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 <?xml version="1.0" encoding="UTF-8"?>
-<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/"><channel><title>My Customized Papers (Auto-Filtered)</title><link>https://github.com/your_username/your_repo</link><description>Aggregated research papers based on keywords</description><language>en-US</language><lastBuildDate>Tue, 30 Dec 2025 06:32:58 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[cond-mat updates on arXiv.org] Thermodynamic Phase Stability, Structural, Mechanical, Optoelectronic, and Thermoelectric Properties of the III-V Semiconductor AlSb for Energy Conversion Applications</title><link>https://arxiv.org/abs/2512.22277</link><description>arXiv:2512.22277v1 Announce Type: new 
+<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/"><channel><title>My Customized Papers (Auto-Filtered)</title><link>https://github.com/your_username/your_repo</link><description>Aggregated research papers based on keywords</description><language>en-US</language><lastBuildDate>Tue, 30 Dec 2025 12:42:44 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[ScienceDirect Publication: Journal of Energy Storage] Machine learning-aided prediction and optimization of specific capacitance in functionalized GO-based Mn/Fe co-doped Bi&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt; nanocomposites</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25048285?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 10 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 146&lt;/p&gt;&lt;p&gt;Author(s): Vijay A. Mane, Kartik M. Chavan, Sushant S. Munde, Dnyaneshwar V. Dake, Nita D. Raskar, Ramprasad B. Sonpir, Pravin V. Dhole, Ketan P. Gattu, Sandeep B. Somvanshi, Pavan R. Kayande, Jagruti S. Pawar, Babasaheb N. Dole&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 30 Dec 2025 12:42:28 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25048285</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Time-resolved impedance spectroscopy analysis of stable lithium iron phosphate cathode with enhanced electronic/ionic conductivity and ion diffusion characteristics</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25049035?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 10 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 146&lt;/p&gt;&lt;p&gt;Author(s): Jiguo Tu, Yan Li, Libo Chen, Dongbai Sun&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 30 Dec 2025 12:42:28 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25049035</guid></item><item><title>[cond-mat updates on arXiv.org] Thermodynamic Phase Stability, Structural, Mechanical, Optoelectronic, and Thermoelectric Properties of the III-V Semiconductor AlSb for Energy Conversion Applications</title><link>https://arxiv.org/abs/2512.22277</link><description>arXiv:2512.22277v1 Announce Type: new 
 Abstract: This study presents a first principles investigation of the structural, thermodynamic, electronic, optical and thermoelectric properties of aluminum antimonide (AlSb) in its cubic (F-43m) and hexagonal (P63mc) phases. Both structures are dynamically and mechanically stable, as confirmed by phonon calculations and the Born Huang criteria. The lattice constants obtained using the SCAN and PBEsol functionals show good agreement with experimental data. The cubic phase exhibits a direct band gap of 1.66 to 1.78 eV, while the hexagonal phase shows a band gap of 1.48 to 1.59 eV, as confirmed by mBJ and HSE06 calculations. Under external pressure, the band gap decreases in the cubic phase and increases in the hexagonal phase due to different s p orbital hybridization mechanisms. The optical absorption coefficient reaches 1e6 cm-1, which is comparable to or higher than values reported for other III V semiconductors. The Seebeck coefficient exceeds 1500 microV per K under intrinsic conditions, and the thermoelectric performance improves above 600 K due to enhanced phonon scattering and lattice anharmonicity. The calculated formation energies (-1.316 eV for F-43m and -1.258 eV for P63mc) confirm that the cubic phase is thermodynamically more stable. The hexagonal phase exhibits higher anisotropy and lower lattice stiffness, which is favorable for thermoelectric applications. These results demonstrate the strong interplay between crystal symmetry, phonon behavior and charge transport, and provide useful guidance for the design of AlSb based materials for optoelectronic and energy conversion technologies.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 30 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.22277v1</guid></item><item><title>[cond-mat updates on arXiv.org] The Role of THz Phonons in the Ionic Conduction Mechanism of $Li_7La_3Zr_2O_{12}$ Polymorphs</title><link>https://arxiv.org/abs/2512.22427</link><description>arXiv:2512.22427v1 Announce Type: new 
 Abstract: Superionic conduction in solid-state materials is governed not only by static factors, such as structure and composition, but also by dynamic interactions between the mobile ion and the crystal lattice. Specifically, the dynamics of lattice vibrations, or phonons, have attracted interest because of their hypothesized ability to facilitate superionic conduction. However, direct experimental measurement of the role of phonons in ionic conduction is challenging due to the fast intrinsic timescales of ion hopping and the difficulty of driving relevant phonon modes, which often lie in the low-energy THz regime. To overcome these limitations, we use laser-driven ultrafast impedance spectroscopy (LUIS). LUIS resonantly excites phonons using a THz field and probes ion hopping with picosecond time resolution. We apply LUIS to understand the dynamical role of phonons in $Li_7La_3Zr_2O_{12}$ (LLZO). When in its cubic phase (c-LLZO), this garnet-type solid electrolyte has an ionic conductivity two orders of magnitude greater than its tetragonal phase (t-LLZO). T-LLZO is characterized by an ordered and filled $Li^+$ sublattice necessitating synchronous ion hopping. In contrast, c-LLZO is characterized by a disordered and vacancy-rich $Li^+$ sublattice, and has a conduction mechanism dominated by single hops. We find that, upon excitation of phonons in the 0.5-7.5 THz range, the impedance of t-LLZO experiences a longer ion hopping decay signal in comparison to c-LLZO. The results suggest that phonon-mediated ionic conduction by THz modes may lead to larger ion displacements in ordered and fully occupied mobile ion sublattices as opposed to those that are disordered and vacancy-rich. Overall, this work highlights the interplay between static and dynamic factors that enables improved ionic conductivity in otherwise poorly conducting inorganic solids.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 30 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.22427v1</guid></item><item><title>[cond-mat updates on arXiv.org] Thermally Activated Non-Affine Rearrangements in Amorphous Glass: Emergence of Intrinsic Length Scales</title><link>https://arxiv.org/abs/2512.22530</link><description>arXiv:2512.22530v1 Announce Type: new 
 Abstract: We present a systematic study of temperature-driven nonaffine rearrangements in a model amorphous solid across the full thermodynamic range, from a high-temperature liquid, through supercooled and sub-glass regimes, into deep glassy states. The central result is a quantitative characterisation of the componentwise nonaffine residual displacements, obtained by subtracting local affine maps from particle displacements. For each state point the tails of the probability distributions of these nonaffine components display clear exponential decay; linear fits to the logarithm of the tail region yield characteristic nonaffine length scales {\xi}NA,x and {\xi}NA,y , which quantify the spatial extent of purely nonaffine, local rearrangements. To compare with other length scales, we compute van Hove distributions Gx(ux), Gy (uy ) which capture the full particle displacement field (coherent affine-like motion plus residuals). A robust, key finding is that the van Hove length scale consistently exceeds the filtered nonaffine length scale, i.e. {\xi}VH &gt; {\xi}NA, across all temperatures, state points, and densities we studied. The nonaffine length {\xi}NA quantifies the distance over which complex deformation occurs, specifically nonlinear and anharmonic responses, irreversible (plastic) rearrangements, topological non-recoverable particle rearrangements, and other residual motions that cannot be represented by a local affine map. Moreover, near equality of {\xi}NA,x and {\xi}NA,y in all conditions provides further evidence that nonaffine rearrangements propagate isotropically under thermally driven deformation in contrast to externally driven shear.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 30 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.22530v1</guid></item><item><title>[cond-mat updates on arXiv.org] Fast and accurate Fe-H machine-learning interatomic potential for elucidating hydrogen embrittlement mechanisms</title><link>https://arxiv.org/abs/2512.22934</link><description>arXiv:2512.22934v1 Announce Type: new 
@@ -14,14 +14,14 @@ Abstract: We study the replica-symmetric saddle point equations for the Ising pe
 Abstract: The special quasirandom structure (SQS) method is widely used for modeling disordered materials under periodic boundary conditions, with the ATAT mcsqs module being one of the most established implementations. However, SQS generation with mcsqs typically relies on manual preparation of input files, ad hoc execution scripts, and post-processing steps, which introduces user-dependent errors and limits reproducibility. Here, we present SimplySQS (https://simplysqs.com), an automated and reproducible workflow for SQS generation that is delivered through an online, interactive interface. SimplySQS guides users through structure import, compositional and supercell definition, and cluster parameter selection, while automatically generates all required ATAT input files and a single all-in-one execution script that encapsulates the complete search process. By standardizing input preparation, execution, and output analysis, the framework minimizes errors associated with manual file handling and enables consistent reproducibility of SQS searches. The workflow is demonstrated on the Pb1-xSrxTiO3 (PSTO, including PbTiO3 (PTO) and SrTiO3 (STO)) perovskite system. SQSs spanning the entire concentration range were generated using a single automated bash script produced by SimplySQS, after which all resulting structures were subjected to geometry optimization using a universal machine-learning interatomic potential (MACE MATPES-r2SCAN-0). This approach reliably reproduced the experimentally observed cubic-to-tetragonal transition near x = 0.5, with lattice parameters deviating by less than 1 % in the cubic region (x &gt; 0.5) and less than 4 % in the tetragonal region (x &lt; 0.5). Overall, SimplySQS transforms SQS generation with ATAT into intuitive, reproducible, and systematic framework for modeling disordered materials.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 30 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2510.18020v3</guid></item><item><title>[cond-mat updates on arXiv.org] Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems</title><link>https://arxiv.org/abs/2512.20230</link><description>arXiv:2512.20230v2 Announce Type: replace 
