My Customized Papers

[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

Journal of Chemical Theory and Computation: Latest Articles (ACS Publications) — Mon, 12 Jan 2026 20:10:43 GMT

Journal of Chemical Theory and Computation

DOI: 10.1021/acs.jctc.5c01561

[ScienceDirect Publication: Computational Materials Science] Machine learning guided prediction of solute segregation at coherent and semi-coherent metal/oxide interfaces

ScienceDirect Publication: Computational Materials Science — Mon, 12 Jan 2026 12:46:02 GMT

Publication date: October 2026

Source: Computational Materials Science, Volume 264

Author(s): Yizhou Lu, Blas Pedro Uberuaga, Samrat Choudhury

[cond-mat updates on arXiv.org] The effect of normal stress on stacking fault energy in face-centered cubic metals

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2601.06384v1 Announce Type: new +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.

[cond-mat updates on arXiv.org] Beyond Predicted ZT: Machine Learning Strategies for the Experimental Discovery of Thermoelectric Materials

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2601.06571v1 Announce Type: new +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.

[cond-mat updates on arXiv.org] Altermagnetism-driven FFLO superconductivity in finite-filling 2D lattices

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2601.06735v1 Announce Type: new +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.

[cond-mat updates on arXiv.org] Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2601.06820v1 Announce Type: new +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.

[cond-mat updates on arXiv.org] Nanoindentation induced plasticity in equiatomic MoTaW alloys by experimentally guided machine learning molecular dynamics simulations

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2601.06846v1 Announce Type: new +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.

[cond-mat updates on arXiv.org] A survey of active learning in materials science: Data-driven paradigm for accelerating the research pipeline

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2601.06971v1 Announce Type: new +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. + 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. + 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.

[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

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2601.07012v1 Announce Type: new +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.

[cond-mat updates on arXiv.org] Observation of Time-Reversal Symmetry Breaking in the Type-I Superconductor YbSb$_2$

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2601.07460v1 Announce Type: new +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.

[cond-mat updates on arXiv.org] Machine learning nonequilibrium phase transitions in charge-density wave insulators

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2601.07583v1 Announce Type: new +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.

[cond-mat updates on arXiv.org] PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2601.07742v1 Announce Type: new +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.

[cond-mat updates on arXiv.org] Quantum algorithm for dephasing of coupled systems: decoupling and IQP duality

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2601.06298v1 Announce Type: cross +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.

[cond-mat updates on arXiv.org] Physics-Informed Tree Search for High-Dimensional Computational Design

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2601.06444v1 Announce Type: cross +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.

[cond-mat updates on arXiv.org] Cation Dominated but Negatively Charged Na2SO4,aq-Graphene Interfaces

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2601.06995v1 Announce Type: cross +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.

[cond-mat updates on arXiv.org] The Impact of Ionic Anharmonicity on Superconductivity in Metal-Stuffed B-C Clathrates

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2501.12068v2 Announce Type: replace +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.

[cond-mat updates on arXiv.org] Microscopic theory of phonon polaritons and long wavelength dielectric response

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2505.03915v2 Announce Type: replace +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.

[cond-mat updates on arXiv.org] Machine Learning Interatomic Potentials Enable Molecular Dynamics Simulations of Doped MoS2

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2510.05339v2 Announce Type: replace +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.

[cond-mat updates on arXiv.org] Kinetic Flux Equations for Ion Exchange in Silicate Glasses

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2601.03207v3 Announce Type: replace +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.

[cond-mat updates on arXiv.org] Scalable Dielectric Tensor Predictions for Inorganic Materials using Equivariant Graph Neural Networks

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2601.04755v2 Announce Type: replace +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.

[cond-mat updates on arXiv.org] An information-matching approach to optimal experimental design and active learning

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2411.02740v4 Announce Type: replace-cross +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.

[cond-mat updates on arXiv.org] Berezinskii--Kosterlitz--Thouless transition in a context-sensitive random language model

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2412.01212v2 Announce Type: replace-cross +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.

[cond-mat updates on arXiv.org] Refractive Index, Its Chromatic Dispersion, and Thermal Coefficients of Four Less Common Glycols

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2504.11819v2 Announce Type: replace-cross +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.

[cond-mat updates on arXiv.org] TBPLaS 2.0: a Tight-Binding Package for Large-scale Simulation

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2509.26309v2 Announce Type: replace-cross +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.

[cond-mat updates on arXiv.org] Symbolic regression for defect interactions in 2D materials

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2512.20785v2 Announce Type: replace-cross +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.

[cond-mat updates on arXiv.org] MiST: Understanding the Role of Mid-Stage Scientific Training in Developing Chemical Reasoning Models

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2512.21231v2 Announce Type: replace-cross +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.

[cond-mat updates on arXiv.org] Exact Multimode Quantization of Superconducting Circuits via Boundary Admittance

cond-mat updates on arXiv.org — Tue, 13 Jan 2026 05:00:00 GMT

arXiv:2601.04407v2 Announce Type: replace-cross +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.

[ChemRxiv] A data-efficient reactive machine learning potential to accelerate automated exploration of complex reaction networks

ChemRxiv — Tue, 13 Jan 2026 00:00:00 GMT

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.

[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

Journal of Chemical Theory and Computation: Latest Articles (ACS Publications) — Mon, 12 Jan 2026 20:10:43 GMT

Journal of Chemical Theory and Computation

DOI: 10.1021/acs.jctc.5c01561

[ScienceDirect Publication: Computational Materials Science] Machine learning guided prediction of solute segregation at coherent and semi-coherent metal/oxide interfaces

ScienceDirect Publication: Computational Materials Science — Mon, 12 Jan 2026 12:46:02 GMT

Publication date: October 2026

Source: Computational Materials Science, Volume 264

Author(s): Yizhou Lu, Blas Pedro Uberuaga, Samrat Choudhury

[cond-mat updates on arXiv.org] The effect of normal stress on stacking fault energy in face-centered cubic metals

cond-mat updates on arXiv.org — Mon, 12 Jan 2026 05:00:00 GMT

arXiv:2601.05453v1 Announce Type: new 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.

[cond-mat updates on arXiv.org] Autonomous Discovery of the Ising Model's Critical Parameters with Reinforcement Learning

cond-mat updates on arXiv.org — Mon, 12 Jan 2026 05:00:00 GMT

arXiv:2601.05577v1 Announce Type: new 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.

[cond-mat updates on arXiv.org] Phase Frustration Induced Intrinsic Bose Glass in the Kitaev-Bose-Hubbard Model

cond-mat updates on arXiv.org — Mon, 12 Jan 2026 05:00:00 GMT

arXiv:2601.05781v1 Announce Type: new 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.

