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-<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>Sun, 28 Dec 2025 12:39:33 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><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>[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 
+<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>Sun, 28 Dec 2025 18:29:00 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><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 
 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 experiments 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 statistics can also arise from a purely local, dynamical mechanism, which we elucidate using a minimal Lorenz-like model of a walking droplet. In this model, a localized defect perturbs the particle's internal dynamical state, generating underdamped velocity oscillations that give rise to oscillatory ensemble position statistics. This attractor-driven, local mechanism opens new avenues for hydrodynamic quantum analogs based on active particles with internal degrees of freedom.</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:2512.21049v1</guid></item><item><title>[cond-mat updates on arXiv.org] From cluster to nanocrystal: the continuous evolution and critical size of copper clusters revealed by machine learning</title><link>https://arxiv.org/abs/2512.21067</link><description>arXiv:2512.21067v1 Announce Type: new