 Abstract: The rapid emergence of universal Machine Learning Interatomic Potentials (uMLIPs) has transformed materials modeling. However, a comprehensive understanding of their generalization behavior across configurational space remains an open challenge. In this work, we introduce a benchmarking framework to evaluate both the equilibrium and far-from-equilibrium performance of state-of-the-art uMLIPs, including three MACE-based models, MatterSim, and PET-MAD. Our assessment utilizes Equation-of-State (EOS) tests to evaluate near-equilibrium properties, such as bulk moduli and equilibrium volumes, alongside extensive Minima Hopping (MH) structural searches to probe the global Potential Energy Surface (PES). Here, we assess universality within the fundamental limit of unary (elemental) systems, which serve as a necessary baseline for broader chemical generalization and provide a framework that can be systematically extended to multicomponent materials. We find that while most models exhibit high accuracy in reproducing equilibrium volumes for transition metals, significant performance gaps emerge in alkali and alkaline earth metal groups. Crucially, our MH results reveal a decoupling between search efficiency and structural fidelity, highlighting that smoother learned PESs do not necessarily yield more accurate energetic landscapes.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 30 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.20230v2</guid></item><item><title>[cond-mat updates on arXiv.org] Emergence of Friedel-like oscillations from Lorenz dynamics in walking droplets</title><link>https://arxiv.org/abs/2512.21049</link><description>arXiv:2512.21049v2 Announce Type: replace 
 Abstract: Friedel oscillations are spatially decaying density modulations near localized defects and are a hallmark of quantum systems. Walking droplets provide a macroscopic platform for hydrodynamic quantum analogs, and Friedel-like oscillations were recently observed in droplet-defect scattering through wave-mediated speed modulation [P.~J.~S\'aenz \textit{et al.}, \textit{Sci.\ Adv.} \textbf{6}, eay9234 (2020)]. Here we show that Friedel-like oscillatory statistics can also arise from a purely local dynamical mechanism, revealed using a minimal Lorenz model description of a walking droplet viewed as an active particle with internal degrees of freedom. A localized defect directly perturbs the particle's internal dynamical state, generating underdamped velocity oscillations that give rise to oscillatory ensemble position statistics. This work opens new avenues for hydrodynamic quantum analogs by revealing how quantum-like statistics can emerge from local internal-state dynamics of active particles.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 30 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.21049v2</guid></item><item><title>[cond-mat updates on arXiv.org] Functional Renormalization Group flows as diffusive Hamilton-Jacobi-type equations</title><link>https://arxiv.org/abs/2512.05973</link><description>arXiv:2512.05973v2 Announce Type: replace-cross 
-Abstract: In order to find reliable and efficient numerical approximation schemes, we suggest to identify the Functional Renormalization Group flow equations of one-particle irreducible two-point functions as Hamilton-Jacobi(-Bellman)-type partial differential equations. Based on this reformulation and reinterpretation we adopt a numerical scheme for the solution of field-dependent flow equations as nonlinear partial differential equations. We demonstrate this novel approach by first applying it to a simple fermion-boson system in zero spacetime dimensions - which itself presents as an interesting playground for method development. Afterwards, we show, how the gained insights can be transferred to more interesting problems: One is the bosonic $\mathbb{Z}_2$-symmetric model in three Euclidean dimensions within a truncation that involves the field-dependent effective potential and field-dependent wave-function renormalization. The other example is the $(1 + 1)$-dimensional Gross-Neveu model within a truncation that involves a field-dependent potential and a field-dependent fermion mass/Yukawa coupling at nonzero temperature, chemical potential, and finite fermion number.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 30 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.05973v2</guid></item><item><title>[ChemRxiv] Probabilistic Forecasting for Coarse-Grained Molecular Dynamics</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-vn440?rft_dat=source%3Ddrss</link><description>Coarse-grained molecular dynamics enables access to long length and time scales but often fails to reproduce atomistic kinetics when memory effects and slow collective motions are important. We introduce Probabilistic Forecasting for Coarse-Graining (PFCG), a machine learning framework that learns stochastic coarse-grained equations of motion directly from atomistic trajectories by formulating coarse-grained simulation as a probabilistic time-series forecasting problem with both Markovian and non-Markovian contributions. PFCG incorporates non-Markovian effects through finite trajectory history without requiring explicit memory kernels or learned effective potentials. We apply PFCG to miniproteins and polyalanine peptides and evaluate both configurational and dynamical fidelity using free energy surfaces, autocorrelation functions, and transition timescales from Markov state models. Across all systems, non-Markovian PFCG models significantly improve dynamical agreement with atomistic simulation relative to Markovian baselines while also maintaining excellent agreement with stationary distributions. These results highlight the importance of inductive biases at the level of equations of motion and establish PFCG as a complementary approach to existing machine learning-based coarse-graining methods for modeling biomolecular processes.</description><author>ChemRxiv</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-vn440?rft_dat=source%3Ddrss</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] Protonation dynamics of confined ethanol–water mixtures in H-ZSM-5 from machine learning-driven metadynamics</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725007249?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Catalysis, Volume 454&lt;/p&gt;&lt;p&gt;Author(s): Princy Jarngal, Benjamin A. Jackson, Simuck F. Yuk, Difan Zhang, Mal-Soon Lee, Maria Cristina Menziani, Vassiliki-Alexandra Glezakou, Roger Rousseau, GiovanniMaria Piccini&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Mon, 29 Dec 2025 18:30:33 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725007249</guid></item><item><title>[Wiley: Small: Table of Contents] Enhancing Cycling Stability and Suppressing Lithium Dendrite Formation With A Hierarchical Artificial Solid Electrolyte Interphase Layer on Lithium Anodes for High‐Voltage Lithium Metal Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202512973?af=R</link><description>Small, EarlyView.</description><author>Wiley: Small: Table of Contents</author><pubDate>Mon, 29 Dec 2025 09:13:44 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202512973</guid></item><item><title>[Wiley: Small: Table of Contents] Elucidating the Sigmoidal Adsorption Behavior of Xenon in Flexible Hofmann‐Type MOFs Through Experiments and Molecular Dynamics with Machine Learning Potentials</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202509479?af=R</link><description>Small, EarlyView.</description><author>Wiley: Small: Table of Contents</author><pubDate>Mon, 29 Dec 2025 08:31:34 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202509479</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Gradient‐Heterojunction in Solid Electrolytes for Fast‐Charging Dendrite‐Free Solid‐State Lithium Metal Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202519284?af=R</link><description>Advanced Materials, EarlyView.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Mon, 29 Dec 2025 07:59:12 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202519284</guid></item><item><title>[cond-mat updates on arXiv.org] Upper bounds on the separation efficiency of diffusiophoresis</title><link>https://arxiv.org/abs/2512.21758</link><description>arXiv:2512.21758v1 Announce Type: new 
+Abstract: In order to find reliable and efficient numerical approximation schemes, we suggest to identify the Functional Renormalization Group flow equations of one-particle irreducible two-point functions as Hamilton-Jacobi(-Bellman)-type partial differential equations. Based on this reformulation and reinterpretation we adopt a numerical scheme for the solution of field-dependent flow equations as nonlinear partial differential equations. We demonstrate this novel approach by first applying it to a simple fermion-boson system in zero spacetime dimensions - which itself presents as an interesting playground for method development. Afterwards, we show, how the gained insights can be transferred to more interesting problems: One is the bosonic $\mathbb{Z}_2$-symmetric model in three Euclidean dimensions within a truncation that involves the field-dependent effective potential and field-dependent wave-function renormalization. The other example is the $(1 + 1)$-dimensional Gross-Neveu model within a truncation that involves a field-dependent potential and a field-dependent fermion mass/Yukawa coupling at nonzero temperature, chemical potential, and finite fermion number.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 30 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.05973v2</guid></item><item><title>[ChemRxiv] Probabilistic Forecasting for Coarse-Grained Molecular Dynamics</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-vn440?rft_dat=source%3Ddrss</link><description>Coarse-grained molecular dynamics enables access to long length and time scales but often fails to reproduce atomistic kinetics when memory effects and slow collective motions are important. We introduce Probabilistic Forecasting for Coarse-Graining (PFCG), a machine learning framework that learns stochastic coarse-grained equations of motion directly from atomistic trajectories by formulating coarse-grained simulation as a probabilistic time-series forecasting problem with both Markovian and non-Markovian contributions. PFCG incorporates non-Markovian effects through finite trajectory history without requiring explicit memory kernels or learned effective potentials. We apply PFCG to miniproteins and polyalanine peptides and evaluate both configurational and dynamical fidelity using free energy surfaces, autocorrelation functions, and transition timescales from Markov state models. Across all systems, non-Markovian PFCG models significantly improve dynamical agreement with atomistic simulation relative to Markovian baselines while also maintaining excellent agreement with stationary distributions. These results highlight the importance of inductive biases at the level of equations of motion and establish PFCG as a complementary approach to existing machine learning-based coarse-graining methods for modeling biomolecular processes.</description><author>ChemRxiv</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-vn440?rft_dat=source%3Ddrss</guid></item><item><title>[npj Computational Materials] Toward high entropy material discovery for energy applications using computational and machine learning methods</title><link>https://www.nature.com/articles/s41524-025-01918-6</link><description>&lt;p&gt;npj Computational Materials, Published online: 30 December 2025; &lt;a href="https://www.nature.com/articles/s41524-025-01918-6"&gt;doi:10.1038/s41524-025-01918-6&lt;/a&gt;&lt;/p&gt;Toward high entropy material discovery for energy applications using computational and machine learning methods</description><author>npj Computational Materials</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41524-025-01918-6</guid></item><item><title>[ChemRxiv] Augmenting Large Language Models for Automated Discovery of f-Element Extractants</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-b1m2q-v2?rft_dat=source%3Ddrss</link><description>Efficient separation of f-elements is a critical challenge for a wide range of emerging technologies. The chemical similarity among these elements makes the development of selective solvent extraction reagents both slow and difficult. Here, we present a quasi-autonomous, AI-enabled workflow for the design and computational screening of selective extractant ligands. Molecular design is guided by SAFE-MolGen, a large language model-based agentic system that leverages curated extraction data to propose new ligands and preliminarily rank their performance using a supervised machine learning model trained on experimental datasets to consider the impact of realistic experimental conditions. Promising human-approved ligands are then passed to a second automated pipeline that constructs three-dimensional metal-ligand complexes and performs quantum mechanical free energy calculations to directly assess metal selectivity. We demonstrate this approach for Am(III)/Eu(III) separations and report several newly designed ligands predicted to exhibit higher Am(III)/Eu(III) selectivity than the benchmark extractant CyMe4BTBP. This workflow accelerates computational exploration of the molecular space in this data-sparse field and provides a general strategy for the rapid generation and evaluation of novel lanthanide (Ln) and actinide (An) extractants.