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-<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/"><channel><title>My Customized Papers</title><link>https://github.com/your_username/your_repo</link><description>Aggregated research papers</description><language>en-US</language><lastBuildDate>Sat, 10 Jan 2026 18:28:28 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[ChemRxiv] ConforFormer: representation for molecules through understanding of conformers</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-x68vd-v2?rft_dat=source%3Ddrss</link><description>Recent years have seen a growing interest in machine learning approaches for chemical tasks. The best existing methods focus on building base models that combine molecular graphs (“2D structures”) with atomic coordinates in 3D to predict molecular properties, typically through pre-training followed by fine-tuning on benchmark datasets. However, current approaches require updating the weights of the entire model during the fine-tuning procedure for each prediction task. While this enables state-of-the-art performance, it limits practical deployment, as real-world datasets are often too small to support the stable retraining of large models. Importantly, the 3D geometry of a molecule holds crucial information for predicting its properties, but a single molecular graph usually corresponds to several 3D geometries, called conformers, introducing ambiguity into the inference process. Typical solutions rely on molecular graphs, but this approach is not easily generalizable beyond organic molecules. Here, we present ConforFormer, a method that explicitly accounts for the diversity of 3D conformations of a molecule to derive a task-agnostic and conformation-agnostic vector representation. This model serves as a foundational framework, producing embeddings that can be generated once and directly applied to downstream tasks, including property prediction and structural similarity, without extensive fine-tuning.</description><author>ChemRxiv</author><pubDate>Sat, 10 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-x68vd-v2?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Graph learning of sequence statistics for polymer representation</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-tb0td?rft_dat=source%3Ddrss</link><description>Polymers underpin critical technologies from medicine to energy, but their immense chemical and structural diversity makes rational design exceptionally difficult. Machine learning offers a way to navigate this space, yet prevailing approaches inherit small-molecule representations that fail to encode polymer-specific architecture; the distinction between random, block and other statistical copolymers is often collapsed into a categorical tag or ignored. Here, we introduce SCALE (Statistical Copolymer Architecture with Learning Edges), which recasts a copolymer as a Markovian sequence over a monomer alphabet and embeds the transition probabilities P(j/i) as edge features within a graph attention network. Message passing thus computes contextualized monomer states analogous to applying a transfer operator along the chain, while attention learns a data-driven kernel over paths that weights sequence heterogeneity versus block persistence. On a robotically synthesized, high-throughput fluorescence library, SCALE attained RMSE ≈228 and R² ≈0.84, surpassing polymer-adapted neural baselines and descriptor regressors (e.g., wDMPNN RMSE ≈326; XGBoost RMSE ≈254). The model is interpretable: edges dominate predictions for statistical (random) copolymers, whereas nodes prevail for block copolymers, consistent with NOESY 2D NMR. Beyond photophysics, SCALE generalized to antibacterial design across penta- and hexa-copolymer libraries with validation from &lt;300 syntheses. By elevating sequence statistics to first-class learning variables, SCALE provides a generalizable, data-efficient route to closed-loop polymer discovery.</description><author>ChemRxiv</author><pubDate>Sat, 10 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-tb0td?rft_dat=source%3Ddrss</guid></item><item><title>[Wiley: Small Structures: Table of Contents] Dielectric Constant Guided Solvation Structure Design for Stable Solid Electrolyte Interphase in Lithium Metal Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/sstr.202500760?af=R</link><description>Small Structures, Volume 7, Issue 1, January 2026.</description><author>Wiley: Small Structures: Table of Contents</author><pubDate>Fri, 09 Jan 2026 19:05:13 GMT</pubDate><guid isPermaLink="true">10.1002/sstr.202500760</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] External pressure's influence on lithium-ion transport within solid-state electrolytes</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X26001180?