasizes non-equilibrium configurations, achieve mean absolute percentage errors below 6% and correctly identify the thermodynamically most stable surface terminations in 87% of cases, without any explicit surface energy training. In contrast, architecturally identical models trained on equilibrium-only datasets show five-fold higher errors, while models trained on surface-adsorbate data fail catastrophically with a 17-fold degradation. Remarkably, simpler architectures trained on appropriate data achieve comparable accuracy to complex transformers while offering 10–100× computational speedup. These findings fundamentally reframe MLIP development priorities: highlighting that strategic training-data generation with appropriate non-equilibrium sampling deserves equal or greater attention than architectural complexity.</description><author>AI for Science - latest papers</author><pubDate>Fri, 07 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae1408</guid></item><item><title>[Joule] Li–Si compound anodes enabling high-performance all-solid-state Li-ion batteries</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00376-9?rss=yes</link><description>Li–Si compound anodes, exemplified by Li2.33Si, overcome the degradation issues of conventional Si anodes by combining high ionic and electronic conductivity, favorable mechanical properties, and a negligible-volume-change Li-storage mechanism (Li2.33 + αSi, 0 < α < 0.92), thereby enabling high areal capacity, long cycle life, and fast rate capability in all-solid-state Li-ion batteries.</description><author>Joule</author><pubDate>Fri, 07 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00376-9?rss=yes</guid></item><item><title>[ACS Physical Chemistry Au: Latest Articles (ACS Publications)] [ASAP] Machine Learning as a Method for Retrieving Pressure Values by Analyzing Spectral Line Parameters: The Hydrochloric Acid Case</title><link>http://dx.doi.org/10.1021/acsphyschemau.5c00097</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsphyschemau.5c00097/asset/images/medium/pg5c00097_0010.gif" /></p><div><cite>ACS Physical Chemistry Au</cite></div><div>DOI: 10.1021/acsphyschemau.5c00097</div></description><author>ACS Physical Chemistry Au: Latest Articles (ACS Publications)</author><pubDate>Tue, 04 Nov 2025 19:09:10 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsphyschemau.5c00097</guid></item><item><title>[Applied Physics Reviews Current Issue] Dynamic landscape of chemiresistive breathomic nanosensors based on fifth-generation chips for complex disease diagnosis and healthcare monitoring</title><link>https://pubs.aip.org/aip/apr/article/12/4/041311/3371043/Dynamic-landscape-of-chemiresistive-breathomic</link><description><span class="paragraphSection">The growing global population is overwhelming the existing medical infrastructure, demanding a pressing need for the advancement of early-stage and point-of-care disease diagnostics. Conventional techniques are mostly invasive, time-consuming, expensive, sophisticated, and centered at urban facilities. Moreover, they are unable to address the biological complexities related to critical diseases, disorders, and pandemics, resulting in associated high morbidity and mortality. To address this gap, miniaturized fifth-generation sensing chips provide alternatives in terms of accessibility, affordability, and adaptability, being point-of-care and minimally invasive diagnostics. In this context, Breathomic chips based on nanoscale semiconductors have shown their potential for noninvasive, personalized, and on-site operation, offering the capability to identify volatile organic compounds/gases as disease biomarkers from exhaled breath and enabling early disease detection. However, the practical implementation of these sensors in real-time medical contexts remains challenging due to factors including the lack of clinical trials, dedicated data analysis, understanding of the complexities, public awareness, scalability, and accessibility. This comprehensive review critically summarizes the landscape of breath biomarkers detecting fifth-generation chemiresistive chips for human disease diagnosis, methodically outlining associated challenges, alternative strategies, and prospects for clinical implementations and commercial advancement. It details the biological origins of biomarkers, the diverse sensing modalities, and the underlying mechanisms pertaining to breathomic biomarker diagnosis. Furthermore, it highlights the integration of digital-age technologies, including nanotechnology, artificial intelligence, bioinformatics, and machine learning, for high-performance breathomic chips. These next-generation smart sensory chips have the potential to revolutionize medical healthcare facilities, improving patient outcomes, understanding prognosis, and aiding the UN's sustainable development goals.</span></description><author>Applied Physics Reviews Current Issue</author><pubDate>Tue, 04 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/12/4/041311/3371043/Dynamic-landscape-of-chemiresistive-breathomic</guid></item><item><title>[tandf: Materials Research Letters: Table of Contents] Machine learning-assisted design of strong and ductile BCC high-entropy alloys</title><link>https://www.tandfonline.com/doi/full/10.1080/21663831.2025.2577751?af=R</link><description>Volume 13, Issue 12, December 2025, Page 1260-1268<br />. <br /></description><author>tandf: Materials Research Letters: Table of Contents</author><pubDate>Thu, 30 Oct 2025 12:22:23 GMT</pubDate><guid isPermaLink="true">/doi/full/10.1080/21663831.2025.2577751?af=R</guid></item><item><title>[Wiley: InfoMat: Table of Contents] Delicate design of lithium‐ion bridges in hybrid solid electrolyte for wide‐temperature adaptive solid‐state lithium metal batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/inf2.70095?af=R</link><description>InfoMat, EarlyView.</description><author>Wiley: InfoMat: Table of Contents</author><pubDate>Wed, 29 Oct 2025 00:36:10 GMT</pubDate><guid isPermaLink="true">10.1002/inf2.70095</guid></item><item><title>[APL Machine Learning Current Issue] Building an affordable self-driving lab: Practical machine learning experiments for physics education using Internet-of-Things</title><link>https://pubs.aip.org/aip/aml/article/3/4/046105/3369966/Building-an-affordable-self-driving-lab-Practical</link><description><span class="paragraphSection">Machine learning (ML) is transforming modern physics research, but practical, hands-on experience with ML techniques remains limited due to cost and complexity barriers. To address this gap, we introduce an affordable, autonomous, Internet-of-Things (IoT)-enabled experimental platform designed specifically for applied physics education. Utilizing an Arduino microcontroller, a customizable multi-wavelength light emitting diode array, and photosensors, our setup generates diverse, real-time optical datasets ideal for training and evaluating foundational ML algorithms, including traversal methods, Bayesian inference, and deep learning. The platform facilitates a closed-loop, self-driving experimental workflow, encompassing automated data collection, preprocessing, model training, and validation. Through systematic performance comparisons, we demonstrate the superior ability of deep learning to capture complex nonlinear relationships compared to traversal and Bayesian methods. At ∼$60, this open-source IoT platform provides an accessible, practical pathway for students to master advanced ML concepts, promoting deeper conceptual insights and essential technical skills required for the next generation of physicists and engineers.