</description><author>ChemRxiv</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-b1m2q-v2?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Hybrid ChemBERTa and DFT Machine Learning Framework for Predicting Enantioselectivity in Organosilanes Mediated Carbonyl Reduction Reactions</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-zhr57?rft_dat=source%3Ddrss</link><description>Predicting the small yet meaningful enantioselectivity differences in organosilanes mediated carbonyl reductions remains challenging because multiple steric, electronic and conformational factors interact in ways that traditional mechanistic rules struggle to describe. To address this challenge, this study integrates quantum chemical de- scriptors with ChemBERTa based molecular embeddings to construct a machine learn- ing framework capable of capturing these subtle structure selectivity relationships. A systematic model comparison was performed using a carefully curated dataset, where LightGBM demonstrated the highest predictive accuracy with RMSE value of 8.381 for %ee. SHAP based interpretability analysis clarified which steric, electronic and geometrical descriptors most strongly influence facial selectivity across these reduc- tions. Together, this hybrid computational approach provides both predictive power and mechanistic insight offering a practical tool for understanding selectivity trends.</description><author>ChemRxiv</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-zhr57?rft_dat=source%3Ddrss</guid></item><item><title>[RSC - Chem. Sci. latest articles] Solid Dual-Salt Plastic Crystal Electrolyte Enabling Rapid Ion Transfer and Stable Interphases for High-Performance Solid-State Sodium Ion Batteries</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC09186A</link><description>&lt;div&gt;&lt;i&gt;&lt;b&gt;Chem. Sci.&lt;/b&gt;&lt;/i&gt;, 2026, Accepted Manuscript&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5SC09186A, Edge Article&lt;/div&gt;&lt;div&gt;&lt;img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /&gt; Open Access&lt;/div&gt;&lt;div&gt;&lt;a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt; &lt;img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution-NonCommercial 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Yang Jiang, Rui Wang, Peng Xiong, Yangyang Liu, Hongbao Li, Longhai Zhang, Ya You, Chaofeng Zhang&lt;br /&gt;As promising next-generation energy storage systems, solid-state sodium ion batteries (SIBs) are hindered by the low ionic conductivity of their solid electrolytes and poor interfacial compatibility. Here, we developed a...&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Chem. Sci. latest articles</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC09186A</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Intelligent Sensing: The Emerging Integration of Machine Learning and Soft Sensors Based on Hydrogels and Ionogels</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202517851?af=R</link><description>Advanced Science, Volume 12, Issue 48, December 29, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Mon, 29 Dec 2025 21:01:21 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202517851</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Pre‐Constructed Mechano‐Electrochemical Adaptive Solid Electrolyte Interphase to Enhance Li+ Diffusion Kinetics and Interface Stability for Chemically Prelithiated SiO Anodes</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202515555?af=R</link><description>Advanced Science, Volume 12, Issue 48, December 29, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Mon, 29 Dec 2025 21:01:21 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202515555</guid></item><item><title>[Wiley: Small: Table of Contents] Unraveling A‐Site Cation Control of Hot Carrier Relaxation in Vacancy‐Ordered Halide Perovskites Through Quantum Dynamics and Interpretable Machine Learning</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202507018?af=R</link><description>Small, Volume 21, Issue 52, December 29, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Mon, 29 Dec 2025 20:38:41 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202507018</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Rational Design of Metal–Organic Frameworks for Pancreatic Cancer Therapy: from Machine Learning Screening to In Vivo Efficacy (Adv. Mater. 52/2025)</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.71868?af=R</link><description>Advanced Materials, Volume 37, Issue 52, December 29, 2025.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Mon, 29 Dec 2025 19:50:02 GMT</pubDate><guid isPermaLink="true">10.1002/adma.71868</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Rational Design of Metal–Organic Frameworks for Pancreatic Cancer Therapy: from Machine Learning Screening to In Vivo Efficacy</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202412757?af=R</link><description>Advanced Materials, Volume 37, Issue 52, December 29, 2025.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Mon, 29 Dec 2025 19:50:02 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202412757</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] Protonation dynamics of confined ethanol–water mixtures in H-ZSM-5 from machine learning-driven metadynamics</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725007249?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Catalysis, Volume 454&lt;/p&gt;&lt;p&gt;Author(s): Princy Jarngal, Benjamin A. Jackson, Simuck F. Yuk, Difan Zhang, Mal-Soon Lee, Maria Cristina Menziani, Vassiliki-Alexandra Glezakou, Roger Rousseau, GiovanniMaria Piccini&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Mon, 29 Dec 2025 18:30:33 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725007249</guid></item><item><title>[Wiley: Small: Table of Contents] Enhancing Cycling Stability and Suppressing Lithium Dendrite Formation With A Hierarchical Artificial Solid Electrolyte Interphase Layer on Lithium Anodes for High‐Voltage Lithium Metal Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202512973?af=R</link><description>Small, EarlyView.</description><author>Wiley: Small: Table of Contents</author><pubDate>Mon, 29 Dec 2025 09:13:44 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202512973</guid></item><item><title>[Wiley: Small: Table of Contents] Elucidating the Sigmoidal Adsorption Behavior of Xenon in Flexible Hofmann‐Type MOFs Through Experiments and Molecular Dynamics with Machine Learning Potentials</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202509479?af=R</link><description>Small, EarlyView.</description><author>Wiley: Small: Table of Contents</author><pubDate>Mon, 29 Dec 2025 08:31:34 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202509479</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Gradient‐Heterojunction in Solid Electrolytes for Fast‐Charging Dendrite‐Free Solid‐State Lithium Metal Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202519284?af=R</link><description>Advanced Materials, EarlyView.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Mon, 29 Dec 2025 07:59:12 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202519284</guid></item><item><title>[cond-mat updates on arXiv.org] Upper bounds on the separation efficiency of diffusiophoresis</title><link>https://arxiv.org/abs/2512.21758</link><description>arXiv:2512.21758v1 Announce Type: new 
 Abstract: The separation of colloidal particles from fluids is essential to ensure a safe global supply of drinking water yet, in the case of microscopic particles, it remains a highly energy-intensive process when using traditional filtration methods. Water cleaning through diffusiophoresis $\unicode{x2014}$spontaneous colloid migration in chemical gradients$\unicode{x2014}$ effectively circumvents the need for physical filters, representing a promising alternative. This separation process is typically realized in internal flows where a cross-channel electrolyte gradient drives particle accumulation at walls, with colloid separation slowly increasing in the streamwise direction. However, the maximum separation efficiency, achieved sufficiently downstream as diffusiophoretic migration (driving particle accumulation) is balanced by Brownian motion (inducing diffusive spreading), has not yet been characterized. In this work, we develop a theory to predict this upper bound, and derive the colloid separation efficiency by analyzing the asymptotic structure of the governing equations. We find that the mechanism by which the chemical permeates in the channel, as well as the reaction kinetics governing its dissociation into ions, play key roles in the process. Moreover, we identify four distinct regimes in which separation is controlled by different scaling laws involving a Damk\"ohler and a P\'eclet number, which measure the ratio of reaction kinetics to ion diffusion and of diffusiophoresis to Brownian motion, respectively. We also confirm the scaling of one of these regimes using microfluidic experiments where separation is driven by CO$_\text{2}$ gradients. Our results shed light on pathways towards new, more efficient separations, and are also applicable to quantify colloidal accumulation in the presence of chemical gradients in more general situations.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 29 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.21758v1</guid></item><item><title>[cond-mat updates on arXiv.org] Temperature- and Pressure-Dependent Vibrational Properties and Phase Stability of Pristine and Sb-Doped Vacancy-Ordered Double Perovskite</title><link>https://arxiv.org/abs/2512.21810</link><description>arXiv:2512.21810v1 Announce Type: new 
 Abstract: Understanding lattice dynamics and structural transitions in vacancy-ordered double perovskites is crucial for developing lead-free optoelectronic materials, yet the role of dopants in modulating these properties remains poorly understood. We investigate Sb-doped Cs$_2$TiCl$_6$ through temperature-dependent Raman spectroscopy (4 to 273 K), high-pressure studies (0 to 30 GPa), powder XRD, and photoluminescence measurements. Sb doping dramatically improves phase purity, eliminating all impurity-related Raman modes present in pristine and Bi-doped samples while retaining only the three fundamental [TiCl$_6$]$^{2-}$ octahedral vibrations. This enhanced purity reveals a previously unobserved structural phenomenon: Sb-doped samples (2\% doped and 3\%) incorporated) exhibit a sharp anomaly at 100 K marked by the emergence of a new Raman mode M$_1$ at 314--319 cm$^{-1}$ and abrupt changes in the temperature coefficient $\chi$ (factor of 2--8$\times$ change) and anharmonic constant $A$ across this threshold. No such transition occurs in pristine Cs$_2$TiCl$_6$, indicating Sb-dopant-induced order-disorder transformation. The enhanced phonon anharmonicity in Sb-doped samples directly manifests in photoluminescence: self-trapped exciton emission at 448 nm shows 19\% broader FWHM (164.73 nm) compared to Bi-doped samples (138.2 nm), confirming stronger electron-phonon coupling. High-pressure measurements reveal structural robustness to 30 GPa with no phase transitions. These findings establish that strategic Sb doping not only improves material quality but also enables a novel low-temperature structural transition, providing fundamental insights into dopant-mediated phase control in vacancy-ordered perovskites for next-generation optoelectronic devices.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 29 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.21810v1</guid></item><item><title>[cond-mat updates on arXiv.org] Incorporating rank-free coupling and external field via an amplitude-only modulated spatial photonic Ising machine</title><link>https://arxiv.org/abs/2512.21587</link><description>arXiv:2512.21587v1 Announce Type: cross 