[cond-mat updates on arXiv.org] A Critical Examination of Active Learning Workflows in Materials Science

cond-mat updates on arXiv.org — Mon, 12 Jan 2026 05:00:00 GMT

arXiv:2601.05946v1 Announce Type: new @@ -9,7 +36,7 @@ Abstract: Multiscale modeling, which integrates material properties from ab init 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.

[cond-mat updates on arXiv.org] Efficient Band Structure Unfolding with Atom-centered Orbitals: General Theory and Application

cond-mat updates on arXiv.org — Mon, 12 Jan 2026 05:00:00 GMT

arXiv:2506.21089v2 Announce Type: replace 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.

[cond-mat updates on arXiv.org] Disordered Dynamics in High Dimensions: Connections to Random Matrices and Machine Learning

cond-mat updates on arXiv.org — Mon, 12 Jan 2026 05:00:00 GMT

arXiv:2601.01010v2 Announce Type: replace 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.

[cond-mat updates on arXiv.org] Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system

cond-mat updates on arXiv.org — Mon, 12 Jan 2026 05:00:00 GMT

arXiv:2506.05999v2 Announce Type: replace-cross -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.

[ChemRxiv] ACCEL: Automated Closed-loop Co-Optimization and Experimentation Learning Enables Phase-Pure Identification in Formamidinium-based Dion–Jacobson Halide Perovskites

ChemRxiv — Mon, 12 Jan 2026 00:00:00 GMT

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.

[RSC - Chem. Sci. latest articles] From Crystal Structure Prediction to Polymorphic Behaviour: Monte Carlo Threshold Mapping of Crystal Energy Landscapes

RSC - Chem. Sci. latest articles — Mon, 12 Jan 2026 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC08644B, Edge Article

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Pedro Juan-Royo, Graeme Matthew Day
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,...
The content of this RSS Feed (c) The Royal Society of Chemistry

[RSC - Chem. Sci. latest articles] Exploring stacking pressure-induced mechanical failure of a Ni-rich cathode in sulfide solid-state batteries

RSC - Chem. Sci. latest articles — Mon, 12 Jan 2026 00:00:00 GMT

Chem. Sci., 2026, Advance Article
DOI: 10.1039/D5SC09321J, Edge Article

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Yiman Feng, Zhixing Wang, Gui Luo, Duo Deng, Wenjie Peng, Wenchao Zhang, Hui Duan, Feixiang Wu, Xing Ou, Junchao Zheng, Jiexi Wang
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.
To cite this article before page numbers are assigned, use the DOI form of citation above.
The content of this RSS Feed (c) The Royal Society of Chemistry

[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

ACS Materials Letters: Latest Articles (ACS Publications) — Sun, 11 Jan 2026 18:52:15 GMT

ACS Materials Letters

DOI: 10.1021/acsmaterialslett.5c01201

[ScienceDirect Publication: Joule] A critical outlook for large-scale all-solid-state batteries

ScienceDirect Publication: Joule — Sun, 11 Jan 2026 01:50:45 GMT

Publication date: Available online 9 January 2026

Source: Joule

Author(s): Seongjae Ko, Makoto Ue, Atsuo Yamada

[Wiley: Advanced Functional Materials: Table of Contents] Recycling of Thermoplastics with Machine Learning: A Review

Wiley: Advanced Functional Materials: Table of Contents — Sat, 10 Jan 2026 15:14:36 GMT

Advanced Functional Materials, Volume 36, Issue 3, 8 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Electron Compensation Enhanced Triboelectric Sensor Assisted by Machine Learning for Tactile Perception Recognition

Wiley: Advanced Functional Materials: Table of Contents — Sat, 10 Jan 2026 15:14:36 GMT

Advanced Functional Materials, Volume 36, Issue 3, 8 January 2026.

[Wiley: Angewandte Chemie International Edition: Table of Contents] Selective Ion Transport Regulation Enables High Current Density CO2‐to‐C2+ Conversion in Acid

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:07:04 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[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

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:04:51 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[Wiley: Angewandte Chemie International Edition: Table of Contents] Coupled Engineering of Short‐/Long‐Range Disorder in Oxyhalides Unlocks Benchmark Sodium Superionic Conductor

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:04:51 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[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

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:04:51 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[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

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:04:51 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[Wiley: Angewandte Chemie International Edition: Table of Contents] Balancing Oxidative Stability and Ion Transport in Quasi‐Solid Polymer Electrolytes via Chlorine‐Driven Halogenation Engineering

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:04:51 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[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

Wiley: Advanced Energy Materials: Table of Contents — Sat, 10 Jan 2026 14:09:28 GMT

Advanced Energy Materials, EarlyView.

[ChemRxiv] ConforFormer: representation for molecules through understanding of conformers

ChemRxiv — Sat, 10 Jan 2026 00:00:00 GMT

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.

[ChemRxiv] Graph learning of sequence statistics for polymer representation

ChemRxiv — Sat, 10 Jan 2026 00:00:00 GMT

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.

[npj Computational Materials] Machine learning for phase prediction of high entropy carbide ceramics from imbalanced data

npj Computational Materials — Sat, 10 Jan 2026 00:00:00 GMT

npj Computational Materials, Published online: 10 January 2026; doi:10.1038/s41524-025-01873-2

Machine learning for phase prediction of high entropy carbide ceramics from imbalanced data

[Wiley: Small Structures: Table of Contents] Dielectric Constant Guided Solvation Structure Design for Stable Solid Electrolyte Interphase in Lithium Metal Batteries

Wiley: Small Structures: Table of Contents — Fri, 09 Jan 2026 19:05:13 GMT

Small Structures, Volume 7, Issue 1, January 2026.

[ScienceDirect Publication: Journal of Energy Storage] External pressure's influence on lithium-ion transport within solid-state electrolytes

ScienceDirect Publication: Journal of Energy Storage — Fri, 09 Jan 2026 18:31:35 GMT

Publication date: 1 March 2026

Source: Journal of Energy Storage, Volume 149

Author(s): Yuan He, Xiongying Zhang, Dong Lu

[ScienceDirect Publication: Journal of Energy Storage] Alterative aqueous polymer anode binder enabling interfacial stabilization and improved lithium-ion transport

ScienceDirect Publication: Journal of Energy Storage — Fri, 09 Jan 2026 18:31:35 GMT

Publication date: 1 March 2026

Source: Journal of Energy Storage, Volume 149

Author(s): Yuan Peng, Huimin Chen, Xiaowen Qv, Ao Zeng, Jianfeng Xia, Jiangtao Xu, Kunkun Guo

[ScienceDirect Publication: Journal of Energy Storage] Interface engineering of Li_1.5Al_0.5Ge_1.5(PO₄)₃ electrolytes via in-situ polymer–cerium hybrid interlayers for high-performance all-solid-state lithium metal batteries