</span></description><author>APL Machine Learning Current Issue</author><pubDate>Wed, 29 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/046105/3369966/Building-an-affordable-self-driving-lab-Practical</guid></item><item><title>[APL Machine Learning Current Issue] Data integration and data fusion approaches in self-driving labs: A perspective</title><link>https://pubs.aip.org/aip/aml/article/3/4/040901/3369947/Data-integration-and-data-fusion-approaches-in</link><description><span class="paragraphSection">Self-driving laboratories (SDLs) are transforming materials discovery by combining automation, machine learning, and real-time feedback. Yet, their success depends on robust data integration and fusion methods capable of handling materials data that are heterogeneous, sparse, and multi-scale. Such data span theoretical models, simulations, and experimental techniques across diverse spatial and temporal scales, creating significant challenges for interoperability and analysis. This perspective reviews the state-of-the-art techniques, including knowledge graphs, structured pipelines, multimodal machine learning, and physics-informed models, that are enabling materials science and SDLs to unify and learn from disparate data sources, identify critical challenges, and propose forward-looking directions to enhance data readiness, interoperability, and predictive power in SDLs. We also highlight emerging methods such as transformer architectures, zero-shot learning, and real-time stream processing, and discuss the critical need for more scalable, interpretable, and adaptive solutions to fully realize autonomous materials innovation. By mapping out both the current landscape and future opportunities, we argue that next-generation data integration and fusion are not just enablers but essential pillars for achieving fully autonomous, adaptive, and intelligent SDL systems capable of addressing the complexities of hierarchical and multifunctional materials.</span></description><author>APL Machine Learning Current Issue</author><pubDate>Wed, 29 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/040901/3369947/Data-integration-and-data-fusion-approaches-in</guid></item><item><title>[Applied Physics Reviews Current Issue] Strain engineering of van Hove singularity and coupled itinerant ferromagnetism in quasi-2D oxide superlattices</title><link>https://pubs.aip.org/aip/apr/article/12/4/041406/3369932/Strain-engineering-of-van-Hove-singularity-and</link><description><span class="paragraphSection">Engineering van Hove singularities (vHss) near the Fermi level, if feasible, offers a powerful route to control exotic quantum phases in electronic and magnetic behaviors. However, conventional approaches rely primarily on chemical and electrical doping and focus mainly on local electrical or optical measurements, limiting their applicability to coupled functionalities. In this study, a vHs-induced insulator-metal transition coupled with a ferromagnetic phase transition was empirically achieved in atomically designed quasi-2D SrRuO<sub>3</sub> (SRO) superlattices via epitaxial strain engineering, which has not been observed in conventional 3D SRO systems. Theoretical calculations revealed that epitaxial strain effectively modulates the strength and energy positions of vHs of specific Ru orbitals, driving correlated phase transitions in the electronic and magnetic ground states. X-ray absorption spectroscopy confirmed the anisotropic electronic structure of quasi-2D SRO modulated by epitaxial strain. Magneto-optic Kerr effect and electrical transport measurements demonstrated modulated magnetic and electronic phases. Furthermore, magneto-electrical measurements detected significant anomalous Hall effect signals and ferromagnetic magnetoresistance, indicating the presence of magnetically coupled charge carriers in the 2D metallic regime. This study establishes strain engineering as a promising platform for tuning vHss and resultant itinerant ferromagnetism of low-dimensional correlated quantum systems.</span></description><author>Applied Physics Reviews Current Issue</author><pubDate>Tue, 28 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/12/4/041406/3369932/Strain-engineering-of-van-Hove-singularity-and</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Machine Learning‐Enhanced Random Matrix Theory Design for Human Immunodeficiency Virus Vaccine Development</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500124?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Mon, 27 Oct 2025 03:21:44 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500124</guid></item><item><title>[Applied Physics Reviews Current Issue] 3D-printed lithium-metal batteries: Multiscale architectures, hybrid technologies, and monolithic integration for next-generation energy storage</title><link>https://pubs.aip.org/aip/apr/article/12/4/041310/3369005/3D-printed-lithium-metal-batteries-Multiscale</link><description><span class="paragraphSection">Lithium-metal anodes, with their unmatched theoretical capacity (3860 mAh g<sup>−1</sup>) and ultra-low electrochemical potential (−3.04 V vs standard hydrogen electrode), are pivotal for next-generation high-energy-density batteries. However, their practical deployment is hindered by persistent challenges—dendritic growth, unstable solid electrolyte interphases (SEIs), and severe volumetric expansion. Emerging as a transformative solution, three-dimensional (3D) printing enables the rational design of multiscale architectures (e.g., micro-lattice anodes and gradient-porous cathodes) and hybrid solid-state electrolytes to address these limitations. This review presents a pioneering synthesis of 3D printing's role in lithium-metal battery engineering, focusing on its capacity to regulate lithium-ion flux, stabilize SEIs, and suppress dendrite proliferation through hierarchical structural control. We systematically analyze four key additive manufacturing technologies (inkjet printing, direct ink writing, fused deposition modeling, and stereolithography), delineating their unique advantages in tailoring ion transport pathways and mechanical robustness. Furthermore, we propose multi-material co-printing strategies to resolve interfacial incompatibilities in monolithic lithium-metal batteries, a critical barrier in current research. By bridging additive manufacturing with electrochemical fundamentals, this work outlines a roadmap to harness 3D printing's full potential, addressing scalability challenges and advancing applications in aerospace, wearables, and biomedical devices where energy density and safety are paramount.</span></description><author>Applied Physics Reviews Current Issue</author><pubDate>Thu, 23 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/12/4/041310/3369005/3D-printed-lithium-metal-batteries-Multiscale</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Harnessing Large Language Models to Advance Microbiome Research: From Sequence Analysis to Clinical Applications</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500038?