 Abstract: Ising machines have emerged as effective solvers for combinatorial optimization problems, such as NP-hard problems, machine learning, and financial modeling. Recent spatial photonic Ising machines (SPIMs) excel in multi-node optimization and spin glass simulations, leveraging their large-scale and fully connected characteristics. However, existing laser diffraction-based SPIMs usually sacrifice time efficiency or spin count to encode high-rank spin-spin coupling and external fields, limiting their scalability for real-world applications. Here, we demonstrate an amplitude-only modulated rank-free spatial photonic Ising machine (AR-SPIM) with 200 iterations per second. By re-formulating an arbitrary Ising Hamiltonian as the sum of Hadamard products, followed by loading the corresponding matrices/vectors onto an aligned amplitude spatial light modulator and digital micro-mirrors device, we directly map a 797-spin Ising model with external fields (nearly 9-bit precision, -255 to 255) into an incoherent light field, eliminating the need for repeated and auxiliary operations. Serving as encoding accuracy metrics, the linear coefficient of determination and Pearson correlation coefficient between measured light intensities and Ising Hamiltonians exceed 0.9800, with values exceed 0.9997 globally. The AR-SPIM achieves less than 0.3% error rate for ground-state search of biased Max-cut problems with arbitrary ranks and weights, enables complex phase transition observations, and facilitates scalable spin counts for sparse Ising problems via removing zero-valued Hadamard product terms. This reconfigurable AR-SPIM can be further developed to support large-scale machine-learning training and deployed for practical applications in discrete optimization and quantum many-body simulations.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 29 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.21587v1</guid></item><item><title>[cond-mat updates on arXiv.org] Accelerating Scientific Discovery with Autonomous Goal-evolving Agents</title><link>https://arxiv.org/abs/2512.21782</link><description>arXiv:2512.21782v1 Announce Type: cross 
 Abstract: There has been unprecedented interest in developing agents that expand the boundary of scientific discovery, primarily by optimizing quantitative objective functions specified by scientists. However, for grand challenges in science , these objectives are only imperfect proxies. We argue that automating objective function design is a central, yet unmet requirement for scientific discovery agents. In this work, we introduce the Scientific Autonomous Goal-evolving Agent (SAGA) to amend this challenge. SAGA employs a bi-level architecture in which an outer loop of LLM agents analyzes optimization outcomes, proposes new objectives, and converts them into computable scoring functions, while an inner loop performs solution optimization under the current objectives. This bi-level design enables systematic exploration of the space of objectives and their trade-offs, rather than treating them as fixed inputs. We demonstrate the framework through a broad spectrum of applications, including antibiotic design, inorganic materials design, functional DNA sequence design, and chemical process design, showing that automating objective formulation can substantially improve the effectiveness of scientific discovery agents.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 29 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.21782v1</guid></item><item><title>[cond-mat updates on arXiv.org] Correlated Terahertz phonon-ion interactions control ion conduction in a solid electrolyte</title><link>https://arxiv.org/abs/2305.01632</link><description>arXiv:2305.01632v4 Announce Type: replace 
 Abstract: Ionic conduction in solids that exceeds 1 mS/cm is predicted to involve coupled phonon-ion interactions in the crystal lattice. Here, we use theory and experiment to measure the possible contribution of coupled phonon-ion hopping modes which enhance Li+ migration in Li0.5La0.5TiO3 (LLTO). The ab initio calculations predict that the targeted excitation of individual TiO6 rocking modes greatly increases the Li+ jump rate as compared to the excitation of vibrational modes associated with heating. Experimentally, coherently driving TiO6 rocking modes via terahertz (THz) illumination leads to a ten-fold decrease in the differential impedance compared to the excitation of acoustic and optical phonons. Additionally, we differentiate the ultrafast responses of LLTO due to ultrafast heating and THz-range vibrations using laser-driven spectroscopy (LUIS), finding a unique long-lived response for the THz-range excitation. These findings provide new insights into coupled ion migration mechanisms, indicating the important role of THz-range coupled phonon-ion hopping modes in enabling fast ion conduction at room temperature.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 29 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2305.01632v4</guid></item><item><title>[cond-mat updates on arXiv.org] Fluctuation theorems with optical tweezers: theory and practice</title><link>https://arxiv.org/abs/2503.20894</link><description>arXiv:2503.20894v2 Announce Type: replace 
 Abstract: Fluctuation theorems, such as the Jarzynski equality and the Crooks relation, are effective tools connecting non-equilibrium work statistics and equilibrium free energy differences. However, detailed hands-on, reproducible protocols for implementing and analyzing these relations in real experiments remain scarce. This tutorial provides an end-to-end workflow for measuring, validating, and applying fluctuation theorems using a single-beam optical tweezers setup. It introduces the foundational ideas and consolidates practical calibration (PSD-based trap stiffness and position sensitivity), protocol design (forward/reverse finite-time drives over multiple amplitudes and durations), and robust estimators for free-energy difference and dissipated work, highlighting finite-sampling and rare-event effects. We demonstrate the procedures using an extensive set of measured trajectories under different conditions and provide openly accessible datasets and Python code, enabling new researchers or educators to reproduce the results with minimal effort. Beyond pedagogical validation, we discuss how these recipes translate to broader soft-matter and mesoscopic contexts. By combining user-friendly instruments with clear and transparent analysis, this work promotes the education and reliable adoption of stochastic thermodynamic methods in the curricula of physics and chemistry, as well as among emerging research teams.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 29 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2503.20894v2</guid></item><item><title>[cond-mat updates on arXiv.org] Rewards-based image analysis in microscopy</title><link>https://arxiv.org/abs/2502.18522</link><description>arXiv:2502.18522v2 Announce Type: replace-cross 
-Abstract: Imaging and hyperspectral data analysis is central to progress across biology, medicine, chemistry, and physics. The core challenge lies in converting high-resolution or high-dimensional datasets into interpretable representations that enable insight into the underlying physical or chemical properties of a system. Traditional analysis relies on expert-designed, multistep workflows, such as denoising, feature extraction, clustering, dimensionality reduction, and physics-based deconvolution, or on machine learning (ML) methods that accelerate individual steps. Both approaches, however, typically demand significant human intervention, including hyperparameter tuning and data labeling. Achieving the next level of autonomy in scientific imaging requires designing effective reward-based workflows that guide algorithms toward best data representation for human or automated decision-making. Here, we discuss recent advances in reward-based workflows for image analysis, which capture key elements of human reasoning and exhibit strong transferability across various tasks. We highlight how reward-driven approaches enable a shift from supervised black-box models toward explainable, unsupervised optimization on the examples of Scanning Probe and Electron Microscopies. Such reward-based frameworks are promising for a broad range of applications, including classification, regression, structure-property mapping, and general hyperspectral data processing.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 29 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2502.18522v2</guid></item><item><title>[ChemRxiv] A Protocol to Identify Large Language Model Use in Undergraduate Chemistry Essays</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-cz9pc?rft_dat=source%3Ddrss</link><description>Large language modules (LLMs) such as Chat-GPT have been widely adopted by chemistry undergraduate university students as a learning tool, but few methods exist to measure the scope of their influence on essay writing. This report introduces a protocol based on monitoring the frequency of specific words used excessively by LLMs amongst a sample of around 1000 chemistry student essays between 2018 and 2025. More than 50 key words known to be favored by LLMs were found to have simultaneously increased in frequency amongst essays submitted during the last two years. When the lists of key words were applied to specific essays, the findings indicated 13-29% of essays submitted during 2025 relied heavily on text generated by LLMs.  This protocol offers a simple method to generate an approximate scope of LLM uptake amongst a cohort, with potential applications in defining higher education AI policy and assessment strategies.</description><author>ChemRxiv</author><pubDate>Mon, 29 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-cz9pc?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Graph machine learning can estimate drug concentrations in whole blood from forensic screening results</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-lllcx?rft_dat=source%3Ddrss</link><description>LC-HRMS is widely used in forensic toxicology for broad-scope screening. When a newly emerging or rarely encountered compound is tentatively identified, toxicologists must decide whether it may be relevant to the case and, if so, quantify it. Acquiring reference material for quantification is costly and time-consuming. A fast semi-quantitative estimate would help prioritize only compounds above the toxic threshold. This study presents a machine-learning framework that estimates drug concentrations in whole blood using molecular structure information and LC-HRMS signal intensities. Using a dataset of 191 drugs spiked into whole blood at multiple concentration levels, we trained and evaluated several machine-learning models. Standard models, including random forests, achieved moderate performance. In contrast, a recently published graph neural network (GNN) leveraging atomic features and global molecular properties consistently produced the highest accuracy. Under cross-validation, the GNN predicted signal-to-concentration ratio for 79\% of all molecules, corresponding to concentration estimates between 50-200\% of true value. Toxicological thresholds often span multiple orders of magnitude, making this precision acceptable for application. The GNN model was additionally evaluated on an external benchmark dataset of ionization efficiencies (logIE), where it outperformed the current state of the art. Overall, the results demonstrate the feasibility of using graph-based machine learning to estimate drug concentrations in whole blood without reference material for prioritization. This is a practical and implementation-ready machine learning tool that can support decision-making in toxicological evaluation, particularly for newly emerging or rarely encountered drugs. The GNN model is open source and the dataset used for training and testing the models is publicly available.</description><author>ChemRxiv</author><pubDate>Mon, 29 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-lllcx?