ScienceDirect Publication: Journal of Energy Storage — Fri, 09 Jan 2026 18:31:35 GMT

Publication date: 1 March 2026

Source: Journal of Energy Storage, Volume 149

Author(s): Kaiqi Wu, Chengjin Peng, Fanglin Wu, Liyuan Huang, Liang Lan, Liqiang Kang, Yecheng Liu, Xin Ao, Shan Fang

[ScienceDirect Publication: Computational Materials Science] A general LLM-powered text mining framework: Applied to extract high entropy alloys

ScienceDirect Publication: Computational Materials Science — Fri, 09 Jan 2026 18:31:33 GMT

Publication date: October 2026

Source: Computational Materials Science, Volume 264

Author(s): Haolun Yuan, Jun Zeng, Jie Zuo, Xin Wang, Dingguo Xu

[ScienceDirect Publication: Materials Today] Heteropolyanion regulation activating decoupled ion transition for Na superionic conductors

ScienceDirect Publication: Materials Today — Fri, 09 Jan 2026 18:31:29 GMT

Publication date: Available online 9 January 2026

Source: Materials Today

Author(s): Tian Jiang, Qi Fan, Wenshan Gou, Anyang Yu, Changhao Zhu, Ruirui Zhang, Youwei Dong, Shijun Yuan, Qingyu Xu

[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

ACS Energy Letters: Latest Articles (ACS Publications) — Fri, 09 Jan 2026 16:22:26 GMT

ACS Energy Letters

DOI: 10.1021/acsenergylett.5c02943

[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

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Fri, 09 Jan 2026 12:51:38 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c17242

[Wiley: Advanced Science: Table of Contents] Uncertainty‐Quantified Primary Particle Size Prediction in Li‐Rich NCM Materials via Machine Learning and Chemistry‐Aware Imputation

Wiley: Advanced Science: Table of Contents — Fri, 09 Jan 2026 11:35:15 GMT

Advanced Science, Volume 13, Issue 2, 9 January 2026.

[Wiley: Advanced Science: Table of Contents] Highly Selective CO2 Reduction to Pure Formic Acid Using a Nafion‐TiO2 Composite Porous Solid Electrolyte

Wiley: Advanced Science: Table of Contents — Fri, 09 Jan 2026 11:35:15 GMT

Advanced Science, Volume 13, Issue 2, 9 January 2026.

[Wiley: Advanced Science: Table of Contents] Macroscopically Ordered Piezo‐Potential in All‐Polymetric Solid Electrolytes Responding to Li Anode Volume Changes for Dendrites Suppression

Wiley: Advanced Science: Table of Contents — Fri, 09 Jan 2026 11:35:15 GMT

Advanced Science, Volume 13, Issue 2, 9 January 2026.

[Recent Articles in Phys. Rev. B] Reinforcement fine-tuning for materials design

Recent Articles in Phys. Rev. B — Fri, 09 Jan 2026 10:00:00 GMT

Author(s): Zhendong Cao and Lei Wang

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…

[Phys. Rev. B 113, 024106] Published Fri Jan 09, 2026

[cond-mat updates on arXiv.org] Fluctuation conductivity in ultraclean multicomponent superconductors

cond-mat updates on arXiv.org — Mon, 12 Jan 2026 05:00:00 GMT

arXiv:2601.04308v1 Announce Type: new +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.

[ChemRxiv] ACCEL: Automated Closed-loop Co-Optimization and Experimentation Learning Enables Phase-Pure Identification in Formamidinium-based Dion–Jacobson Halide Perovskites

ChemRxiv — Mon, 12 Jan 2026 00:00:00 GMT

[RSC - Chem. Sci. latest articles] From Crystal Structure Prediction to Polymorphic Behaviour: Monte Carlo Threshold Mapping of Crystal Energy Landscapes

RSC - Chem. Sci. latest articles — Mon, 12 Jan 2026 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC08644B, Edge Article

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

[RSC - Chem. Sci. latest articles] Exploring stacking pressure-induced mechanical failure of a Ni-rich cathode in sulfide solid-state batteries

RSC - Chem. Sci. latest articles — Mon, 12 Jan 2026 00:00:00 GMT

Chem. Sci., 2026, Advance Article
DOI: 10.1039/D5SC09321J, Edge Article

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

[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

ACS Materials Letters: Latest Articles (ACS Publications) — Sun, 11 Jan 2026 18:52:15 GMT

ACS Materials Letters

DOI: 10.1021/acsmaterialslett.5c01201

[ScienceDirect Publication: Joule] A critical outlook for large-scale all-solid-state batteries

ScienceDirect Publication: Joule — Sun, 11 Jan 2026 01:50:45 GMT

Publication date: Available online 9 January 2026

Source: Joule

Author(s): Seongjae Ko, Makoto Ue, Atsuo Yamada

[Wiley: Advanced Functional Materials: Table of Contents] Recycling of Thermoplastics with Machine Learning: A Review

Wiley: Advanced Functional Materials: Table of Contents — Sat, 10 Jan 2026 15:14:36 GMT

Advanced Functional Materials, Volume 36, Issue 3, 8 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Electron Compensation Enhanced Triboelectric Sensor Assisted by Machine Learning for Tactile Perception Recognition

Wiley: Advanced Functional Materials: Table of Contents — Sat, 10 Jan 2026 15:14:36 GMT

Advanced Functional Materials, Volume 36, Issue 3, 8 January 2026.

[Wiley: Angewandte Chemie International Edition: Table of Contents] Selective Ion Transport Regulation Enables High Current Density CO2‐to‐C2+ Conversion in Acid

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:07:04 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[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

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:04:51 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[Wiley: Angewandte Chemie International Edition: Table of Contents] Coupled Engineering of Short‐/Long‐Range Disorder in Oxyhalides Unlocks Benchmark Sodium Superionic Conductor

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:04:51 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[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

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:04:51 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[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

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:04:51 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[Wiley: Angewandte Chemie International Edition: Table of Contents] Balancing Oxidative Stability and Ion Transport in Quasi‐Solid Polymer Electrolytes via Chlorine‐Driven Halogenation Engineering

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 10 Jan 2026 15:04:51 GMT

Angewandte Chemie International Edition, Volume 65, Issue 2, 9 January 2026.

[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

Wiley: Advanced Energy Materials: Table of Contents — Sat, 10 Jan 2026 14:09:28 GMT

Advanced Energy Materials, EarlyView.