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Tue, 21 Oct 2025 05:48:44 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500038</guid></item><item><title>[AI for Science - latest papers] AInstein: numerical Einstein metrics via machine learning</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae1117</link><description>A new semi-supervised machine learning package is introduced which successfully solves the Euclidean vacuum Einstein equations with a cosmological constant, without any symmetry assumptions. The model architecture contains subnetworks for each patch in the manifold-defining atlas. Each subnetwork predicts the components of a metric in its associated patch, with the relevant Einstein conditions of the form being used as independent loss components (here , where n is the dimension of the Riemannian manifold, and the Einstein constant ). To ensure the consistency of the global structure of the manifold, another loss component is introduced across the patch subnetworks which enforces the coordinate transformation between the patches, , for an appropriate analytically known Jacobian J. We test our method for the case of spheres represented by a pair of patches in dimensions 2, 3, 4, and 5. In dimensions 2 and 3, the geometries have been fully classified. However, it is unknown whether a Ricci-flat metric can exist on spheres in dimensions 4 and 5. This work hints against the existence of such a metric.</description><author>AI for Science - latest papers</author><pubDate>Thu, 16 Oct 2025 23:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae1117</guid></item><item><title>[Matter] SpectroGen: A physically informed generative artificial intelligence for accelerated cross-modality spectroscopic materials characterization</title><link>https://www.cell.com/matter/fulltext/S2590-2385(25)00477-1?rss=yes</link><description>SpectroGen seamlessly couples physics-driven distribution models with a variable autoencoder to generate synthetic spectra indistinguishable from real data. By speeding up high-throughput screening, it closes the gap between AI-based materials discovery and experimental confirmation. Its flexible architecture accommodates diverse spectroscopic techniques, extending its utility across multiple scientific domains. The synergy of rapid AI-driven design and swift AI-enabled characterization expedites validation of innovative materials, bridging lab-based discovery and industry-ready applications to address urgent societal needs.</description><author>Matter</author><pubDate>Tue, 14 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/matter/fulltext/S2590-2385(25)00477-1?rss=yes</guid></item><item><title>[Chem] Precisely modulating Li2CO3 coverage on Ni-rich cathode boosts sulfide solid-state lithium battery performance</title><link>https://www.cell.com/chem/fulltext/S2451-9294(25)00366-3?rss=yes</link><description>It is of great importance to address the issues of stability and charge transfer at the cathode/electrolyte interface in all-solid-state lithium batteries (ASSLBs). We proposed a CO2 atmosphere treatment to precisely modulate Li2CO3 coverage on Ni-rich layered oxide cathodes (NRLOs) with minimal damage, effectively utilizing the advantageous effect while avoiding the harmful effect of surficial Li2CO3 on the interface. This fundamental mechanism offers insights for optimizing the NRLO/sulfide solid electrolyte interface and advancing high-energy-density ASSLBs.</description><author>Chem</author><pubDate>Tue, 14 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/chem/fulltext/S2451-9294(25)00366-3?rss=yes</guid></item><item><title>[Matter] Dynamic pressure mapping of infant cervical spines using a wearable magnetoelastic patch</title><link>https://www.cell.com/matter/fulltext/S2590-2385(25)00529-6?rss=yes</link><description>To enable continuous monitoring of infant cervical motion, we present a kirigami-inspired soft magnetoelastic patch that conforms intimately to the cervical position without compromising comfort. By leveraging passive magnetic sensing and flexible structural design, the patch captures subtle biomechanical changes during cervical movements. Integrated with machine learning classification, it enables intelligent recognition of stress patterns, providing a non-invasive and adaptive solution for early assessment of infant cervical motion.</description><author>Matter</author><pubDate>Fri, 10 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/matter/fulltext/S2590-2385(25)00529-6?rss=yes</guid></item><item><title>[Matter] Machine learning-driven ligand engineering decodes and controls structural distortions in 2D perovskites</title><link>https://www.cell.com/matter/fulltext/S2590-2385(25)00525-9?rss=yes</link><description>This work deciphers how ligand molecular descriptors (i.e., nitrogen content, hydrogen bonding, and π-conjugation) govern structural distortions and optoelectronic properties in 2D perovskites. The authors demonstrate that machine learning can quantitatively correlate these descriptors with octahedral distortions (92.6% prediction accuracy) and enable the targeted synthesis of six new perovskites with tunable band gaps (1.91–2.39 eV). The established structure-property relationships and machine learning-driven design paradigm represent a transformative approach for accelerating the discovery of functional perovskites, bridging computational prediction with experimental validation for optoelectronic applications.</description><author>Matter</author><pubDate>Fri, 10 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/matter/fulltext/S2590-2385(25)00525-9?rss=yes</guid></item><item><title>[Applied Physics Reviews Current Issue] The enduring legacy of scanning spreading resistance microscopy: Overview, advancements, and future directions</title><link>https://pubs.aip.org/aip/apr/article/12/4/041305/3366934/The-enduring-legacy-of-scanning-spreading</link><description><span class="paragraphSection">Scanning spreading resistance microscopy (SSRM) has recently celebrated 30 years of existence when counting from the original patent of 1994. In this time, the technique has experienced an incredible journey with substantial evolutions that transformed SSRM from a small-scale experiment into a staple for chip manufacturing laboratories for physical analysis of materials, failure analysis, and process development of integrated circuits. As the nanoelectronics industry is ready for a new inflection point, with the introduction of nanosheet field-effect transistor to replace FinFETs and cell track scaling architectures such as the complementary field-effect transistors, SSRM is once again at a turning point. This review aims to highlight the state-of-the-art while discussing the emerging challenges introduced by the ever-increasing complexity in complementary metal–oxide–semiconductor (CMOS) manufacturing. We start by illustrating the unique capability of the SSRM technique, its origin, and its evolution. Next, we continue by showing the considerable research effort that enabled SSRM to transition to a tomographic sensing method in support of FinFET transistors. Here, the high aspect ratio fin geometry and the complex contacts technology have imposed important modifications to the original method. Later, we elaborate on some of the key challenges introduced by the upcoming device transition from three-sided channel FinFETs into nanosheet FETs, i.e., offering a four-sided electrostatic control of the channel. Finally, we present the use of machine learning for automation in carrier calibration with increased accuracy. We close by introducing some of the concepts that we consider promising for further extension of SSRM to obtain sub-nm structural information and doping profiles in the area of advanced FinFETs and nanosheet FET technologies, including (a) correlative analysis flow, (b) liquid-assisted probing, and (c) top–down and bottom–up multi-probe sensing schemes to merge low- and high-pressure SSRM scans.