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Confidently uncertain: Probabilistic machine learning to predict soil biotransformation half-lives</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-xmslf?rft_dat=source%3Ddrss</link><description>Predicting environmental persistence of chemicals from molecular structure is an open challenge, yet indispensable in regulatory screenings for potentially harmful substances and to advance the development of safe-and-sustainable-by-design chemicals. Limited availability of biotransformation half-life data makes persistence prediction difficult, and models typically struggle to generalize beyond their training data. Therefore, reliable estimates of prediction confidence are key.  Here, we propose a probabilistic model for the prediction of soil biotransformation half-lives. A Gaussian Process Regressor was trained on 867 mean pesticide half-lives with data uncertainty estimates. Instead of single half-life values, our model predicts well-calibrated probability distributions that can be used to calculate a compound's probability of being persistent. Although the overall model performance remains moderate, the predictions are reliable when the confidence in the prediction is high.  We applied our model to pesticide transformation products with unknown half-lives, and to a database of globally marketed chemicals. We show that our model is able to identify chemicals that are known, or suspected to be, persistent in the environment. The model is available as an online app (https://pepper-app.streamlit.app/) and as a Python library (pepper-lab) to meet diverse user needs.</description><author>ChemRxiv</author><pubDate>Mon, 29 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-xmslf?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Characterizing PEDOT:PSS for Electronic Control of Stiffness</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-1qw4z?rft_dat=source%3Ddrss</link><description>Active stiffness, the changing of material stiffness in response to an external stimulus, can be harnessed for mechanically adaptive implantable devices and dynamic cell culture substrates for mechanobiology investigations. Conducting polymer (CP)-based materials are capable of changing stiffness in response to an applied electrical potential: redox-driven changes in charge state lead to ion transport and subsequent swelling. However, conducting polymers have seldom been investigated for this purpose. In this study, the stiffness of poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) films as a function of applied potential is characterized. Electrochemical preconditioning is first defined and the proportionality of ion transport to voltage is identified. The maximum stiffness change observed over the potential range was found to be ~32.5% and changes of ~6.7-10.4% were found with 0.2 V increments. PEDOT:PSS films deviate in both their charge state and stiffness over a period of many hours after unbiasing. After unbiasing, PEDOT:PSS loses the transported charge over time and the stiffness changes by ~2.6-15.2% over 24 hrs. Finally, to evaluate feasibility for biomedical applications, assays involving active stiffness modulation determine that the process is cytocompatible. These characterizations highlight both the potential of CPs for active stiffness and identify areas for future optimization.</description><author>ChemRxiv</author><pubDate>Mon, 29 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-1qw4z?rft_dat=source%3Ddrss</guid></item><item><title>[iScience] River plastic hotspot detection from space</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02831-7?rss=yes</link><description>Plastic pollution threatens terrestrial and aquatic ecosystems, and rivers play a central role in transporting and retaining plastics across landscapes. Effective mitigation requires scalable methods to identify riverine plastic accumulation hotspots. Here, we present a semi-automated, cloud-based pipeline that integrates satellite remote sensing and machine learning to detect river plastic hotspots. High-resolution PlanetScope imagery is used to annotate training regions, which are transferred to Sentinel-2 multispectral data to train Random Forest classifiers within Google Earth Engine.</description><author>iScience</author><pubDate>Mon, 29 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02831-7?rss=yes</guid></item><item><title>[APL Machine Learning Current Issue] Synthetic images from generative AI for compositional analysis of dried solution patterns</title><link>https://pubs.aip.org/aip/aml/article/3/4/046112/3375778/Synthetic-images-from-generative-AI-for</link><description>&lt;span class="paragraphSection"&gt;Image-based identification of solutes from dried drop stains offers a low-cost, scalable alternative to traditional chemical analysis, enabled by artificial intelligence and annotated image libraries. While recent advances in robotic droplet imaging have facilitated the collection of such datasets, experimental acquisition remains a key bottleneck. Here, we explore whether synthetic image generation can supplement or replace real data in the context of salt classification. Using Stable Diffusion with low-rank adaptation, we generate 133 000 synthetic images from as few as &lt;span style="font-style: italic;"&gt;N&lt;/span&gt; = 1–128 experimental images per salt across seven common inorganic solutes. Synthetic images become visually indistinguishable from real ones for &lt;span style="font-style: italic;"&gt;N&lt;/span&gt; ≥ 6; however, quantitative analysis based on 47 image metrics reveals subtle differences that vanish around &lt;span style="font-style: italic;"&gt;N&lt;/span&gt; = 128. When used to train random forest, XGBoost, and multilayer perceptron (MLP) models, synthetic data alone achieve classification accuracies of up to 90% in some low-data regimes. Synthetic augmentation offers a powerful strategy for expanding training sets and enabling image-based chemical classification in data-scarce scenarios, even if it seldom outperforms models trained on real data. The realism of synthetic images also highlights the growing need for tools that detect AI-generated scientific images to ensure data integrity.&lt;/span&gt;</description><author>APL Machine Learning Current Issue</author><pubDate>Mon, 29 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/046112/3375778/Synthetic-images-from-generative-AI-for</guid></item><item><title>[ChemRxiv] ChemTSv3: Generalizing Molecular Design via Flexible Search Space Control</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-kdvrt?rft_dat=source%3Ddrss</link><description>Recent advances in generative artificial intelligence have enabled in silico molecular design to become a powerful approach for exploring chemical space toward specific design goals across various domains. However, in actual design workflows, determining the appropriate generation conditions, including generative strategies and reward formulations, remains difficult; thus, trial-and-error adjustments are unavoidable. Yet, most existing generation methods implicitly fix the searchable chemical space defined by the molecular representation and generation method, which significantly limits the flexibility of practical design.  This paper introduces ChemTSv3, an exploration framework based on reinforcement learning with a flexible architecture that accommodates diverse design scenarios for adaptive molecular design.  Specifically, molecular representations are unified as nodes, enabling, for example, string-based encodings, molecular graphs, and protein sequences to be handled within the same logic.  Molecular generations and editing operations are abstracted as transitions between nodes, allowing classical graph-based modifications, sequential mutations, and even large-language-model-driven transformations to be handled within the same formulation. ChemTSv3 supports dynamic switching among molecular representations and transition types, which enables the search strategy itself to adapt to the stage and nature of the design task.   ChemTSv3 enables scalable molecular generation, from drug-like small molecules to proteins, and its switching capability supports realistic change in design scenarios while allowing efficient exploration.</description><author>ChemRxiv</author><pubDate>Sun, 28 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-kdvrt?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Machine learning the quantum topology of chemical bonds</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-zljl9?rft_dat=source%3Ddrss</link><description>The chemical bonding can be characterized within quantum chemical topology (QCT), which provides a real-space description via the topological analysis of the electron density and the electron localization function (ELF). While QCT has traditionally been applied on a molecule-by-molecule basis, recent advances in machine learning (ML) and the availability of large quantum chemical datasets now enable bonding analysis at scale. Here, we integrate ELF-based topological descriptors with ML to establish a data-driven framework for mapping chemical bonding across the QM9 dataset. Wavefunctions computed at the B3LYP/6-31G(2df,p) level were used to extract ELF basin populations, which were paired with geometric and bonding descriptors to construct a bond-level dataset. Statistical analysis revealed relationships between ELF populations, bond lengths, and local chemical environments. Regression models were trained to predict ELF electron populations directly from molecular geometry. The best performance was obtained when local environmental descriptors were included, reducing the prediction error by a factor of two relative to models using only bond type and bond length. These results demonstrate real-space bonding parameters, such as bond electron populations, can be predicted from simple structural features, enabling scalable and interpretable exploration of chemical bonding across large chemical spaces.</description><author>ChemRxiv</author><pubDate>Sun, 28 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-zljl9?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] StereoMolGraph: Stereochemistry-Aware Molecular and Reaction Graphs</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-0g4wn?rft_dat=source%3Ddrss</link><description>Conventional molecular graphs often are unable to reliably encode stereochemistry, especially for symmetric molecules, non-tetrahedral centers, and transition states. To overcome this, we present StereoMolGraph, an open source Python library implementing a stereochemistry-aware graph representation for molecules and condensed graphs of reactions. Our method uses permutation invariant local stereodescriptors, grounded in group theory, to provide an extensible representation of chirality. Based on this we introduce methods allowingfor robust comparison of stereoisomers, including the identification of enantiomerism and diastereomerism, and supports the of fleeting stereochemistry in transition states. We demonstrate the library’s utility for complex organic molecules and metal complexes and analysis of distinct chiral reaction pathways. With RDKit interoperability and visualization features, StereoMolGraph offers a practical and transparent tool for advanced stereochemically aware chemoinformatics workflows.</description><author>ChemRxiv</author><pubDate>Sun, 28 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-0g4wn?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] GPU-Accelerated Analytic Coulomb- and Exchange Gradients for Hartree Fock and Density Functional Theory</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-tt68b?rft_dat=source%3Ddrss</link><description>We present a GPU-accelerated software package for the evaluation of analytic two-electron energy and gradient contributions in Hartree-Fock (HF) and Density Functional theory (DFT) calculations. The implementation is provided as a Python library with a C++ backend, enabling straightforward integration into modern computational chemistry and drug-discovery workflows. The code supports single-point energy and nuclear gradient evaluations on both single- and multi-GPU systems, and employs MPI-based parallelization with dynamic load balancing in multi-node environments.  