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 March 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 149&lt;/p&gt;&lt;p&gt;Author(s): Yuan He, Xiongying Zhang, Dong Lu&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Fri, 09 Jan 2026 18:31:35 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X26001180</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Alterative aqueous polymer anode binder enabling interfacial stabilization and improved lithium-ion transport</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X26000423?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 March 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 149&lt;/p&gt;&lt;p&gt;Author(s): Yuan Peng, Huimin Chen, Xiaowen Qv, Ao Zeng, Jianfeng Xia, Jiangtao Xu, Kunkun Guo&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Fri, 09 Jan 2026 18:31:35 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X26000423</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Interface engineering of Li&lt;sub&gt;1.5&lt;/sub&gt;Al&lt;sub&gt;0.5&lt;/sub&gt;Ge&lt;sub&gt;1.5&lt;/sub&gt;(PO&lt;sub&gt;4&lt;/sub&gt;)&lt;sub&gt;3&lt;/sub&gt; electrolytes via in-situ polymer–cerium hybrid interlayers for high-performance all-solid-state lithium metal batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25047759?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 March 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 149&lt;/p&gt;&lt;p&gt;Author(s): Kaiqi Wu, Chengjin Peng, Fanglin Wu, Liyuan Huang, Liang Lan, Liqiang Kang, Yecheng Liu, Xin Ao, Shan Fang&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Fri, 09 Jan 2026 18:31:35 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25047759</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] A general LLM-powered text mining framework: Applied to extract high entropy alloys</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625008195?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: October 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 264&lt;/p&gt;&lt;p&gt;Author(s): Haolun Yuan, Jun Zeng, Jie Zuo, Xin Wang, Dingguo Xu&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Fri, 09 Jan 2026 18:31:33 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625008195</guid></item><item><title>[ScienceDirect Publication: Materials Today] Heteropolyanion regulation activating decoupled ion transition for Na superionic conductors</title><link>https://www.sciencedirect.com/science/article/pii/S1369702125005450?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 9 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today&lt;/p&gt;&lt;p&gt;Author(s): Tian Jiang, Qi Fan, Wenshan Gou, Anyang Yu, Changhao Zhu, Ruirui Zhang, Youwei Dong, Shijun Yuan, Qingyu Xu&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today</author><pubDate>Fri, 09 Jan 2026 18:31:29 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1369702125005450</guid></item><item><title>[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Correlating (Chemo-)Mechanical Coupling in TiS2 during Li+ Intercalation across Liquid and Solid Electrolytes Via Operando Analysis</title><link>http://dx.doi.org/10.1021/acsenergylett.5c02943</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsenergylett.5c02943/asset/images/medium/nz5c02943_0006.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;ACS Energy Letters&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acsenergylett.5c02943&lt;/div&gt;</description><author>ACS Energy Letters: Latest Articles (ACS Publications)</author><pubDate>Fri, 09 Jan 2026 16:22:26 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsenergylett.5c02943</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Harnessing Entropic Effects from Interlayer Coupling to Modulate Ion Transport and Rectification in Multilayered Janus Graphene Nanopores</title><link>http://dx.doi.org/10.1021/jacs.5c17242</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c17242/asset/images/medium/ja5c17242_0006.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;Journal of the American Chemical Society&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/jacs.5c17242&lt;/div&gt;</description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Fri, 09 Jan 2026 12:51:38 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c17242</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Uncertainty‐Quantified Primary Particle Size Prediction in Li‐Rich NCM Materials via Machine Learning and Chemistry‐Aware Imputation</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202515694?af=R</link><description>Advanced Science, Volume 13, Issue 2, 9 January 2026.