</span></description><author>Applied Physics Reviews Current Issue</author><pubDate>Wed, 08 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/12/4/041305/3366934/The-enduring-legacy-of-scanning-spreading</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Oxygen Vacancy-Modulated Molybdenum Oxide/Montmorillonite Heterointerface Nanoarchitectures for Plasmon-Enhanced Solar-Osmotic Energy Harvesting</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202506132?af=R</link><description><p><img src="https://www.chinesechemsoc.org/cms/asset/762823f9-fd0a-4aff-acde-289220f569a8/keyimage.png" /></p><div><cite>CCS Chemistry, Ahead of Print.</cite></div><div>DOI:10.31635/ccschem.025.202506132</div>Two-dimensional (2D) nanofluidic membranes with precise ion transport kinetics hold transformative potential for next-generation ion-selective membranes, yet their practical implementation in osmotic energy conversion remains constrained by inherent ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Mon, 06 Oct 2025 03:22:16 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202506132?af=R</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Oxygen Vacancy-Modulated Molybdenum Oxide/Montmorillonite Heterointerface Nanoarchitectures for Plasmon-Enhanced Solar-Osmotic Energy Harvesting</title><link>https://ccs-stag.literatumonline.com.localhost.literatumonline.com:25116/doi/abs/10.31635/ccschem.025.202506132?af=R</link><description><p><img src="https://www.chinesechemsoc.org/cms/asset/762823f9-fd0a-4aff-acde-289220f569a8/keyimage.png" /></p><div><cite>CCS Chemistry, Ahead of Print.</cite></div><div>DOI:10.31635/ccschem.025.202506132</div>Two-dimensional (2D) nanofluidic membranes with precise ion transport kinetics hold transformative potential for next-generation ion-selective membranes, yet their practical implementation in osmotic energy conversion remains constrained by inherent ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Mon, 06 Oct 2025 03:22:16 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com.localhost.literatumonline.com:25116/doi/abs/10.31635/ccschem.025.202506132?af=R</guid></item><item><title>[APL Machine Learning Current Issue] Deep learning model of myofilament cooperative activation and cross-bridge cycling in cardiac muscle</title><link>https://pubs.aip.org/aip/aml/article/3/4/046101/3365811/Deep-learning-model-of-myofilament-cooperative</link><description><span class="paragraphSection">Cardiac muscle contraction is driven by the cross-bridge cycle, where myosin heads generate force by cyclically attaching to and pulling on actin filaments using energy from ATP. Modeling this process is central to understanding cardiac sarcomere mechanics. In this study, we developed supervised machine learning (ML) models using artificial neural networks (ANNs) to simulate cross-bridge cycling and muscle behavior under isosarcometric, isometric, and isotonic conditions. Trained on synthetic data, the ANN captured nonlinear dependencies among calcium concentration, stiffness, sarcomere length, temperature, and force output. Error analysis through histograms and unity-line scatterplots validated prediction accuracy and identified underfitting and overfitting patterns. Comparisons across ANN architectures showed how hidden layer complexity affects model generalization. The present deep learning models accurately reproduced key physiological behaviors, including steady-state force–Ca<sup>2+</sup> relations, sarcomere length changes, and force–velocity relations, and matched theoretical results. This work demonstrates the potential of ML tools to enhance cardiac muscle modeling and exploit existing experimental datasets for improved prediction of cardiac muscle diseases.</span></description><author>APL Machine Learning Current Issue</author><pubDate>Fri, 03 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/046101/3365811/Deep-learning-model-of-myofilament-cooperative</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Discovery of Novel Materials with Giant Dielectric Constants via First‐Principles Phonon Calculations and Machine Learning</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500141?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Tue, 30 Sep 2025 06:30:24 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500141</guid></item><item><title>[AI for Science - latest papers] FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae0808</link><description>We introduce a rapid, accurate framework for computing atomic migration barriers in crystals by combining universal machine‐learning force fields (MLFFs) with 3D potential‐energy‐surface sampling and interpolation. Our method suppresses periodic self‐interactions via supercell expansion, builds a continuous potential energy surface (PES) from MLFF energies on a spatial grid, and extracts minimum‐energy pathways without predefined nudged elastic band (NEB) images. For a benchmark set of twelve electrode and electrolyte materials, including LiCoO2, LiFePO4, and Li10GeP2S12, our MLFF‐derived barriers lie within tens of meV of density functional theory (DFT) and experiment values, while achieving a ∼100-fold speedup over standard DFT‐NEB calculations. We benchmark GPTFF, CHGNet, and MACE, showing that fine‐tuning on PBE/PBE + U data further enhances accuracy. Ultimately, we introduce an open‐source package for high‐throughput materials screening and interactive PES visualization.</description><author>AI for Science - latest papers</author><pubDate>Mon, 29 Sep 2025 23:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae0808</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500060?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Wed, 24 Sep 2025 13:21:08 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500060</guid></item><item><title>[Recent Articles in PRX Energy] Reconstructions and Dynamics of $β$-Lithium Thiophosphate Surfaces</title><link>http://link.aps.org/doi/10.1103/5hf9-hlj6</link><description>Author(s): Hanna Türk, Davide Tisi, and Michele Ceriotti<br /><p>Machine-learning-based molecular dynamics simulations of the solid electrolyte <math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><mi>β</mi></math>-Li<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msub><mrow></mrow><mn>3</mn></msub></math>PS<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msub><mrow></mrow><mn>4</mn></msub></math> under realistic conditions reveal dynamic surface structure and reactivity.</p><img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/5hf9-hlj6.png" width="200" /><br />[PRX Energy 4, 033010] Published Tue Aug 26, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Tue, 26 Aug 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/5hf9-hlj6</guid></item><item><title>[Matter] CGformer: Transformer-enhanced crystal graph network with global attention for material property prediction</title><link>https://www.cell.com/matter/fulltext/S2590-2385(25)00423-0?rss=yes</link><description>Designing new materials for better batteries is a major challenge, especially for complex “high-entropy” materials with countless atomic combinations. We developed a novel AI model, CGformer, that looks at the entire crystal structure to accurately predict material properties. By screening nearly 150,000 candidates, our AI identified promising new sodium-ion solid electrolytes. We successfully synthesized these materials, and they showed excellent performance, validating our AI-driven discovery pipeline. This work provides a powerful tool to accelerate the design of next-generation energy materials.