[ChemRxiv] ConforFormer: representation for molecules through understanding of conformers

ChemRxiv — Sat, 10 Jan 2026 00:00:00 GMT

[ChemRxiv] Graph learning of sequence statistics for polymer representation

ChemRxiv — Sat, 10 Jan 2026 00:00:00 GMT

[npj Computational Materials] Machine learning for phase prediction of high entropy carbide ceramics from imbalanced data

npj Computational Materials — Sat, 10 Jan 2026 00:00:00 GMT

npj Computational Materials, Published online: 10 January 2026; doi:10.1038/s41524-025-01873-2

Machine learning for phase prediction of high entropy carbide ceramics from imbalanced data

[Wiley: Small Structures: Table of Contents] Dielectric Constant Guided Solvation Structure Design for Stable Solid Electrolyte Interphase in Lithium Metal Batteries

Wiley: Small Structures: Table of Contents — Fri, 09 Jan 2026 19:05:13 GMT

Small Structures, Volume 7, Issue 1, January 2026.

[ScienceDirect Publication: Journal of Energy Storage] External pressure's influence on lithium-ion transport within solid-state electrolytes

ScienceDirect Publication: Journal of Energy Storage — Fri, 09 Jan 2026 18:31:35 GMT

Publication date: 1 March 2026

Source: Journal of Energy Storage, Volume 149

Author(s): Yuan He, Xiongying Zhang, Dong Lu

[ScienceDirect Publication: Journal of Energy Storage] Alterative aqueous polymer anode binder enabling interfacial stabilization and improved lithium-ion transport

ScienceDirect Publication: Journal of Energy Storage — Fri, 09 Jan 2026 18:31:35 GMT

Publication date: 1 March 2026

Source: Journal of Energy Storage, Volume 149

Author(s): Yuan Peng, Huimin Chen, Xiaowen Qv, Ao Zeng, Jianfeng Xia, Jiangtao Xu, Kunkun Guo

[ScienceDirect Publication: Journal of Energy Storage] Interface engineering of Li_1.5Al_0.5Ge_1.5(PO₄)₃ electrolytes via in-situ polymer–cerium hybrid interlayers for high-performance all-solid-state lithium metal batteries

ScienceDirect Publication: Journal of Energy Storage — Fri, 09 Jan 2026 18:31:35 GMT

Publication date: 1 March 2026

Source: Journal of Energy Storage, Volume 149

Author(s): Kaiqi Wu, Chengjin Peng, Fanglin Wu, Liyuan Huang, Liang Lan, Liqiang Kang, Yecheng Liu, Xin Ao, Shan Fang

[ScienceDirect Publication: Computational Materials Science] A general LLM-powered text mining framework: Applied to extract high entropy alloys

ScienceDirect Publication: Computational Materials Science — Fri, 09 Jan 2026 18:31:33 GMT

Publication date: October 2026

Source: Computational Materials Science, Volume 264

Author(s): Haolun Yuan, Jun Zeng, Jie Zuo, Xin Wang, Dingguo Xu

[ScienceDirect Publication: Materials Today] Heteropolyanion regulation activating decoupled ion transition for Na superionic conductors

ScienceDirect Publication: Materials Today — Fri, 09 Jan 2026 18:31:29 GMT

Publication date: Available online 9 January 2026

Source: Materials Today

Author(s): Tian Jiang, Qi Fan, Wenshan Gou, Anyang Yu, Changhao Zhu, Ruirui Zhang, Youwei Dong, Shijun Yuan, Qingyu Xu

[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

ACS Energy Letters: Latest Articles (ACS Publications) — Fri, 09 Jan 2026 16:22:26 GMT

ACS Energy Letters

DOI: 10.1021/acsenergylett.5c02943

[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

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Fri, 09 Jan 2026 12:51:38 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c17242

[Wiley: Advanced Science: Table of Contents] Uncertainty‐Quantified Primary Particle Size Prediction in Li‐Rich NCM Materials via Machine Learning and Chemistry‐Aware Imputation

Wiley: Advanced Science: Table of Contents — Fri, 09 Jan 2026 11:35:15 GMT

Advanced Science, Volume 13, Issue 2, 9 January 2026.

[Wiley: Advanced Science: Table of Contents] Highly Selective CO2 Reduction to Pure Formic Acid Using a Nafion‐TiO2 Composite Porous Solid Electrolyte

Wiley: Advanced Science: Table of Contents — Fri, 09 Jan 2026 11:35:15 GMT

Advanced Science, Volume 13, Issue 2, 9 January 2026.

[Wiley: Advanced Science: Table of Contents] Macroscopically Ordered Piezo‐Potential in All‐Polymetric Solid Electrolytes Responding to Li Anode Volume Changes for Dendrites Suppression

Wiley: Advanced Science: Table of Contents — Fri, 09 Jan 2026 11:35:15 GMT

Advanced Science, Volume 13, Issue 2, 9 January 2026.

[Recent Articles in Phys. Rev. B] Reinforcement fine-tuning for materials design

Recent Articles in Phys. Rev. B — Fri, 09 Jan 2026 10:00:00 GMT

Author(s): Zhendong Cao and Lei Wang

[Phys. Rev. B 113, 024106] Published Fri Jan 09, 2026

[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

Fri, 09 Jan 2026 08:00:00 GMT

Proceedings of the National Academy of Sciences, Volume 123, Issue 2, January 2026.
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,...

[cond-mat updates on arXiv.org] Fluctuation conductivity in ultraclean multicomponent superconductors

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2601.04308v1 Announce Type: new 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.

[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

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2601.04725v1 Announce Type: new 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.

[cond-mat updates on arXiv.org] Scalable Dielectric Tensor Predictions for Inorganic Materials using Equivariant Graph Neural Networks

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2601.04755v1 Announce Type: new 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.

[cond-mat updates on arXiv.org] Lateral Graphene-Metallene Interfaces at the Nanoscale

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2601.04838v1 Announce Type: new @@ -25,7 +52,7 @@ Abstract: Large language models suffer from "hallucinations"-logical inconsisten 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.

[cond-mat updates on arXiv.org] Characterizing the cage state of glassy systems and its sensitivity to frozen boundaries

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2507.16339v2 Announce Type: replace 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.

[cond-mat updates on arXiv.org] Li+/H+ exchange in solid-state oxide Li-ion conductors

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2509.13477v2 Announce Type: replace 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.

[cond-mat updates on arXiv.org] A universal machine learning model for the electronic density of states

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2508.17418v2 Announce Type: replace-cross -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.