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Fri, 09 Jan 2026 11:35:15 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202515694</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Highly Selective CO2 Reduction to Pure Formic Acid Using a Nafion‐TiO2 Composite Porous Solid Electrolyte</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202515967?af=R</link><description>Advanced Science, Volume 13, Issue 2, 9 January 2026.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Fri, 09 Jan 2026 11:35:15 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202515967</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Macroscopically Ordered Piezo‐Potential in All‐Polymetric Solid Electrolytes Responding to Li Anode Volume Changes for Dendrites Suppression</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202509897?af=R</link><description>Advanced Science, Volume 13, Issue 2, 9 January 2026.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Fri, 09 Jan 2026 11:35:15 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202509897</guid></item><item><title>[Recent Articles in Phys. Rev. B] Reinforcement fine-tuning for materials design</title><link>http://link.aps.org/doi/10.1103/45zh-44bg</link><description>Author(s): Zhendong Cao and Lei Wang&lt;br /&gt;&lt;p&gt;Reinforcement fine-tuning played an instrumental role in enhancing the instruction-following and reasoning abilities of large language models. In this work, we employ reinforcement fine-tuning for materials design, in which discriminative machine learning models are used to provide rewards to the au…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 113, 024106] Published Fri Jan 09, 2026</description><author>Recent Articles in Phys. Rev. B</author><pubDate>Fri, 09 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/45zh-44bg</guid></item><item><title>[cond-mat updates on arXiv.org] Fluctuation conductivity in ultraclean multicomponent superconductors</title><link>https://arxiv.org/abs/2601.04308</link><description>arXiv:2601.04308v1 Announce Type: new 
+<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/"><channel><title>My Customized Papers</title><link>https://github.com/your_username/your_repo</link><description>Aggregated research papers</description><language>en-US</language><lastBuildDate>Sun, 11 Jan 2026 01:51:07 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[ScienceDirect Publication: Joule] A critical outlook for large-scale all-solid-state batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2542435125004507?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 9 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Joule&lt;/p&gt;&lt;p&gt;Author(s): Seongjae Ko, Makoto Ue, Atsuo Yamada&lt;/p&gt;</description><author>ScienceDirect Publication: Joule</author><pubDate>Sun, 11 Jan 2026 01:50:45 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125004507</guid></item><item><title>[ChemRxiv] ConforFormer: representation for molecules through understanding of conformers</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-x68vd-v2?rft_dat=source%3Ddrss</link><description>Recent years have seen a growing interest in machine learning approaches for chemical tasks. The best existing methods focus on building base models that combine molecular graphs (“2D structures”) with atomic coordinates in 3D to predict molecular properties, typically through pre-training followed by fine-tuning on benchmark datasets. However, current approaches require updating the weights of the entire model during the fine-tuning procedure for each prediction task. While this enables state-of-the-art performance, it limits practical deployment, as real-world datasets are often too small to support the stable retraining of large models. Importantly, the 3D geometry of a molecule holds crucial information for predicting its properties, but a single molecular graph usually corresponds to several 3D geometries, called conformers, introducing ambiguity into the inference process. Typical solutions rely on molecular graphs, but this approach is not easily generalizable beyond organic molecules. Here, we present ConforFormer, a method that explicitly accounts for the diversity of 3D conformations of a molecule to derive a task-agnostic and conformation-agnostic vector representation. This model serves as a foundational framework, producing embeddings that can be generated once and directly applied to downstream tasks, including property prediction and structural similarity, without extensive fine-tuning.</description><author>ChemRxiv</author><pubDate>Sat, 10 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-x68vd-v2?