</description><author>Matter</author><pubDate>Wed, 20 Aug 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/matter/fulltext/S2590-2385(25)00423-0?rss=yes</guid></item><item><title>[Recent Articles in PRX Energy] Large-Scale Simulation Unveiled Superior Potassium-Based Solid Electrolyte with High Ionic Conductivity and Excellent Electrochemical Stability in ${M}_{5}{\mathrm{YSi}}_{4}{\mathrm{O}}_{12}$ ($M=\mathrm{Li},\mathrm{K}$)</title><link>http://link.aps.org/doi/10.1103/8wkh-238p</link><description>Author(s): Zhao Li, Jiaxiang Li, Congwei Xie, Keith Butler, Fei Du, and Yu Xie<br /><p>Advanced computational modeling predicts the ionic conductivity and electrochemical stability of a promising potassium-based solid electrolyte. The approach highlights the importance of longer length and time scales during simulations, achievable with machine learning potentials.</p><img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/8wkh-238p.png" width="200" /><br />[PRX Energy 4, 033007] Published Thu Aug 14, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Thu, 14 Aug 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/8wkh-238p</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Interpretable Machine Learning for Solvent‐Dependent Carrier Mobility in Solution‐Processed Organic Thin Films</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500078?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Fri, 08 Aug 2025 09:54:45 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500078</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Macrophage Phenotype Detection Methodology on Textured Surfaces via Nuclear Morphology Using Machine Learning</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500055?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Fri, 01 Aug 2025 08:40:28 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500055</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500079?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Thu, 24 Jul 2025 10:45:19 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500079</guid></item><item><title>[Recent Articles in PRX Energy] Origin of Intrinsically Low Thermal Conductivity in a Garnet-Type Solid Electrolyte: Linking Lattice and Ionic Dynamics with Thermal Transport</title><link>http://link.aps.org/doi/10.1103/6wj2-kzhh</link><description>Author(s): Yitian Wang, Yaokun Su, Jesús Carrete, Huanyu Zhang, Nan Wu, Yutao Li, Hongze Li, Jiaming He, Youming Xu, Shucheng Guo, Qingan Cai, Douglas L. Abernathy, Travis Williams, Kostiantyn V. Kravchyk, Maksym V. Kovalenko, Georg K.H. Madsen, Chen Li, and Xi Chen<br /><p>Using a comprehensive experimental and computational approach, this work analyzes the intrinsically low thermal conductivity of solid ionic conductor Li<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msub><mrow></mrow><mrow><mn>6</mn><mo lspace="0" rspace="0">.</mo><mn>5</mn></mrow></msub></math>La<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msub><mrow></mrow><mn>3</mn></msub></math>Zr<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msub><mrow></mrow><mrow><mn>1</mn><mo lspace="0" rspace="0">.</mo><mn>5</mn></mrow></msub></math>Ta<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msub><mrow></mrow><mrow><mn>0</mn><mo lspace="0" rspace="0">.</mo><mn>5</mn></mrow></msub></math>O<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msub><mrow></mrow><mn>12</mn></msub></math>, a promising electrolyte for all-solid-state batteries.</p><img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/6wj2-kzhh.png" width="200" /><br />[PRX Energy 4, 033004] Published Thu Jul 17, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Thu, 17 Jul 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/6wj2-kzhh</guid></item><item><title>[Recent Articles in PRX Energy] A Comparative Study of Solid Electrolyte Interphase Evolution in Ether and Ester-Based Electrolytes for $\mathrm{Na}$-ion Batteries</title><link>http://link.aps.org/doi/10.1103/jfvb-wp5w</link><description>Author(s): Liang Zhao, Sara I.R. Costa, Yue Chen, Jack R. Fitzpatrick, Andrew J. Naylor, Oleg Kolosov, and Nuria Tapia-Ruiz<br /><p>Diglyme-based electrolytes promote a thin, uniform, and stable solid electrolyte interphase that can extend the lifespan of sodium-ion batteries, as shown using advanced spectroscopic and electrochemical techniques.</p><img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/jfvb-wp5w.png" width="200" /><br />[PRX Energy 4, 033002] Published Tue Jul 15, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Tue, 15 Jul 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/jfvb-wp5w</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Autonomous Machine Learning‐Based Classification and Arrangement of Submillimeter Objects Using a Capillary Force Gripper</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500068?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Wed, 09 Jul 2025 08:01:30 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500068</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500031?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Wed, 09 Jul 2025 07:56:18 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500031</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500074?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Fri, 27 Jun 2025 08:27:19 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500074</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500022?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Fri, 27 Jun 2025 08:15:35 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500022</guid></item><item><title>[ACS Materials Au: Latest Articles (ACS Publications)] [ASAP] Historical Data Mining Deep Dive into Machine Learning-Aided 2D Materials Research in Electrochemical Applications</title><link>http://dx.doi.org/10.1021/acsmaterialsau.5c00030</link><description><p><img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsmaterialsau.5c00030/asset/images/medium/mg5c00030_0020.gif" /></p><div><cite>ACS Materials Au</cite></div><div>DOI: 10.1021/acsmaterialsau.5c00030</div></description><author>ACS Materials Au: Latest Articles (ACS Publications)</author><pubDate>Mon, 23 Jun 2025 15:22:16 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsmaterialsau.5c00030</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Machine Learning‐Assisted Infectious Disease Detection in Low‐Income Areas: Toward Rapid Triage of Dengue and Zika Virus Using Open‐Source Hardware</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500049?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Mon, 23 Jun 2025 08:20:28 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500049</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500033?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Fri, 20 Jun 2025 08:36:19 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500033</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Predicting High‐Resolution Spatial and Spectral Features in Mass Spectrometry Imaging with Machine Learning and Multimodal Data Fusion</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500021?