[RSC - Digital Discovery latest articles] MOFReasoner: Think Like a Scientist-A Reasoning Large Language Model via Knowledge Distillation

RSC - Digital Discovery latest articles — Fri, 09 Jan 2026 00:00:00 GMT

Digital Discovery, 2025, Accepted Manuscript
DOI: 10.1039/D5DD00429B, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Xuefeng Bai, Zhiling Zheng, Xin Zhang, Hao-Tian Wang, Rui Yang, Jian-Rong Li
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,...
The content of this RSS Feed (c) The Royal Society of Chemistry

[npj Computational Materials] Scaling reliable interatomic potentials to complex nuclear alloys via pretrained atomic models

npj Computational Materials — Fri, 09 Jan 2026 00:00:00 GMT

npj Computational Materials, Published online: 09 January 2026; doi:10.1038/s41524-025-01950-6

Scaling reliable interatomic potentials to complex nuclear alloys via pretrained atomic models

[ChemRxiv] Efficient Simulation of Optical Spectra via Machine Learning and Physical Decomposition of Environmental Effects

ChemRxiv — Fri, 09 Jan 2026 00:00:00 GMT

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.

[ChemRxiv] MolPic: Name/SMILES to Publication-Ready Molecular Figures

ChemRxiv — Fri, 09 Jan 2026 00:00:00 GMT

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.

[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

RSC - Chem. Sci. latest articles — Fri, 09 Jan 2026 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC08747C, Edge Article

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Lin-bo Tang, Xian-Kai Fan, Kaixiong Xiang, Wei Zhou, Weina Deng, Hai Zhu, Liang Chen, Junchao Zheng, Han Chen
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...
The content of this RSS Feed (c) The Royal Society of Chemistry

[ChemRxiv] Toward On-the-Fly Prediction of Reaction Energetics for High-Throughput Screening

ChemRxiv — Fri, 09 Jan 2026 00:00:00 GMT

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.

[RSC - Chem. Sci. latest articles] Unveiling sodium storage mechanisms in hard carbon via machine learning-driven simulations with accurate site occupation identification

RSC - Chem. Sci. latest articles — Fri, 09 Jan 2026 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC07068F, Edge Article

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Zhaoming Wang, Guanghui Shi, Guanghui Wang, Man Wang, Feng Ding, Xiao Wang
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...
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[Joule] A critical outlook for large-scale all-solid-state batteries

Joule — Fri, 09 Jan 2026 00:00:00 GMT

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.

[ChemRxiv] Optical Fiber Chemical Catalysis

ChemRxiv — Fri, 09 Jan 2026 00:00:00 GMT

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.

[ChemRxiv] ReactionForge: Temporal Graph Networks with Cross-Attention and Evidential Learning Surpass State-of-the-Art in Suzuki-Miyaura Yield Prediction

ChemRxiv — Fri, 09 Jan 2026 00:00:00 GMT

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.

[ChemRxiv] Organic ionic plastic crystals composed of tetrahydrothiophenium cation with high conductivity

ChemRxiv — Fri, 09 Jan 2026 00:00:00 GMT

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.

[Applied Physics Reviews Current Issue] Ferroelectric and ferroionic multifunctional quantum sensors: Incursion into applications

Applied Physics Reviews Current Issue — Fri, 09 Jan 2026 00:00:00 GMT

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.

[ScienceDirect Publication: Journal of Energy Storage] Polydopamine coating on garnet-type solid electrolyte for enhancing interfacial compatibility in solid-state lithium metal batteries

ScienceDirect Publication: Journal of Energy Storage — Thu, 08 Jan 2026 18:28:37 GMT

Publication date: 28 February 2026

Source: Journal of Energy Storage, Volume 148

Author(s): Lifeng Guan, Lian Wu, Xinyuan Li, Xuanshuo Zhang, Xiuqing Hao, Jinxiu Wen, Wei Zeng

[ScienceDirect Publication: Science Bulletin] Machine learning-based diagnosis of uterine myomas and sarcomas using tumor-educated platelet transcriptomics: a retrospective multicenter study

ScienceDirect Publication: Science Bulletin — Thu, 08 Jan 2026 18:28:36 GMT

Publication date: 15 January 2026

Source: Science Bulletin, Volume 71, Issue 1

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

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Local Symmetry Breaking Induced Superionic Conductivity in Argyrodites

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Thu, 08 Jan 2026 18:12:05 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c17193

[Wiley: Advanced Science: Table of Contents] OBUSight: Clinically Aligned Generative AI for Ophthalmic Ultrasound Interpretation and Diagnosis

Wiley: Advanced Science: Table of Contents — Thu, 08 Jan 2026 13:20:36 GMT

Advanced Science, EarlyView.

[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

Wiley: Advanced Science: Table of Contents — Thu, 08 Jan 2026 13:11:10 GMT

Advanced Science, EarlyView.

[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

ScienceDirect Publication: Solid State Ionics — Thu, 08 Jan 2026 12:44:16 GMT

Publication date: 1 March 2026

Source: Solid State Ionics, Volume 436

Author(s): Ranaa M. Almarshedy, Siti Rohana Majid, Ninie Suhana Abdul Manan

[ScienceDirect Publication: Solid State Ionics] One – Step synthesis of glass ceramic Li₆PS₅Cl_1-xI_x solid electrolytes for all-solid-state batteries

ScienceDirect Publication: Solid State Ionics — Thu, 08 Jan 2026 12:44:16 GMT

Publication date: 1 March 2026

Source: Solid State Ionics, Volume 436

Author(s): Nurcemal Atmaca, Mahir Uenal, Hansen Chang, Oliver Clemens

[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)

Wiley: Small: Table of Contents — Thu, 08 Jan 2026 12:24:03 GMT

Small, Volume 22, Issue 2, 8 January 2026.

[Wiley: Small: Table of Contents] From Composition to Ionic Conductivity: Machine Learning‐Guided Discovery and Experimental Validation of Argyrodite‐Type Lithium‐Ion Electrolytes

Wiley: Small: Table of Contents — Thu, 08 Jan 2026 12:24:03 GMT

Small, Volume 22, Issue 2, 8 January 2026.

[Wiley: Small: Table of Contents] Organosilane Plasma Enhanced Interfacial Engineering to Boost Inorganic‐Rich Hybrid Solid Electrolyte Interface for Advanced Lithium Metal Batteries

Wiley: Small: Table of Contents — Thu, 08 Jan 2026 12:24:03 GMT

Small, Volume 22, Issue 2, 8 January 2026.

[Wiley: Small: Table of Contents] Conductive Composite Hydrogel with Unsymmetrical Structure as Multimodal Triboelectric Nanogenerators for Machine Learning‐Assisted Motion

Wiley: Small: Table of Contents — Thu, 08 Jan 2026 12:24:03 GMT

Small, Volume 22, Issue 2, 8 January 2026.

[Wiley: Small: Table of Contents] Adsorption‐Enhanced Bismuth Oxide Efficiently Convert CO2 to Formate Over a Wide Potential Window

Wiley: Small: Table of Contents — Thu, 08 Jan 2026 11:36:11 GMT

Small, EarlyView.

