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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, 23 Dec 2025 12:42:46 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[Wiley: Advanced Materials: Table of Contents] Defect‐Driven Ionic Trap Construction and Interface Modulation for Rapid Li+ Kinetics in Composite Solid Electrolytes</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202519541?af=R</link><description>Advanced Materials, EarlyView.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Tue, 23 Dec 2025 08:43:35 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202519541</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] A Smart Self‐Immobilization Magnetic Resonance Contrast Agent for Delayed Tumor Imaging In Vivo</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202516998?af=R</link><description>Angewandte Chemie International Edition, Volume 64, Issue 52, December 22, 2025.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Tue, 23 Dec 2025 08:21:09 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202516998</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Iron/NHC‐Catalyzed Regio‐ and Stereoselective 1,6‐Additions of Aliphatic Grignard Reagents to α,β,γ,δ‐Unsaturated Carbonyl Compounds: Asymmetric Variants with Chiral NHCs</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202518346?af=R</link><description>Angewandte Chemie International Edition, Volume 64, Issue 52, December 22, 2025.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Tue, 23 Dec 2025 08:21:09 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202518346</guid></item><item><title>[Wiley: Small: Table of Contents] Supersaturation‐Driven Co‐Precipitation Enables Scalable Wet‐Chemical Synthesis of High‐Purity Na3InCl6 Solid Electrolyte for Sodium‐Ion Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202509165?af=R</link><description>Small, Volume 21, Issue 51, December 23, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Tue, 23 Dec 2025 07:06:10 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202509165</guid></item><item><title>[Wiley: Small: Table of Contents] Synergistic Co‐Optimization Strategy for Electron‐Ion Transport Kinetics in all‐Solid‐State Sulfurized Polyacrylonitrile Cathodes</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202507810?af=R</link><description>Small, Volume 21, Issue 51, December 23, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Tue, 23 Dec 2025 07:06:10 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202507810</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] Defect-reconstructed carbon nitride nanosheets for sacrificial agent-free H&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;2&lt;/sub&gt; photosynthesis coupled with biomass-derived polyols valorization</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725007006?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): Yanmei Zheng, Qiang Xu, Jianghong Ouyang, Ziwei Hang, Jingde Li, Xinli Guo, Zupeng Chen&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Tue, 23 Dec 2025 06:33:51 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725007006</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] A novel kind of activating agents for preparation of high specific surface area porous carbon towards zinc‑iodine batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25045542?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 145&lt;/p&gt;&lt;p&gt;Author(s): Yun Zhou, Boyang Liu, Xuejin Chen, Junchen Chen, Chenglong Li, Xulai Xiao&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 23 Dec 2025 06:33:34 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25045542</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] A molecular simulation study to the effect of T313 bonding agent and crystal defects on the performance of ammonium perchlorate oxidizer</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625006913?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 30 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 262&lt;/p&gt;&lt;p&gt;Author(s): Bin Yuan, Rui Zhu, Jianfa Chen, Kuai He&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 06:33:32 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625006913</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] Mobility and sintering of silica-supported platinum clusters via reactive neural network potentials</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725005998?