+Abstract: Imaging and hyperspectral data analysis is central to progress across biology, medicine, chemistry, and physics. The core challenge lies in converting high-resolution or high-dimensional datasets into interpretable representations that enable insight into the underlying physical or chemical properties of a system. Traditional analysis relies on expert-designed, multistep workflows, such as denoising, feature extraction, clustering, dimensionality reduction, and physics-based deconvolution, or on machine learning (ML) methods that accelerate individual steps. Both approaches, however, typically demand significant human intervention, including hyperparameter tuning and data labeling. Achieving the next level of autonomy in scientific imaging requires designing effective reward-based workflows that guide algorithms toward best data representation for human or automated decision-making. Here, we discuss recent advances in reward-based workflows for image analysis, which capture key elements of human reasoning and exhibit strong transferability across various tasks. We highlight how reward-driven approaches enable a shift from supervised black-box models toward explainable, unsupervised optimization on the examples of Scanning Probe and Electron Microscopies. Such reward-based frameworks are promising for a broad range of applications, including classification, regression, structure-property mapping, and general hyperspectral data processing.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 29 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2502.18522v2</guid></item><item><title>[ChemRxiv] A Protocol to Identify Large Language Model Use in Undergraduate Chemistry Essays</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-cz9pc?rft_dat=source%3Ddrss</link><description>Large language modules (LLMs) such as Chat-GPT have been widely adopted by chemistry undergraduate university students as a learning tool, but few methods exist to measure the scope of their influence on essay writing. This report introduces a protocol based on monitoring the frequency of specific words used excessively by LLMs amongst a sample of around 1000 chemistry student essays between 2018 and 2025. More than 50 key words known to be favored by LLMs were found to have simultaneously increased in frequency amongst essays submitted during the last two years. When the lists of key words were applied to specific essays, the findings indicated 13-29% of essays submitted during 2025 relied heavily on text generated by LLMs.  This protocol offers a simple method to generate an approximate scope of LLM uptake amongst a cohort, with potential applications in defining higher education AI policy and assessment strategies.</description><author>ChemRxiv</author><pubDate>Mon, 29 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-cz9pc?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Graph machine learning can estimate drug concentrations in whole blood from forensic screening results</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-lllcx?rft_dat=source%3Ddrss</link><description>LC-HRMS is widely used in forensic toxicology for broad-scope screening. When a newly emerging or rarely encountered compound is tentatively identified, toxicologists must decide whether it may be relevant to the case and, if so, quantify it. Acquiring reference material for quantification is costly and time-consuming. A fast semi-quantitative estimate would help prioritize only compounds above the toxic threshold. This study presents a machine-learning framework that estimates drug concentrations in whole blood using molecular structure information and LC-HRMS signal intensities. Using a dataset of 191 drugs spiked into whole blood at multiple concentration levels, we trained and evaluated several machine-learning models. Standard models, including random forests, achieved moderate performance. In contrast, a recently published graph neural network (GNN) leveraging atomic features and global molecular properties consistently produced the highest accuracy. Under cross-validation, the GNN predicted signal-to-concentration ratio for 79\% of all molecules, corresponding to concentration estimates between 50-200\% of true value. Toxicological thresholds often span multiple orders of magnitude, making this precision acceptable for application. The GNN model was additionally evaluated on an external benchmark dataset of ionization efficiencies (logIE), where it outperformed the current state of the art. Overall, the results demonstrate the feasibility of using graph-based machine learning to estimate drug concentrations in whole blood without reference material for prioritization. This is a practical and implementation-ready machine learning tool that can support decision-making in toxicological evaluation, particularly for newly emerging or rarely encountered drugs. The GNN model is open source and the dataset used for training and testing the models is publicly available.</description><author>ChemRxiv</author><pubDate>Mon, 29 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-lllcx?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Confidently uncertain: Probabilistic machine learning to predict soil biotransformation half-lives</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-xmslf?rft_dat=source%3Ddrss</link><description>Predicting environmental persistence of chemicals from molecular structure is an open challenge, yet indispensable in regulatory screenings for potentially harmful substances and to advance the development of safe-and-sustainable-by-design chemicals. Limited availability of biotransformation half-life data makes persistence prediction difficult, and models typically struggle to generalize beyond their training data. Therefore, reliable estimates of prediction confidence are key.  Here, we propose a probabilistic model for the prediction of soil biotransformation half-lives. A Gaussian Process Regressor was trained on 867 mean pesticide half-lives with data uncertainty estimates. Instead of single half-life values, our model predicts well-calibrated probability distributions that can be used to calculate a compound's probability of being persistent. Although the overall model performance remains moderate, the predictions are reliable when the confidence in the prediction is high.  We applied our model to pesticide transformation products with unknown half-lives, and to a database of globally marketed chemicals. We show that our model is able to identify chemicals that are known, or suspected to be, persistent in the environment. The model is available as an online app (https://pepper-app.streamlit.app/) and as a Python library (pepper-lab) to meet diverse user needs.</description><author>ChemRxiv</author><pubDate>Mon, 29 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-xmslf?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Characterizing PEDOT:PSS for Electronic Control of Stiffness</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-1qw4z?rft_dat=source%3Ddrss</link><description>Active stiffness, the changing of material stiffness in response to an external stimulus, can be harnessed for mechanically adaptive implantable devices and dynamic cell culture substrates for mechanobiology investigations. Conducting polymer (CP)-based materials are capable of changing stiffness in response to an applied electrical potential: redox-driven changes in charge state lead to ion transport and subsequent swelling. However, conducting polymers have seldom been investigated for this purpose. In this study, the stiffness of poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) films as a function of applied potential is characterized. Electrochemical preconditioning is first defined and the proportionality of ion transport to voltage is identified. The maximum stiffness change observed over the potential range was found to be ~32.5% and changes of ~6.7-10.4% were found with 0.2 V increments. PEDOT:PSS films deviate in both their charge state and stiffness over a period of many hours after unbiasing. After unbiasing, PEDOT:PSS loses the transported charge over time and the stiffness changes by ~2.6-15.2% over 24 hrs. Finally, to evaluate feasibility for biomedical applications, assays involving active stiffness modulation determine that the process is cytocompatible. These characterizations highlight both the potential of CPs for active stiffness and identify areas for future optimization.</description><author>ChemRxiv</author><pubDate>Mon, 29 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-1qw4z?rft_dat=source%3Ddrss</guid></item><item><title>[iScience] River plastic hotspot detection from space</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02831-7?rss=yes</link><description>Plastic pollution threatens terrestrial and aquatic ecosystems, and rivers play a central role in transporting and retaining plastics across landscapes. Effective mitigation requires scalable methods to identify riverine plastic accumulation hotspots. Here, we present a semi-automated, cloud-based pipeline that integrates satellite remote sensing and machine learning to detect river plastic hotspots. High-resolution PlanetScope imagery is used to annotate training regions, which are transferred to Sentinel-2 multispectral data to train Random Forest classifiers within Google Earth Engine.