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Graph learning of sequence statistics for polymer representation</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-tb0td?rft_dat=source%3Ddrss</link><description>Polymers underpin critical technologies from medicine to energy, but their immense chemical and structural diversity makes rational design exceptionally difficult. Machine learning offers a way to navigate this space, yet prevailing approaches inherit small-molecule representations that fail to encode polymer-specific architecture; the distinction between random, block and other statistical copolymers is often collapsed into a categorical tag or ignored. Here, we introduce SCALE (Statistical Copolymer Architecture with Learning Edges), which recasts a copolymer as a Markovian sequence over a monomer alphabet and embeds the transition probabilities P(j/i) as edge features within a graph attention network. Message passing thus computes contextualized monomer states analogous to applying a transfer operator along the chain, while attention learns a data-driven kernel over paths that weights sequence heterogeneity versus block persistence. On a robotically synthesized, high-throughput fluorescence library, SCALE attained RMSE ≈228 and R² ≈0.84, surpassing polymer-adapted neural baselines and descriptor regressors (e.g., wDMPNN RMSE ≈326; XGBoost RMSE ≈254). The model is interpretable: edges dominate predictions for statistical (random) copolymers, whereas nodes prevail for block copolymers, consistent with NOESY 2D NMR. Beyond photophysics, SCALE generalized to antibacterial design across penta- and hexa-copolymer libraries with validation from &lt;300 syntheses. By elevating sequence statistics to first-class learning variables, SCALE provides a generalizable, data-efficient route to closed-loop polymer discovery.</description><author>ChemRxiv</author><pubDate>Sat, 10 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-tb0td?rft_dat=source%3Ddrss</guid></item><item><title>[npj Computational Materials] Machine learning for phase prediction of high entropy carbide ceramics from imbalanced data</title><link>https://www.nature.com/articles/s41524-025-01873-2</link><description>&lt;p&gt;npj Computational Materials, Published online: 10 January 2026; &lt;a href="https://www.nature.com/articles/s41524-025-01873-2"&gt;doi:10.1038/s41524-025-01873-2&lt;/a&gt;&lt;/p&gt;Machine learning for phase prediction of high entropy carbide ceramics from imbalanced data</description><author>npj Computational Materials</author><pubDate>Sat, 10 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41524-025-01873-2</guid></item><item><title>[Wiley: Small Structures: Table of Contents] Dielectric Constant Guided Solvation Structure Design for Stable Solid Electrolyte Interphase in Lithium Metal Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/sstr.202500760?af=R</link><description>Small Structures, Volume 7, Issue 1, January 2026.</description><author>Wiley: Small Structures: Table of Contents</author><pubDate>Fri, 09 Jan 2026 19:05:13 GMT</pubDate><guid isPermaLink="true">10.1002/sstr.202500760</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] External pressure's influence on lithium-ion transport within solid-state electrolytes</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X26001180?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 March 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 149&lt;/p&gt;&lt;p&gt;Author(s): Yuan He, Xiongying Zhang, Dong Lu&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Fri, 09 Jan 2026 18:31:35 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X26001180</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Alterative aqueous polymer anode binder enabling interfacial stabilization and improved lithium-ion transport</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X26000423?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 March 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 149&lt;/p&gt;&lt;p&gt;Author(s): Yuan Peng, Huimin Chen, Xiaowen Qv, Ao Zeng, Jianfeng Xia, Jiangtao Xu, Kunkun Guo&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Fri, 09 Jan 2026 18:31:35 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X26000423</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Interface engineering of Li&lt;sub&gt;1.5&lt;/sub&gt;Al&lt;sub&gt;0.5&lt;/sub&gt;Ge&lt;sub&gt;1.5&lt;/sub&gt;(PO&lt;sub&gt;4&lt;/sub&gt;)&lt;sub&gt;3&lt;/sub&gt; electrolytes via in-situ polymer–cerium hybrid interlayers for high-performance all-solid-state lithium metal batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25047759?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 March 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 149&lt;/p&gt;&lt;p&gt;Author(s): Kaiqi Wu, Chengjin Peng, Fanglin Wu, Liyuan Huang, Liang Lan, Liqiang Kang, Yecheng Liu, Xin Ao, Shan Fang&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Fri, 09 Jan 2026 18:31:35 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25047759</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] A general LLM-powered text mining framework: Applied to extract high entropy alloys</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625008195?