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Wed, 18 Jun 2025 08:10:58 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500021</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Efficient Pure Organic Near-Infrared Room-Temperature Phosphorescence Based on n/π Orbital Decoupling</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202405319?af=R</link><description><p><img src="https://www.chinesechemsoc.org/cms/asset/91960c98-a47a-4293-846c-f1e9b670d740/keyimage.jpg" /></p><div><cite>CCS Chemistry, Ahead of Print.</cite></div><div>DOI:10.31635/ccschem.025.202405319</div>Developing pure organic room-temperature phosphorescence (RTP) materials remains an enormous challenge, especially for efficient near-infrared (NIR) RTP materials. Herein, a functional unit combination strategy is employed to design a series of pure ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Sat, 14 Jun 2025 05:08:51 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202405319?af=R</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Efficient Pure Organic Near-Infrared Room-Temperature Phosphorescence Based on n/π Orbital Decoupling</title><link>https://ccs-stag.literatumonline.com.localhost.literatumonline.com:25116/doi/abs/10.31635/ccschem.025.202405319?af=R</link><description><p><img src="https://www.chinesechemsoc.org/cms/asset/91960c98-a47a-4293-846c-f1e9b670d740/keyimage.jpg" /></p><div><cite>CCS Chemistry, Ahead of Print.</cite></div><div>DOI:10.31635/ccschem.025.202405319</div>Developing pure organic room-temperature phosphorescence (RTP) materials remains an enormous challenge, especially for efficient near-infrared (NIR) RTP materials. Herein, a functional unit combination strategy is employed to design a series of pure ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Sat, 14 Jun 2025 05:08:51 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com.localhost.literatumonline.com:25116/doi/abs/10.31635/ccschem.025.202405319?af=R</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Identification of Cu-N2 Sites for Zn-Air Batteries in Harsh Electrolytes: Computer Virtual Screening, Machine Learning, and Practical Application</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505577?af=R</link><description><p><img src="https://www.chinesechemsoc.org/cms/asset/48f68dbe-4a5c-4ae6-a581-ca6b1854a4fe/keyimage.jpg" /></p><div><cite>CCS Chemistry, Ahead of Print.</cite></div><div>DOI:10.31635/ccschem.025.202505577</div>Nonprecious-metal catalysts possess great potential to replace noble metals in catalysis; however, selecting efficient candidates through experiments is time-consuming and costly. Herein, we employed a data-driven virtual screening (VS) method to discover ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Sat, 14 Jun 2025 04:39:17 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505577?af=R</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Identification of Cu-N2 Sites for Zn-Air Batteries in Harsh Electrolytes: Computer Virtual Screening, Machine Learning, and Practical Application</title><link>https://ccs-stag.literatumonline.com.localhost.literatumonline.com:25116/doi/abs/10.31635/ccschem.025.202505577?af=R</link><description><p><img src="https://www.chinesechemsoc.org/cms/asset/48f68dbe-4a5c-4ae6-a581-ca6b1854a4fe/keyimage.jpg" /></p><div><cite>CCS Chemistry, Ahead of Print.</cite></div><div>DOI:10.31635/ccschem.025.202505577</div>Nonprecious-metal catalysts possess great potential to replace noble metals in catalysis; however, selecting efficient candidates through experiments is time-consuming and costly. Herein, we employed a data-driven virtual screening (VS) method to discover ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Sat, 14 Jun 2025 04:39:17 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com.localhost.literatumonline.com:25116/doi/abs/10.31635/ccschem.025.202505577?af=R</guid></item><item><title>[Recent Articles in PRX Energy] Correlating Local Morphology and Charge Dynamics via Kelvin Probe Force Microscopy to Explain Photoelectrode Performance</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.023010</link><description>Author(s): Maryam Pourmahdavi, Mauricio Schieda, Ragle Raudsepp, Steffen Fengler, Jiri Kollmann, Yvonne Pieper, Thomas Dittrich, Thomas Klassen, and Francesca M. Toma<br /><p>Charge transport in photoelectrodes for photoelectrochemical cells is influenced by microstructural variations; here, the authors use Kelvin Probe Force Microscopy to correlate local morphology with optoelectronic properties toward optimizing materials toward material optimization.</p><img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.023010.png" width="200" /><br />[PRX Energy 4, 023010] Published Mon Jun 09, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Mon, 09 Jun 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.023010</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Minimal Sulfur-Grafted Graphite Anode with Accelerated Interfacial Kinetics for Fast-Charging Lithium-Ion Batteries</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505705?af=R</link><description><p><img src="https://www.chinesechemsoc.org/cms/asset/3b36947a-a194-4971-8441-76ab0db62773/keyimage.png" /></p><div><cite>CCS Chemistry, Ahead of Print.</cite></div><div>DOI:10.31635/ccschem.025.202505705</div>The significant capacity decay and undesired metallic Li plating of graphite anode resulting from sluggish Li-ion diffusion kinetics at the graphite/electrolyte interface have largely hindered the fast-charging capability of lithium-ion batteries (LIBs). ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Wed, 28 May 2025 08:32:07 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505705?af=R</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Minimal Sulfur-Grafted Graphite Anode with Accelerated Interfacial Kinetics for Fast-Charging Lithium-Ion Batteries</title><link>https://ccs-stag.literatumonline.com.localhost.literatumonline.com:25116/doi/abs/10.31635/ccschem.025.202505705?af=R</link><description><p><img src="https://www.chinesechemsoc.org/cms/asset/3b36947a-a194-4971-8441-76ab0db62773/keyimage.png" /></p><div><cite>CCS Chemistry, Ahead of Print.</cite></div><div>DOI:10.31635/ccschem.025.202505705</div>The significant capacity decay and undesired metallic Li plating of graphite anode resulting from sluggish Li-ion diffusion kinetics at the graphite/electrolyte interface have largely hindered the fast-charging capability of lithium-ion batteries (LIBs). ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Wed, 28 May 2025 08:32:07 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com.localhost.literatumonline.com:25116/doi/abs/10.31635/ccschem.025.202505705?af=R</guid></item><item><title>[Recent Articles in PRX Energy] Resolving the Solvation Structure and Transport Properties of Aqueous Zinc Electrolytes from Salt-in-Water to Water-in-Salt Using Neural Network Potential</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.023004</link><description>Author(s): Chuntian Cao, Arun Kingan, Ryan C. Hill, Jason Kuang, Lei Wang, Chunyi Zhang, Matthew R. Carbone, Hubertus van Dam, Shinjae Yoo, Shan Yan, Esther S. Takeuchi, Kenneth J. Takeuchi, Xifan Wu, AM Milinda Abeykoon, Amy C. Marschilok, and Deyu Lu<br /><p>A neural network potential model is developed for ZnCl<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msub><mrow></mrow><mn>2</mn></msub></math> electrolytes that provides atomic scale insights into the solvation structure and ionic conductivity. The results agree well with experiment and shed light on the performance of aqueous zinc-ion batteries across a wide concentration range of ZnCl<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><msub><mrow></mrow><mn>2</mn></msub></math> electrolytes.