[Wiley: Small: Table of Contents] MOF in Polymer Electrolytes Raising Ion Transport for Breakthrough Lithium Metal Batteries

Wiley: Small: Table of Contents — Thu, 08 Jan 2026 11:17:57 GMT

Small, EarlyView.

[Recent Articles in Phys. Rev. Lett.] Enhancing Gravitational-Wave Detection: A Machine Learning Pipeline Combination Approach with Robust Uncertainty Quantification

Recent Articles in Phys. Rev. Lett. — Thu, 08 Jan 2026 10:00:00 GMT

Author(s): Gregory Ashton, Ann-Kristin Malz, and Nicolo Colombo

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 …

[Phys. Rev. Lett. 136, 011402] Published Thu Jan 08, 2026

[Recent Articles in Phys. Rev. B] Universal band center model for the HER activity of nonmetal sites in transition metal dichalcogenides

Recent Articles in Phys. Rev. B — Thu, 08 Jan 2026 10:00:00 GMT

Author(s): Ruixin Xu, Shiqian Cao, Tingting Bo, Yanyu Liu, and Wei Zhou

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…

[Phys. Rev. B 113, 035305] Published Thu Jan 08, 2026

[Wiley: Small Methods: Table of Contents] Interfacial Stability and Design Strategies for Halide Solid Electrolytes in High‐Voltage All‐Solid‐State Sodium‐Ion Batteries

Wiley: Small Methods: Table of Contents — Thu, 08 Jan 2026 06:35:51 GMT

Small Methods, EarlyView.

[cond-mat updates on arXiv.org] Physically Consistent Machine Learning for Melting Temperature Prediction of Refractory High-Entropy Alloys

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2601.03801v1 Announce Type: new +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.

[RSC - Digital Discovery latest articles] MOFReasoner: Think Like a Scientist-A Reasoning Large Language Model via Knowledge Distillation

RSC - Digital Discovery latest articles — Fri, 09 Jan 2026 00:00:00 GMT

Digital Discovery, 2025, Accepted Manuscript
DOI: 10.1039/D5DD00429B, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

[npj Computational Materials] Scaling reliable interatomic potentials to complex nuclear alloys via pretrained atomic models

npj Computational Materials — Fri, 09 Jan 2026 00:00:00 GMT

npj Computational Materials, Published online: 09 January 2026; doi:10.1038/s41524-025-01950-6

Scaling reliable interatomic potentials to complex nuclear alloys via pretrained atomic models

[ChemRxiv] Efficient Simulation of Optical Spectra via Machine Learning and Physical Decomposition of Environmental Effects

ChemRxiv — Fri, 09 Jan 2026 00:00:00 GMT

[ChemRxiv] MolPic: Name/SMILES to Publication-Ready Molecular Figures

ChemRxiv — Fri, 09 Jan 2026 00:00:00 GMT

[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

RSC - Chem. Sci. latest articles — Fri, 09 Jan 2026 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC08747C, Edge Article

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

[ChemRxiv] Toward On-the-Fly Prediction of Reaction Energetics for High-Throughput Screening

ChemRxiv — Fri, 09 Jan 2026 00:00:00 GMT

[RSC - Chem. Sci. latest articles] Unveiling sodium storage mechanisms in hard carbon via machine learning-driven simulations with accurate site occupation identification

RSC - Chem. Sci. latest articles — Fri, 09 Jan 2026 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC07068F, Edge Article

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

[Joule] A critical outlook for large-scale all-solid-state batteries

Joule — Fri, 09 Jan 2026 00:00:00 GMT

[ChemRxiv] Optical Fiber Chemical Catalysis

ChemRxiv — Fri, 09 Jan 2026 00:00:00 GMT

[ChemRxiv] ReactionForge: Temporal Graph Networks with Cross-Attention and Evidential Learning Surpass State-of-the-Art in Suzuki-Miyaura Yield Prediction

ChemRxiv — Fri, 09 Jan 2026 00:00:00 GMT

[ChemRxiv] Organic ionic plastic crystals composed of tetrahydrothiophenium cation with high conductivity

ChemRxiv — Fri, 09 Jan 2026 00:00:00 GMT

[Applied Physics Reviews Current Issue] Ferroelectric and ferroionic multifunctional quantum sensors: Incursion into applications

Applied Physics Reviews Current Issue — Fri, 09 Jan 2026 00:00:00 GMT

[ScienceDirect Publication: Journal of Energy Storage] Polydopamine coating on garnet-type solid electrolyte for enhancing interfacial compatibility in solid-state lithium metal batteries

ScienceDirect Publication: Journal of Energy Storage — Thu, 08 Jan 2026 18:28:37 GMT

Publication date: 28 February 2026

Source: Journal of Energy Storage, Volume 148

Author(s): Lifeng Guan, Lian Wu, Xinyuan Li, Xuanshuo Zhang, Xiuqing Hao, Jinxiu Wen, Wei Zeng

[ScienceDirect Publication: Science Bulletin] Machine learning-based diagnosis of uterine myomas and sarcomas using tumor-educated platelet transcriptomics: a retrospective multicenter study

ScienceDirect Publication: Science Bulletin — Thu, 08 Jan 2026 18:28:36 GMT

Publication date: 15 January 2026

Source: Science Bulletin, Volume 71, Issue 1

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Local Symmetry Breaking Induced Superionic Conductivity in Argyrodites

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Thu, 08 Jan 2026 18:12:05 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c17193

[Wiley: Advanced Science: Table of Contents] OBUSight: Clinically Aligned Generative AI for Ophthalmic Ultrasound Interpretation and Diagnosis

Wiley: Advanced Science: Table of Contents — Thu, 08 Jan 2026 13:20:36 GMT

Advanced Science, EarlyView.

[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

Wiley: Advanced Science: Table of Contents — Thu, 08 Jan 2026 13:11:10 GMT

Advanced Science, EarlyView.

[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

ScienceDirect Publication: Solid State Ionics — Thu, 08 Jan 2026 12:44:16 GMT

Publication date: 1 March 2026

Source: Solid State Ionics, Volume 436

Author(s): Ranaa M. Almarshedy, Siti Rohana Majid, Ninie Suhana Abdul Manan

[ScienceDirect Publication: Solid State Ionics] One – Step synthesis of glass ceramic Li₆PS₅Cl_1-xI_x solid electrolytes for all-solid-state batteries

ScienceDirect Publication: Solid State Ionics — Thu, 08 Jan 2026 12:44:16 GMT

Publication date: 1 March 2026

Source: Solid State Ionics, Volume 436

Author(s): Nurcemal Atmaca, Mahir Uenal, Hansen Chang, Oliver Clemens

[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)

Wiley: Small: Table of Contents — Thu, 08 Jan 2026 12:24:03 GMT

Small, Volume 22, Issue 2, 8 January 2026.