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: October 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 264&lt;/p&gt;&lt;p&gt;Author(s): Haolun Yuan, Jun Zeng, Jie Zuo, Xin Wang, Dingguo Xu&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Fri, 09 Jan 2026 18:31:33 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625008195</guid></item><item><title>[ScienceDirect Publication: Materials Today] Heteropolyanion regulation activating decoupled ion transition for Na superionic conductors</title><link>https://www.sciencedirect.com/science/article/pii/S1369702125005450?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 9 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today&lt;/p&gt;&lt;p&gt;Author(s): Tian Jiang, Qi Fan, Wenshan Gou, Anyang Yu, Changhao Zhu, Ruirui Zhang, Youwei Dong, Shijun Yuan, Qingyu Xu&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today</author><pubDate>Fri, 09 Jan 2026 18:31:29 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1369702125005450</guid></item><item><title>[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Correlating (Chemo-)Mechanical Coupling in TiS2 during Li+ Intercalation across Liquid and Solid Electrolytes Via Operando Analysis</title><link>http://dx.doi.org/10.1021/acsenergylett.5c02943</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsenergylett.5c02943/asset/images/medium/nz5c02943_0006.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;ACS Energy Letters&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acsenergylett.5c02943&lt;/div&gt;</description><author>ACS Energy Letters: Latest Articles (ACS Publications)</author><pubDate>Fri, 09 Jan 2026 16:22:26 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsenergylett.5c02943</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Harnessing Entropic Effects from Interlayer Coupling to Modulate Ion Transport and Rectification in Multilayered Janus Graphene Nanopores</title><link>http://dx.doi.org/10.1021/jacs.5c17242</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c17242/asset/images/medium/ja5c17242_0006.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;Journal of the American Chemical Society&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/jacs.5c17242&lt;/div&gt;</description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Fri, 09 Jan 2026 12:51:38 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c17242</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Uncertainty‐Quantified Primary Particle Size Prediction in Li‐Rich NCM Materials via Machine Learning and Chemistry‐Aware Imputation</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202515694?af=R</link><description>Advanced Science, Volume 13, Issue 2, 9 January 2026.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Fri, 09 Jan 2026 11:35:15 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202515694</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Highly Selective CO2 Reduction to Pure Formic Acid Using a Nafion‐TiO2 Composite Porous Solid Electrolyte</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202515967?af=R</link><description>Advanced Science, Volume 13, Issue 2, 9 January 2026.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Fri, 09 Jan 2026 11:35:15 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202515967</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Macroscopically Ordered Piezo‐Potential in All‐Polymetric Solid Electrolytes Responding to Li Anode Volume Changes for Dendrites Suppression</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202509897?af=R</link><description>Advanced Science, Volume 13, Issue 2, 9 January 2026.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Fri, 09 Jan 2026 11:35:15 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202509897</guid></item><item><title>[Recent Articles in Phys. Rev. B] Reinforcement fine-tuning for materials design</title><link>http://link.aps.org/doi/10.1103/45zh-44bg</link><description>Author(s): Zhendong Cao and Lei Wang&lt;br /&gt;&lt;p&gt;Reinforcement fine-tuning played an instrumental role in enhancing the instruction-following and reasoning abilities of large language models. In this work, we employ reinforcement fine-tuning for materials design, in which discriminative machine learning models are used to provide rewards to the au…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 113, 024106] Published Fri Jan 09, 2026</description><author>Recent Articles in Phys. Rev. B</author><pubDate>Fri, 09 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/45zh-44bg</guid></item><item><title>[cond-mat updates on arXiv.org] Fluctuation conductivity in ultraclean multicomponent superconductors</title><link>https://arxiv.org/abs/2601.04308</link><description>arXiv:2601.04308v1 Announce Type: new 