</p><img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.023004.png" width="200" /><br />[PRX Energy 4, 023004] Published Fri Apr 11, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Fri, 11 Apr 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.023004</guid></item><item><title>[Recent Articles in PRX Energy] Crystal Structure Prediction and Phase Stability in Highly Anharmonic Silver-Based Chalcohalide Antiperovskites</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.023002</link><description>Author(s): Pol Benítez, Cibrán López, Cong Liu, Ivan Caño, Josep-Lluís Tamarit, Edgardo Saucedo, and Claudio Cazorla<br /><p>Using theoretical first-principles methods, stable and metastable phases of silver-based chalcohalide anti-perovskites are predicted, offering insight into their stability for potential energy and optoelectronic applications.</p><img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.023002.png" width="200" /><br />[PRX Energy 4, 023002] Published Thu Apr 03, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Thu, 03 Apr 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.023002</guid></item><item><title>[Recent Articles in PRX Energy] Unraveling Temperature-Induced Vacancy Clustering in Tungsten: From Direct Microscopy to Atomistic Insights via Data-Driven Bayesian Sampling</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.013008</link><description>Author(s): Anruo Zhong, Clovis Lapointe, Alexandra M. Goryaeva, Kazuto Arakawa, Manuel Athènes, and Mihai-Cosmin Marinica<br /><p>This study reveals how anharmonic, entropy-driven stabilization of di-vacancies at elevated temperatures reconcile theoretical predictions with experimental observations of vacancy clustering in tungsten, a prime candidate material for fusion reactors.</p><img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.013008.png" width="200" /><br />[PRX Energy 4, 013008] Published Tue Feb 25, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Tue, 25 Feb 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.013008</guid></item><item><title>[Recent Articles in PRX Energy] Constant-Current Nonequilibrium Molecular Dynamics Approach for Accelerated Computation of Ionic Conductivity Including Ion-Ion Correlation</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.013005</link><description>Author(s): Ryoma Sasaki, Yoshitaka Tateyama, and Debra J. Searles<br /><p>A new “constant-current” nonequilibrium molecular dynamics simulation method accelerates ionic conductivity calculations by up to two orders of magnitude while accurately capturing ion-ion correlations, enabling more efficient screening of solid electrolytes and revealing important low-temperature conduction behaviors.</p><img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.013005.png" width="200" /><br />[PRX Energy 4, 013005] Published Wed Feb 19, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Wed, 19 Feb 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.013005</guid></item><item><title>[Recent Articles in PRX Energy] Detecting Attacks and Estimating States of Power Grids from Partial Observations with Machine Learning</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.013003</link><description>Author(s): Zheng-Meng Zhai, Mohammadamin Moradi, and Ying-Cheng Lai<br /><p>Using data-driven machine learning models, this work demonstrates a method to map the dynamical state of a full power grid from limited observations, enabling the user to locate disruptions with information only from other areas of the grid.</p><img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.013003.png" width="200" /><br />[PRX Energy 4, 013003] Published Tue Feb 04, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Tue, 04 Feb 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.013003</guid></item><item><title>[Recent Articles in PRX Energy] 3D Reconstruction of a Nuclear Reactor by Muon Tomography: Structure Validation and Anomaly Detection</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.013002</link><description>Author(s): Baptiste Lefevre, Julien Vogel, Héctor Gomez, David Attié, Laurent Gallego, Philippe Gonzales, Bertrand Lesage, Philippe Mas, and Daniel Pomarède<br /><p>Muon tomography, a non-invasive technique that can be used to image large, inaccessible structures, is combined with machine learning to create a 3D reconstruction of a historical nuclear reactor that reveals material density variations, including potential anomalies in the graphite core.</p><img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.013002.png" width="200" /><br />[PRX Energy 4, 013002] Published Tue Jan 28, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Tue, 28 Jan 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.013002</guid></item><item><title>[Recent Articles in PRX Energy] Determining Parameters of Metal-Halide Perovskites Using Photoluminescence with Bayesian Inference</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.013001</link><description>Author(s): Manuel Kober-Czerny, Akash Dasgupta, Seongrok Seo, Florine M. Rombach, David P. McMeekin, Heon Jin, and Henry J. Snaith<br /><p>Experiment and machine learning are combined to extract key material parameters and insight into charge carrier transport in metal halide perovskites for solar cell applications.</p><img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.013001.png" width="200" /><br />[PRX Energy 4, 013001] Published Tue Jan 14, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Tue, 14 Jan 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.013001</guid></item><item><title>[Recent Articles in PRX Energy] Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.3.023006</link><description>Author(s): Hengrui Zhang (张恒睿), Tianxing Lai (来天行), Jie Chen, Arumugam Manthiram, James M. Rondinelli, and Wei Chen<br /><p>MolSets, a machine learning model that integrates graph neural networks with permutation invariant architecture, addresses multilevel complexity for effective prediction of molecular mixture properties, thus accelerating lithium battery electrolyte design.</p><img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.3.023006.png" width="200" /><br />[PRX Energy 3, 023006] Published Wed Jun 12, 2024</description><author>Recent Articles in PRX Energy</author><pubDate>Wed, 12 Jun 2024 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.3.023006</guid></item><item><title>[Recent Articles in PRX Energy] Temperature Impact on Lithium Metal Morphology in Lithium Reservoir-Free Solid-State Batteries</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.3.023003</link><description>Author(s): Min-Gi Jeong, Kelsey B. Hatzell, Sourim Banerjee, Bairav S. Vishnugopi, and Partha P. Mukherjee<br /><p>Toward enhancing performance in reservoir-free solid-state batteries, confocal imaging experiments are combined with meso-scale modeling to unveil vertical and horizontal growth mechanisms at varying temperatures of lithium metal at an agyrodite solid electrolyte|stainless steel interface.