[Wiley: Small: Table of Contents] From Composition to Ionic Conductivity: Machine Learning‐Guided Discovery and Experimental Validation of Argyrodite‐Type Lithium‐Ion Electrolytes

Wiley: Small: Table of Contents — Thu, 08 Jan 2026 12:24:03 GMT

Small, Volume 22, Issue 2, 8 January 2026.

[Wiley: Small: Table of Contents] Organosilane Plasma Enhanced Interfacial Engineering to Boost Inorganic‐Rich Hybrid Solid Electrolyte Interface for Advanced Lithium Metal Batteries

Wiley: Small: Table of Contents — Thu, 08 Jan 2026 12:24:03 GMT

Small, Volume 22, Issue 2, 8 January 2026.

[Wiley: Small: Table of Contents] Conductive Composite Hydrogel with Unsymmetrical Structure as Multimodal Triboelectric Nanogenerators for Machine Learning‐Assisted Motion

Wiley: Small: Table of Contents — Thu, 08 Jan 2026 12:24:03 GMT

Small, Volume 22, Issue 2, 8 January 2026.

[Wiley: Small: Table of Contents] Adsorption‐Enhanced Bismuth Oxide Efficiently Convert CO2 to Formate Over a Wide Potential Window

Wiley: Small: Table of Contents — Thu, 08 Jan 2026 11:36:11 GMT

Small, EarlyView.

[Wiley: Small: Table of Contents] MOF in Polymer Electrolytes Raising Ion Transport for Breakthrough Lithium Metal Batteries

Wiley: Small: Table of Contents — Thu, 08 Jan 2026 11:17:57 GMT

Small, EarlyView.

[Recent Articles in Phys. Rev. Lett.] Enhancing Gravitational-Wave Detection: A Machine Learning Pipeline Combination Approach with Robust Uncertainty Quantification

Recent Articles in Phys. Rev. Lett. — Thu, 08 Jan 2026 10:00:00 GMT

Author(s): Gregory Ashton, Ann-Kristin Malz, and Nicolo Colombo

[Phys. Rev. Lett. 136, 011402] Published Thu Jan 08, 2026

[Recent Articles in Phys. Rev. B] Universal band center model for the HER activity of nonmetal sites in transition metal dichalcogenides

Recent Articles in Phys. Rev. B — Thu, 08 Jan 2026 10:00:00 GMT

Author(s): Ruixin Xu, Shiqian Cao, Tingting Bo, Yanyu Liu, and Wei Zhou

[Phys. Rev. B 113, 035305] Published Thu Jan 08, 2026

[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

Thu, 08 Jan 2026 08:00:00 GMT

Proceedings of the National Academy of Sciences, Volume 123, Issue 2, January 2026.
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 ...

[Wiley: Small Methods: Table of Contents] Interfacial Stability and Design Strategies for Halide Solid Electrolytes in High‐Voltage All‐Solid‐State Sodium‐Ion Batteries

Wiley: Small Methods: Table of Contents — Thu, 08 Jan 2026 06:35:51 GMT

Small Methods, EarlyView.

[cond-mat updates on arXiv.org] Physically Consistent Machine Learning for Melting Temperature Prediction of Refractory High-Entropy Alloys

cond-mat updates on arXiv.org — Thu, 08 Jan 2026 05:00:00 GMT

arXiv:2601.03801v1 Announce Type: new 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.

[cond-mat updates on arXiv.org] Material exploration through active learning -- METAL

cond-mat updates on arXiv.org — Thu, 08 Jan 2026 05:00:00 GMT

arXiv:2601.03933v1 Announce Type: new 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.

[cond-mat updates on arXiv.org] Transport properties in a model of confined granular mixtures at moderate densities

cond-mat updates on arXiv.org — Thu, 08 Jan 2026 05:00:00 GMT

arXiv:2601.04026v1 Announce Type: new 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.

My Customized Papers

[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

[ScienceDirect Publication: Computational Materials Science] Machine learning guided prediction of solute segregation at coherent and semi-coherent metal/oxide interfaces

[cond-mat updates on arXiv.org] The effect of normal stress on stacking fault energy in face-centered cubic metals

[cond-mat updates on arXiv.org] Beyond Predicted ZT: Machine Learning Strategies for the Experimental Discovery of Thermoelectric Materials

[cond-mat updates on arXiv.org] Altermagnetism-driven FFLO superconductivity in finite-filling 2D lattices

[cond-mat updates on arXiv.org] Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery

[cond-mat updates on arXiv.org] Nanoindentation induced plasticity in equiatomic MoTaW alloys by experimentally guided machine learning molecular dynamics simulations

[cond-mat updates on arXiv.org] A survey of active learning in materials science: Data-driven paradigm for accelerating the research pipeline

[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

[cond-mat updates on arXiv.org] Observation of Time-Reversal Symmetry Breaking in the Type-I Superconductor YbSb$_2$

[cond-mat updates on arXiv.org] Machine learning nonequilibrium phase transitions in charge-density wave insulators

[cond-mat updates on arXiv.org] PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials

[cond-mat updates on arXiv.org] Quantum algorithm for dephasing of coupled systems: decoupling and IQP duality

[cond-mat updates on arXiv.org] Physics-Informed Tree Search for High-Dimensional Computational Design

[cond-mat updates on arXiv.org] Cation Dominated but Negatively Charged Na2SO4,aq-Graphene Interfaces

[cond-mat updates on arXiv.org] The Impact of Ionic Anharmonicity on Superconductivity in Metal-Stuffed B-C Clathrates

[cond-mat updates on arXiv.org] Microscopic theory of phonon polaritons and long wavelength dielectric response

[cond-mat updates on arXiv.org] Machine Learning Interatomic Potentials Enable Molecular Dynamics Simulations of Doped MoS2

[cond-mat updates on arXiv.org] Kinetic Flux Equations for Ion Exchange in Silicate Glasses

[cond-mat updates on arXiv.org] Scalable Dielectric Tensor Predictions for Inorganic Materials using Equivariant Graph Neural Networks

[cond-mat updates on arXiv.org] An information-matching approach to optimal experimental design and active learning

[cond-mat updates on arXiv.org] Berezinskii--Kosterlitz--Thouless transition in a context-sensitive random language model

[cond-mat updates on arXiv.org] Refractive Index, Its Chromatic Dispersion, and Thermal Coefficients of Four Less Common Glycols

[cond-mat updates on arXiv.org] TBPLaS 2.0: a Tight-Binding Package for Large-scale Simulation

[cond-mat updates on arXiv.org] Symbolic regression for defect interactions in 2D materials

[cond-mat updates on arXiv.org] MiST: Understanding the Role of Mid-Stage Scientific Training in Developing Chemical Reasoning Models