 Abstract: We consider the intrinsic fluctuation conductivity in metals with multiply sheeted Fermi surfaces approaching a superconducting critical point. Restricting our attention to extreme type-II multicomponent superconductors motivates focusing on the ultraclean limit. Using functional-integral techniques, we derive the Gaussian fluctuation action from which we obtain the gauge-invariant electromagnetic linear response kernel. This allows us to compute the optical conductivity tensor. We identify essential conditions required for a nonzero longitudinal conductivity at finite frequencies in a disorder-free and translationally invariant system. Specifically, this is neither related to impurity scattering nor electron-phonon interaction, but derives indirectly from the multicomponent character of the incipient superconducting order and the parent metallic state. Under these conditions, the enhancement of the DC conductivity due to fluctuations close to the critical point follows the same critical behaviour as in the diffusive limit.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.04308v1</guid></item><item><title>[cond-mat updates on arXiv.org] Towards understanding the defect properties in the multivalent A-site Na$_{0.5}$Bi$_{0.5}$TiO$_3$-based perovskite ceramics</title><link>https://arxiv.org/abs/2601.04725</link><description>arXiv:2601.04725v1 Announce Type: new 
 Abstract: A defect model involving cation and anion vacancies and anti-site defects is proposed that accounts for the non-stoichiometry of multi-valent $A$-site Na$_{0.5}$Bi$_{0.5}$TiO$_3$ based perovskite oxides with $ABO_3$ composition. A series of samples with varying $A$-site non-stoichiometry and $A$:$B$ ratios were prepared to investigate their electrical conductivity. The oxygen partial pressure and temperature dependent conductivities where studied with direct current (dc) and alternating current (ac) techniques, enabling to separate between ionic and electronic conduction. The Na-excess samples, regardless of the $A$:$B$ ratio, exhibit dominant ionic conductivity and $p$-type electronic conduction, with the highest total conductivity reaching $4 \times 10^{-4}$ S/cm at 450$^\circ$C. In contrast, the Bi-excess samples display more insulating characteristics and $n$-type electronic conductivity, with conductivity values within the 10$^{-8}$ S/cm range at 450$^\circ$C. These conductivity results strongly support the proposed defect model, which offers a straightforward description of defect chemistry in NBT-based ceramics and serves as a valuable guide for optimizing sample processing to achieve tailored properties.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.04725v1</guid></item><item><title>[cond-mat updates on arXiv.org] Scalable Dielectric Tensor Predictions for Inorganic Materials using Equivariant Graph Neural Networks</title><link>https://arxiv.org/abs/2601.04755</link><description>arXiv:2601.04755v1 Announce Type: new 
 Abstract: Accurate prediction of dielectric tensors is essential for accelerating the discovery of next-generation inorganic dielectric materials. Existing machine learning approaches, such as equivariant graph neural networks, typically rely on specially-designed network architectures to enforce O(3) equivariance. However, to preserve equivariance, these specially-designed models restrict the update of equivariant features during message passing to linear transformations or gated equivariant nonlinearities. The inability to implicitly characterize more complex nonlinear structures may reduce the predictive accuracy of the model. In this study, we introduce a frame-averaging-based approach to achieve equivariant dielectric tensor prediction. We propose GoeCTP, an O(3)-equivariant framework that predicts dielectric tensors without imposing any structural restrictions on the backbone network. We benchmark its performance against several state-of-the-art models and further employ it for large-scale virtual screening of thermodynamically stable materials from the Materials Project database. GoeCTP successfully identifies various promising candidates, such as Zr(InBr$_3$)$_2$ (band gap $E_g = 2.41$ eV, dielectric constant $\overline{\varepsilon} = 194.72$) and SeI$_2$ (anisotropy ratio $\alpha_r = 96.763$), demonstrating its accuracy and efficiency in accelerating the discovery of advanced inorganic dielectric materials.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.04755v1</guid></item><item><title>[cond-mat updates on arXiv.org] Lateral Graphene-Metallene Interfaces at the Nanoscale</title><link>https://arxiv.org/abs/2601.04838</link><description>arXiv:2601.04838v1 Announce Type: new