</p><img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.3.023003.png" width="200" /><br />[PRX Energy 3, 023003] Published Fri May 17, 2024</description><author>Recent Articles in PRX Energy</author><pubDate>Fri, 17 May 2024 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.3.023003</guid></item><item><title>[Recent Articles in Rev. Mod. Phys.] <i>Colloquium</i>: Advances in automation of quantum dot devices control</title><link>http://link.aps.org/doi/10.1103/RevModPhys.95.011006</link><description>Author(s): Justyna P. Zwolak and Jacob M. Taylor<br /><p>A promising platform for quantum computing consists of arrays of quantum dots. However, operating these devices presents a challenging control problem, since the location of the dots and the charges they contain must be reliably and reproducibly matched with the gate voltages. This Colloquium explains how automated control protocols that make use of machine learning techniques can succeed in systems where heuristic control is not feasible.</p><img height="" src="https://cdn.journals.aps.org/journals/RMP/key_images/10.1103/RevModPhys.95.011006.png" width="200" /><br />[Rev. Mod. Phys. 95, 011006] Published Fri Feb 17, 2023</description><author>Recent Articles in Rev. Mod. Phys.</author><pubDate>Fri, 17 Feb 2023 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/RevModPhys.95.011006</guid></item><item><title>[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Hydrogen as promoter and inhibitor of superionicity: A case study on Li-N-H systems</title><link>http://link.aps.org/doi/10.1103/PhysRevB.82.024304</link><description>Author(s): Andreas Blomqvist, C. Moysés Araújo, Ralph H. Scheicher, Pornjuk Srepusharawoot, Wen Li, Ping Chen, and Rajeev Ahuja<br /><p>Materials which possess a high lithium ion conductivity are very attractive for battery and fuel cell applications. Hydrogenation of the fast-ion conductor lithium nitride $({\text{Li}}_{3}\text{N})$ leads to the formation of lithium imide $({\text{Li}}_{2}\text{NH})$ and subsequently of lithium ami…</p><br />[Phys. Rev. B 82, 024304] Published Mon Jul 26, 2010</description><author>PRB: Dynamics, dynamical systems, lattice effects, quantum solids</author><pubDate>Mon, 26 Jul 2010 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PhysRevB.82.024304</guid></item><item><title>[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Nonadiabatic effects of rattling phonons and $4f$ excitations in $\text{Pr}{({\text{Os}}_{1−x}{\text{Ru}}_{x})}_{4}{\text{Sb}}_{12}$</title><link>http://link.aps.org/doi/10.1103/PhysRevB.81.224305</link><description>Author(s): Peter Thalmeier<br /><p>In the skutterudite compounds the anharmonic “rattling” oscillations of $4f$-guest ions in the surrounding ${\text{Sb}}_{12}$ host cages are found to have significant influence on the low-temperature properties. Recently specific-heat analysis of $\text{Pr}{({\text{Os}}_{1−x}{\text{Ru}}_{x})}_{4}{\t…</p><br />[Phys. Rev. B 81, 224305] Published Fri Jun 18, 2010</description><author>PRB: Dynamics, dynamical systems, lattice effects, quantum solids</author><pubDate>Fri, 18 Jun 2010 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PhysRevB.81.224305</guid></item><item><title>[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Ionic conductivity of nanocrystalline yttria-stabilized zirconia: Grain boundary and size effects</title><link>http://link.aps.org/doi/10.1103/PhysRevB.81.184301</link><description>Author(s): O. J. Durá, M. A. López de la Torre, L. Vázquez, J. Chaboy, R. Boada, A. Rivera-Calzada, J. Santamaria, and C. Leon<br /><p>We report on the effect of grain size on the ionic conductivity of yttria-stabilized zirconia samples synthesized by ball milling. Complex impedance measurements, as a function of temperature and frequency are performed on $10\text{ }\text{mol}\text{ }\mathrm{%}$ yttria-stabilized zirconia nanocryst…</p><br />[Phys. Rev. B 81, 184301] Published Mon May 10, 2010</description><author>PRB: Dynamics, dynamical systems, lattice effects, quantum solids</author><pubDate>Mon, 10 May 2010 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PhysRevB.81.184301</guid></item><item><title>[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Calculating the anharmonic free energy from first principles</title><link>http://link.aps.org/doi/10.1103/PhysRevB.81.172301</link><description>Author(s): Zhongqing Wu<br /><p>We developed a method to calculate the anharmonic free energy without requiring any adjustable parameter. The requisite computations are first-principles quasiharmonic calculations plus an additional Canonical (NVT) ensemble first-principles molecular-dynamics simulation and, therefore, are affordab…</p><br />[Phys. Rev. B 81, 172301] Published Fri May 07, 2010</description><author>PRB: Dynamics, dynamical systems, lattice effects, quantum solids</author><pubDate>Fri, 07 May 2010 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PhysRevB.81.172301</guid></item><item><title>[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Phason dynamics in one-dimensional lattices</title><link>http://link.aps.org/doi/10.1103/PhysRevB.81.064302</link><description>Author(s): Hansjörg Lipp, Michael Engel, Steffen Sonntag, and Hans-Rainer Trebin<br /><p>In quasicrystals, the phason degree of freedom and the inherent anharmonic potentials lead to complex dynamics, which cannot be described by the usual phonon modes of motion. We have constructed simple one-dimensional model systems, the dynamic Fibonacci chain, and approximants thereof. They allow u…</p><br />[Phys. Rev. B 81, 064302] Published Thu Feb 25, 2010</description><author>PRB: Dynamics, dynamical systems, lattice effects, quantum solids</author><pubDate>Thu, 25 Feb 2010 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PhysRevB.81.064302</guid></item><item><title>[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] <i>Ab initio</i> construction of interatomic potentials for uranium dioxide across all interatomic distances</title><link>http://link.aps.org/doi/10.1103/PhysRevB.80.174302</link><description>Author(s): P. Tiwary, A. van de Walle, and N. Grønbech-Jensen<br /><p>We provide a methodology for generating interatomic potentials for use in classical molecular-dynamics simulations of atomistic phenomena occurring at energy scales ranging from lattice vibrations to crystal defects to high-energy collisions. A rigorous method to objectively determine the shape of a…</p><br />[Phys. Rev. B 80, 174302] Published Wed Nov 25, 2009</description><author>PRB: Dynamics, dynamical systems, lattice effects, quantum solids</author><pubDate>Wed, 25 Nov 2009 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PhysRevB.80.174302</guid></item><item><title>[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] One-dimensional nanostructure-guided chain reactions: Harmonic and anharmonic interactions</title><link>http://link.aps.org/doi/10.1103/PhysRevB.80.174301</link><description>Author(s): Nitish Nair and Michael S. Strano<br /><p>We have performed a parametric study of self-propagating chain reactions along a one-dimensional bead-spring array. The coupling between beads is modeled using harmonic and anharmonic Fermi-Pasta-Ulam (FPU)-$β$ and ${φ}^{4}$ potentials. The parameters that define the system are the activation energy…</p><br />[Phys. Rev. B 80, 174301] Published Fri Nov 13, 2009</description><author>PRB: Dynamics, dynamical systems, lattice effects, quantum solids</author><pubDate>Fri, 13 Nov 2009 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PhysRevB.80.174301</guid></item></channel></rss> |