[cond-mat updates on arXiv.org] Exact Multimode Quantization of Superconducting Circuits via Boundary Admittance

[ChemRxiv] A data-efficient reactive machine learning potential to accelerate automated exploration of complex reaction networks

[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

[ScienceDirect Publication: Computational Materials Science] Machine learning guided prediction of solute segregation at coherent and semi-coherent metal/oxide interfaces

[cond-mat updates on arXiv.org] The effect of normal stress on stacking fault energy in face-centered cubic metals

[cond-mat updates on arXiv.org] Autonomous Discovery of the Ising Model's Critical Parameters with Reinforcement Learning

[cond-mat updates on arXiv.org] Phase Frustration Induced Intrinsic Bose Glass in the Kitaev-Bose-Hubbard Model

[cond-mat updates on arXiv.org] A Critical Examination of Active Learning Workflows in Materials Science

[cond-mat updates on arXiv.org] Efficient Band Structure Unfolding with Atom-centered Orbitals: General Theory and Application

[cond-mat updates on arXiv.org] Disordered Dynamics in High Dimensions: Connections to Random Matrices and Machine Learning

[cond-mat updates on arXiv.org] Machine learning for in-situ composition mapping in a self-driving magnetron sputtering system

[ChemRxiv] ACCEL: Automated Closed-loop Co-Optimization and Experimentation Learning Enables Phase-Pure Identification in Formamidinium-based Dion–Jacobson Halide Perovskites

[RSC - Chem. Sci. latest articles] From Crystal Structure Prediction to Polymorphic Behaviour: Monte Carlo Threshold Mapping of Crystal Energy Landscapes

[RSC - Chem. Sci. latest articles] Exploring stacking pressure-induced mechanical failure of a Ni-rich cathode in sulfide solid-state batteries

[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

[ScienceDirect Publication: Joule] A critical outlook for large-scale all-solid-state batteries

[Wiley: Advanced Functional Materials: Table of Contents] Recycling of Thermoplastics with Machine Learning: A Review

[Wiley: Advanced Functional Materials: Table of Contents] Electron Compensation Enhanced Triboelectric Sensor Assisted by Machine Learning for Tactile Perception Recognition

[Wiley: Angewandte Chemie International Edition: Table of Contents] Selective Ion Transport Regulation Enables High Current Density CO2‐to‐C2+ Conversion in Acid

[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

[Wiley: Angewandte Chemie International Edition: Table of Contents] Coupled Engineering of Short‐/Long‐Range Disorder in Oxyhalides Unlocks Benchmark Sodium Superionic Conductor

[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

[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

[Wiley: Angewandte Chemie International Edition: Table of Contents] Balancing Oxidative Stability and Ion Transport in Quasi‐Solid Polymer Electrolytes via Chlorine‐Driven Halogenation Engineering

[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

[ChemRxiv] ConforFormer: representation for molecules through understanding of conformers

[ChemRxiv] Graph learning of sequence statistics for polymer representation

[npj Computational Materials] Machine learning for phase prediction of high entropy carbide ceramics from imbalanced data

[Wiley: Small Structures: Table of Contents] Dielectric Constant Guided Solvation Structure Design for Stable Solid Electrolyte Interphase in Lithium Metal Batteries

[ScienceDirect Publication: Journal of Energy Storage] External pressure's influence on lithium-ion transport within solid-state electrolytes

[ScienceDirect Publication: Journal of Energy Storage] Alterative aqueous polymer anode binder enabling interfacial stabilization and improved lithium-ion transport

[ScienceDirect Publication: Journal of Energy Storage] Interface engineering of Li1.5Al0.5Ge1.5(PO4)3 electrolytes via in-situ polymer–cerium hybrid interlayers for high-performance all-solid-state lithium metal batteries

[ScienceDirect Publication: Computational Materials Science] A general LLM-powered text mining framework: Applied to extract high entropy alloys

[ScienceDirect Publication: Materials Today] Heteropolyanion regulation activating decoupled ion transition for Na superionic conductors

[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

[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

[Wiley: Advanced Science: Table of Contents] Uncertainty‐Quantified Primary Particle Size Prediction in Li‐Rich NCM Materials via Machine Learning and Chemistry‐Aware Imputation

[Wiley: Advanced Science: Table of Contents] Highly Selective CO2 Reduction to Pure Formic Acid Using a Nafion‐TiO2 Composite Porous Solid Electrolyte

[Wiley: Advanced Science: Table of Contents] Macroscopically Ordered Piezo‐Potential in All‐Polymetric Solid Electrolytes Responding to Li Anode Volume Changes for Dendrites Suppression

[Recent Articles in Phys. Rev. B] Reinforcement fine-tuning for materials design

[cond-mat updates on arXiv.org] Fluctuation conductivity in ultraclean multicomponent superconductors

[ChemRxiv] ACCEL: Automated Closed-loop Co-Optimization and Experimentation Learning Enables Phase-Pure Identification in Formamidinium-based Dion–Jacobson Halide Perovskites

[RSC - Chem. Sci. latest articles] From Crystal Structure Prediction to Polymorphic Behaviour: Monte Carlo Threshold Mapping of Crystal Energy Landscapes

[RSC - Chem. Sci. latest articles] Exploring stacking pressure-induced mechanical failure of a Ni-rich cathode in sulfide solid-state batteries

[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

[ScienceDirect Publication: Joule] A critical outlook for large-scale all-solid-state batteries

[Wiley: Advanced Functional Materials: Table of Contents] Recycling of Thermoplastics with Machine Learning: A Review

[Wiley: Advanced Functional Materials: Table of Contents] Electron Compensation Enhanced Triboelectric Sensor Assisted by Machine Learning for Tactile Perception Recognition

[Wiley: Angewandte Chemie International Edition: Table of Contents] Selective Ion Transport Regulation Enables High Current Density CO2‐to‐C2+ Conversion in Acid

[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

[Wiley: Angewandte Chemie International Edition: Table of Contents] Coupled Engineering of Short‐/Long‐Range Disorder in Oxyhalides Unlocks Benchmark Sodium Superionic Conductor

[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

[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

[ScienceDirect Publication: Journal of Energy Storage] Interface engineering of Li_1.5Al_0.5Ge_1.5(PO₄)₃ electrolytes via in-situ polymer–cerium hybrid interlayers for high-performance all-solid-state lithium metal batteries

[ScienceDirect Publication: Journal of Energy Storage] Interface engineering of Li_1.5Al_0.5Ge_1.5(PO₄)₃ electrolytes via in-situ polymer–cerium hybrid interlayers for high-performance all-solid-state lithium metal batteries

[ScienceDirect Publication: Solid State Ionics] One – Step synthesis of glass ceramic Li₆PS₅Cl_1-xI_x solid electrolytes for all-solid-state batteries