</description><author>iScience</author><pubDate>Mon, 29 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02831-7?rss=yes</guid></item><item><title>[APL Machine Learning Current Issue] Synthetic images from generative AI for compositional analysis of dried solution patterns</title><link>https://pubs.aip.org/aip/aml/article/3/4/046112/3375778/Synthetic-images-from-generative-AI-for</link><description>&lt;span class="paragraphSection"&gt;Image-based identification of solutes from dried drop stains offers a low-cost, scalable alternative to traditional chemical analysis, enabled by artificial intelligence and annotated image libraries. While recent advances in robotic droplet imaging have facilitated the collection of such datasets, experimental acquisition remains a key bottleneck. Here, we explore whether synthetic image generation can supplement or replace real data in the context of salt classification. Using Stable Diffusion with low-rank adaptation, we generate 133 000 synthetic images from as few as &lt;span style="font-style: italic;"&gt;N&lt;/span&gt; = 1–128 experimental images per salt across seven common inorganic solutes. Synthetic images become visually indistinguishable from real ones for &lt;span style="font-style: italic;"&gt;N&lt;/span&gt; ≥ 6; however, quantitative analysis based on 47 image metrics reveals subtle differences that vanish around &lt;span style="font-style: italic;"&gt;N&lt;/span&gt; = 128. When used to train random forest, XGBoost, and multilayer perceptron (MLP) models, synthetic data alone achieve classification accuracies of up to 90% in some low-data regimes. Synthetic augmentation offers a powerful strategy for expanding training sets and enabling image-based chemical classification in data-scarce scenarios, even if it seldom outperforms models trained on real data. The realism of synthetic images also highlights the growing need for tools that detect AI-generated scientific images to ensure data integrity.&lt;/span&gt;</description><author>APL Machine Learning Current Issue</author><pubDate>Mon, 29 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/046112/3375778/Synthetic-images-from-generative-AI-for</guid></item><item><title>[iScience] An Interpretable Machine Learning Model for Predicting the Effectiveness of Low-Dose Methylprednisolone in Treating Long COVID: A Retrospective Study</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02835-4?rss=yes</link><description>Long COVID is a chronic, multisystem disease with limited response to conventional treatments. While low-dose methylprednisolone has shown effectiveness in some patients, individual responses vary, and accurate predictive tools are lacking. This retrospective study included 330 Long COVID patients who received low-dose methylprednisolone treatment across three hospitals. Patients were divided into training (n=202), test (n=33), and external validation sets (n=53, n=42). Using Least Absolute Shrinkage and Selection Operator (LASSO) regression, 38 variables were analyzed to develop six machine learning models.</description><author>iScience</author><pubDate>Mon, 29 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02835-4?rss=yes</guid></item><item><title>[ChemRxiv] ChemTSv3: Generalizing Molecular Design via Flexible Search Space Control</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-kdvrt?rft_dat=source%3Ddrss</link><description>Recent advances in generative artificial intelligence have enabled in silico molecular design to become a powerful approach for exploring chemical space toward specific design goals across various domains. However, in actual design workflows, determining the appropriate generation conditions, including generative strategies and reward formulations, remains difficult; thus, trial-and-error adjustments are unavoidable. Yet, most existing generation methods implicitly fix the searchable chemical space defined by the molecular representation and generation method, which significantly limits the flexibility of practical design.  This paper introduces ChemTSv3, an exploration framework based on reinforcement learning with a flexible architecture that accommodates diverse design scenarios for adaptive molecular design.  Specifically, molecular representations are unified as nodes, enabling, for example, string-based encodings, molecular graphs, and protein sequences to be handled within the same logic.  Molecular generations and editing operations are abstracted as transitions between nodes, allowing classical graph-based modifications, sequential mutations, and even large-language-model-driven transformations to be handled within the same formulation. ChemTSv3 supports dynamic switching among molecular representations and transition types, which enables the search strategy itself to adapt to the stage and nature of the design task.   ChemTSv3 enables scalable molecular generation, from drug-like small molecules to proteins, and its switching capability supports realistic change in design scenarios while allowing efficient exploration.</description><author>ChemRxiv</author><pubDate>Sun, 28 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-kdvrt?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Machine learning the quantum topology of chemical bonds</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-zljl9?rft_dat=source%3Ddrss</link><description>The chemical bonding can be characterized within quantum chemical topology (QCT), which provides a real-space description via the topological analysis of the electron density and the electron localization function (ELF). While QCT has traditionally been applied on a molecule-by-molecule basis, recent advances in machine learning (ML) and the availability of large quantum chemical datasets now enable bonding analysis at scale. Here, we integrate ELF-based topological descriptors with ML to establish a data-driven framework for mapping chemical bonding across the QM9 dataset. Wavefunctions computed at the B3LYP/6-31G(2df,p) level were used to extract ELF basin populations, which were paired with geometric and bonding descriptors to construct a bond-level dataset. Statistical analysis revealed relationships between ELF populations, bond lengths, and local chemical environments. Regression models were trained to predict ELF electron populations directly from molecular geometry. The best performance was obtained when local environmental descriptors were included, reducing the prediction error by a factor of two relative to models using only bond type and bond length. These results demonstrate real-space bonding parameters, such as bond electron populations, can be predicted from simple structural features, enabling scalable and interpretable exploration of chemical bonding across large chemical spaces.</description><author>ChemRxiv</author><pubDate>Sun, 28 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-zljl9?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] StereoMolGraph: Stereochemistry-Aware Molecular and Reaction Graphs</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-0g4wn?rft_dat=source%3Ddrss</link><description>Conventional molecular graphs often are unable to reliably encode stereochemistry, especially for symmetric molecules, non-tetrahedral centers, and transition states. To overcome this, we present StereoMolGraph, an open source Python library implementing a stereochemistry-aware graph representation for molecules and condensed graphs of reactions. Our method uses permutation invariant local stereodescriptors, grounded in group theory, to provide an extensible representation of chirality. Based on this we introduce methods allowingfor robust comparison of stereoisomers, including the identification of enantiomerism and diastereomerism, and supports the of fleeting stereochemistry in transition states. We demonstrate the library’s utility for complex organic molecules and metal complexes and analysis of distinct chiral reaction pathways. With RDKit interoperability and visualization features, StereoMolGraph offers a practical and transparent tool for advanced stereochemically aware chemoinformatics workflows.</description><author>ChemRxiv</author><pubDate>Sun, 28 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-0g4wn?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] GPU-Accelerated Analytic Coulomb- and Exchange Gradients for Hartree Fock and Density Functional Theory</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-tt68b?rft_dat=source%3Ddrss</link><description>We present a GPU-accelerated software package for the evaluation of analytic two-electron energy and gradient contributions in Hartree-Fock (HF) and Density Functional theory (DFT) calculations. The implementation is provided as a Python library with a C++ backend, enabling straightforward integration into modern computational chemistry and drug-discovery workflows. The code supports single-point energy and nuclear gradient evaluations on both single- and multi-GPU systems, and employs MPI-based parallelization with dynamic load balancing in multi-node environments.  
 We report comprehensive benchmarks demonstrating favorable scaling with respect to system size, as well as high throughput for batched evaluations relevant to molecular dynamics, geometry optimization, and large-scale virtual screening. Parallel execution of a single system was carried out on up to 24 A100 GPUs. The implementation builds on optimized GPU-enabled variants of the LibintX and GauXC libraries to efficiently compute density-fitted Coulomb, semi-numerical exchange, and exchange--correlation contributions.</description><author>ChemRxiv</author><pubDate>Sun, 28 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-tt68b?rft_dat=source%3Ddrss</guid></item><item><title>[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Machine Learning−Accelerated Discovery of Proton-Conducting 2D Materials for Proton Exchange Membranes</title><link>http://dx.doi.org/10.1021/acsnano.5c15161</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsnano.5c15161/asset/images/medium/nn5c15161_0006.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;ACS Nano&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acsnano.5c15161&lt;/div&gt;</description><author>ACS Nano: Latest Articles (ACS Publications)</author><pubDate>Sat, 27 Dec 2025 14:37:43 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsnano.5c15161</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] An enhanced machine learning and computational screening framework for synthesizable single-phase high-entropy spinel oxides</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625008110?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 263&lt;/p&gt;&lt;p&gt;Author(s): Mahalaxmi Chandramohan, Hridhya Vinod, Meenal Deo&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Fri, 26 Dec 2025 18:29:22 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625008110</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Anomalous temperature evolution of lattice anharmonicity and thermal transport in orthorhombic SnSe</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325003608?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 26 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics&lt;/p&gt;&lt;p&gt;Author(s): Tianxiang Jiang, Wujie Qiu, Haijuan Zhang, Jifen Wang, Kunpeng Zhao, Huaqing Xie&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Fri, 26 Dec 2025 18:29:17 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325003608</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Machine learning aided bandgap and defect engineering of mixed halide perovskites for photovoltaic applications</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325003591?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 26 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics&lt;/p&gt;&lt;p&gt;Author(s): Ayush Kumar Pandey, Vivek Pandey, Abhishek Tewari&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Fri, 26 Dec 2025 18:29:17 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325003591</guid></item><item><title>[Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)] [ASAP] Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Data set Generation for Training Machine-Learned Interatomic Potentials</title><link>http://dx.doi.org/10.1021/acs.jctc.5c01610</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jctc.5c01610/asset/images/medium/ct5c01610_0010.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;Journal of Chemical Theory and Computation&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acs.jctc.5c01610&lt;/div&gt;</description><author>Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)</author><pubDate>Fri, 26 Dec 2025 18:25:53 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jctc.5c01610</guid></item><item><title>[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Machine Learning Photodynamics Unveils a Controlled H2 Loss Channel in the Methaniminium Cation</title><link>http://dx.doi.org/10.1021/acs.jpclett.5c03196</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpclett.5c03196/asset/images/medium/jz5c03196_0006.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;The Journal of Physical Chemistry Letters&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acs.jpclett.5c03196&lt;/div&gt;</description><author>The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)</author><pubDate>Fri, 26 Dec 2025 17:51:53 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpclett.5c03196</guid></item><item><title>[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Solvent Structure and Dynamics Controlled Memristive Ion Transport in Å-Scale Channels</title><link>http://dx.doi.org/10.1021/acs.jpclett.5c03397</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpclett.5c03397/asset/images/medium/jz5c03397_0005.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;The Journal of Physical Chemistry Letters&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acs.jpclett.5c03397&lt;/div&gt;</description><author>The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)</author><pubDate>Fri, 26 Dec 2025 16:50:38 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpclett.5c03397</guid></item><item><title>[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Design of Open Ion Transport Channels in Thick Electrodes through Directional Freezing-Assisted 3D Printing: Enhancing the Electrochemical Performance of High-Load Electrodes</title><link>http://dx.doi.org/10.1021/acs.jpclett.5c02968</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpclett.5c02968/asset/images/medium/jz5c02968_0007.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;The Journal of Physical Chemistry Letters&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acs.jpclett.5c02968&lt;/div&gt;</description><author>The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)</author><pubDate>Fri, 26 Dec 2025 16:49:57 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpclett.5c02968</guid></item><item><title>[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Machine Learning Analysis of Dimensionality Effects on Bandgap Prediction</title><link>http://dx.doi.org/10.1021/acs.jpcc.5c05232</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpcc.5c05232/asset/images/medium/jp5c05232_0011.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;The Journal of Physical Chemistry C&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acs.jpcc.5c05232&lt;/div&gt;</description><author>The Journal of Physical Chemistry C: Latest Articles (ACS Publications)</author><pubDate>Fri, 26 Dec 2025 16:06:02 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpcc.5c05232</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Plasma Design of Alloy‐Based Gradient Solid Electrolyte Interphase on Lithium Metal Anodes for Energy Storage</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202521029?af=R</link><description>Advanced Materials, EarlyView.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Fri, 26 Dec 2025 14:02:31 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202521029</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Pixelation‐Free, Monolithic Iontronic Pressure Sensors Enabling Large‐Area Simultaneous Pressure and Position Recognition via Machine Learning</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202527178?af=R</link><description>Advanced Functional Materials, EarlyView.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Fri, 26 Dec 2025 14:01:16 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202527178</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Machine Learning‐Enhanced Smart Interactive Glove Utilizing Flexible Gradient Ridge Architecture Iontronic Capacitive Sensor</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202529907?af=R</link><description>Advanced Functional Materials, EarlyView.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Fri, 26 Dec 2025 09:52:42 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202529907</guid></item><item><title>[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Machine Learning-Accelerated Development of Li-Rich Layered Oxides with High Reversible Capacity and Coulombic Efficiency</title><link>http://dx.doi.org/10.1021/acsnano.5c16117</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsnano.5c16117/asset/images/medium/nn5c16117_0006.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;ACS Nano&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acsnano.5c16117&lt;/div&gt;</description><author>ACS Nano: Latest Articles (ACS Publications)</author><pubDate>Fri, 26 Dec 2025 09:21:05 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsnano.5c16117</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Mendelian Randomization and Double Machine Learning Modeling Reveal Brain Imaging‐Derived Phenotypes as Functional Contributors to 18 Autoimmune Inflammatory Diseases</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202515675?af=R</link><description>Advanced Science, EarlyView.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Fri, 26 Dec 2025 06:23:13 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202515675</guid></item><item><title>[Wiley: Carbon Energy: Table of Contents] Data‐Driven Design of Scalable Perovskite Film Fabrication via Machine Learning–Guided Processing</title><link>https://onlinelibrary.wiley.com/doi/10.1002/cey2.70164?af=R</link><description>Carbon Energy, EarlyView.</description><author>Wiley: Carbon Energy: Table of Contents</author><pubDate>Fri, 26 Dec 2025 06:22:51 GMT</pubDate><guid isPermaLink="true">10.1002/cey2.70164</guid></item><item><title>[Nature Communications] Inferring fine-grained migration patterns across the United States</title><link>https://www.nature.com/articles/s41467-025-68019-2</link><description>&lt;p&gt;Nature Communications, Published online: 26 December 2025; &lt;a href="https://www.nature.com/articles/s41467-025-68019-2"&gt;doi:10.1038/s41467-025-68019-2&lt;/a&gt;&lt;/p&gt;This study releases a very high-resolution migration dataset that reveals trends that shape daily life: rising moves into high-income neighborhoods, racial gaps in upward mobility, and wildfire-driven moves.</description><author>Nature Communications</author><pubDate>Fri, 26 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41467-025-68019-2</guid></item><item><title>[Communications Materials] Machine learning interatomic potential reveals hydrogen embrittlement origins at general grain boundaries in α-iron</title><link>https://www.nature.com/articles/s43246-025-01042-4</link><description>&lt;p&gt;Communications Materials, Published online: 26 December 2025; &lt;a href="https://www.nature.com/articles/s43246-025-01042-4"&gt;doi:10.1038/s43246-025-01042-4&lt;/a&gt;&lt;/p&gt;Hydrogen embrittlement is an issue that alloys used in the energy sector must overcome. Here, a machine learning interatomic potential for iron-hydrogen is reported, with large-scale molecular dynamics simulations revealing that hydrogen can suppress &gt;111 &lt; /2 dislocation emission at grain boundaries.</description><author>Communications Materials</author><pubDate>Fri, 26 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s43246-025-01042-4</guid></item><item><title>[iScience] Entropy-based byte patching transformer for self-supervised pretraining of SMILES data</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02816-0?rss=yes</link><description>Efficient molecular representations are critical for improving the performance and generalization of large language models in chemical learning. Transformer-based architectures have advanced molecular representation learning, yet capturing localized and hierarchical chemical structures remains challenging. We introduce the SMILES Byte-Patch Transformer (SMiBPT), an adaptive model that dynamically segments SMILES and DeepSMILES strings into chemically meaningful substructures through entropy-based byte patching.</description><author>iScience</author><pubDate>Fri, 26 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02816-0?rss=yes</guid></item><item><title>[ScienceDirect Publication: Solid State Ionics] Anion engineering enhances the electrochemical performance of Li&lt;sub&gt;7&lt;/sub&gt;P&lt;sub&gt;3&lt;/sub&gt;S&lt;sub&gt;11&lt;/sub&gt; solid electrolyte</title><link>https://www.sciencedirect.com/science/article/pii/S0167273825003236?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 15 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Solid State Ionics, Volume 435&lt;/p&gt;&lt;p&gt;Author(s): D. Narsimulu, Ramkumar Balasubramaniam, Kwang-Sun Ryu&lt;/p&gt;</description><author>ScienceDirect Publication: Solid State Ionics</author><pubDate>Thu, 25 Dec 2025 18:28:52 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0167273825003236</guid></item><item><title>[ScienceDirect Publication: Science Bulletin] Machine learning for spectral precision: a new horizon in radiative cooling material design</title><link>https://www.sciencedirect.com/science/article/pii/S2095927325011235?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 30 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Science Bulletin, Volume 70, Issue 24&lt;/p&gt;&lt;p&gt;Author(s): Xinpeng Hu, Mingxiang Liu, Xuemei Fu, Guangming Tao, Xiang Lu, Jinping Qu&lt;/p&gt;</description><author>ScienceDirect Publication: Science Bulletin</author><pubDate>Thu, 25 Dec 2025 18:28:50 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2095927325011235</guid></item><item><title>[cond-mat updates on arXiv.org] Towards predictive atomistic simulations of SiC crystal growth</title><link>https://arxiv.org/abs/2512.20804</link><description>arXiv:2512.20804v1 Announce Type: new 
 Abstract: Simulations of SiC crystal growth using molecular dynamics (MD) have become popular in recent years. They, however, simulate very fast deposition rates, to reduce computational costs. Therefore, they are more akin to surface sputtering, leading to abnormal growth effects, including thick amorphous layers and large defect densities. A recently developed method, called the minimum energy atomic deposition (MEAD), tries to overcome this problem by depositing the atoms directly at the minimum energy positions, increasing the time scale.
   We apply the MEAD method to simulate SiC crystal growth on stepped C-terminated 4H substrates with 4{\deg} and 8{\deg} off-cut angle. We explore relevant calculations settings, such as amount of equilibration steps between depositions and influence of simulation cell sizes and bench mark different interatomic potentials. The carefully calibrated methodology is able to replicate the stable step-flow growth, which was so far not possible using conventional MD simulations. Furthermore, the simulated crystals are evaluated in terms of their dislocations, surface roughness and atom mobility. Our methodology paves the way for future high fidelity investigations of surface phenomena in crystal growth.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 25 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.20804v1</guid></item><item><title>[cond-mat updates on arXiv.org] Emergence of Friedel-like oscillations from Lorenz dynamics in walking droplets</title><link>https://arxiv.org/abs/2512.21049</link><description>arXiv:2512.21049v1 Announce Type: new