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Catalysis, Volume 453&lt;/p&gt;&lt;p&gt;Author(s): Tereza Benešová, Kristýna Pokorná, Andreas Erlebach, Christopher J. Heard&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Tue, 23 Dec 2025 05:56:09 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725005998</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] Machine learning–assisted discovery of chromium bis(2-pyridyl)amine catalysts for ethylene tri-/tetramerization</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725006797?dgcid=rss_sd_all</link><description>&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): Youcai Zhu, Yue Mu, Xiaoke Shi, Shu Yang, Li Sun, Zhen Liu&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Tue, 23 Dec 2025 05:56:09 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725006797</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] The influence of the organic residue and the solvent in the Schlenk equilibrium for Grignard reagents in THF. A molecular dynamics study with machine learning potentials</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725006852?dgcid=rss_sd_all</link><description>&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): Marco Bortoli, Sigbjørn Løland Bore, Odile Eisenstein, Michele Cascella&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Tue, 23 Dec 2025 05:56:09 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725006852</guid></item><item><title>[ScienceDirect Publication: Acta Materialia] DuctGPT: A Generative Transformer for Forward Screening of Ductile Refractory Multi-Principal Element Alloys</title><link>https://www.sciencedirect.com/science/article/pii/S135964542501050X?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Acta Materialia, Volume 304&lt;/p&gt;&lt;p&gt;Author(s): Sai Pranav Reddy Guduru, Mkpe O. 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Ott, Sougata Roy, Prashant Singh&lt;/p&gt;</description><author>ScienceDirect Publication: Acta Materialia</author><pubDate>Tue, 23 Dec 2025 05:56:09 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S135964542501050X</guid></item><item><title>[ScienceDirect Publication: Acta Materialia] On-the-fly machine learning of interatomic potentials for elastic property modeling in Al–Mg–Zr solid solutions</title><link>https://www.sciencedirect.com/science/article/pii/S1359645425011310?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 15 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Acta Materialia, Volume 305&lt;/p&gt;&lt;p&gt;Author(s): Lukas Volkmer, Leonardo Medrano Sandonas, Philip Grimm, Julia Kristin Hufenbach, Gianaurelio Cuniberti&lt;/p&gt;</description><author>ScienceDirect Publication: Acta Materialia</author><pubDate>Tue, 23 Dec 2025 05:56:09 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1359645425011310</guid></item><item><title>[ScienceDirect Publication: Journal of Materiomics] Data-augmented machine learning models for oxynitride glasses &lt;em&gt;via&lt;/em&gt; Wasserstein generative adversarial network with gradient penalty and content constraint</title><link>https://www.sciencedirect.com/science/article/pii/S2352847825001017?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 8 August 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Materiomics&lt;/p&gt;&lt;p&gt;Author(s): Jing Tian, Yuan Li, Min Guan, Jijie Zheng, Jingyuan Chu, Yong Liu, Gaorong Han&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Materiomics</author><pubDate>Tue, 23 Dec 2025 05:55:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352847825001017</guid></item><item><title>[ScienceDirect Publication: Journal of Materiomics] Machine learning assisted &lt;em&gt;τ&lt;/em&gt;&lt;sub&gt;f&lt;/sub&gt; value prediction of ABO&lt;sub&gt;3&lt;/sub&gt;-type microwave dielectric ceramics</title><link>https://www.sciencedirect.com/science/article/pii/S2352847825001078?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 8 August 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Materiomics&lt;/p&gt;&lt;p&gt;Author(s): Mingyue Yang, Liangyu Mo, Jincheng Qin, Faqiang Zhang, Mingsheng Ma, Yongxiang Li, Zhifu Liu&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Materiomics</author><pubDate>Tue, 23 Dec 2025 05:55:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352847825001078</guid></item><item><title>[ScienceDirect Publication: Journal of Materiomics] Domain knowledge-assisted materials data anomaly detection towards constructing high-performance machine learning models</title><link>https://www.sciencedirect.com/science/article/pii/S2352847825000565?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: November 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Materiomics, Volume 11, Issue 6&lt;/p&gt;&lt;p&gt;Author(s): Yue Liu, Shuchang Ma, Zhengwei Yang, Duo Wu, Yali Zhao, Maxim Avdeev, Siqi Shi&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Materiomics</author><pubDate>Tue, 23 Dec 2025 05:55:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352847825000565</guid></item><item><title>[ScienceDirect Publication: Journal of Materiomics] PTCDA/CuS cathode enabling stable sulfide-based all-solid-state batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352847825000814?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Materiomics, Volume 12, Issue 1&lt;/p&gt;&lt;p&gt;Author(s): Zhixing Wan, Shuo Wang, Yahao Mu, Ruihua Zhou, Hang Liu, Tingwu Jin, Di Wu, Jianlong Xia, Ce-Wen Nan&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Materiomics</author><pubDate>Tue, 23 Dec 2025 05:55:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352847825000814</guid></item><item><title>[ScienceDirect Publication: Current Opinion in Solid State and Materials Science] Solid electrolyte-driven suppression of H2–H3 phase transition in Ni-rich cathodes for stable high-voltage cycling</title><link>https://www.sciencedirect.com/science/article/pii/S1359028625000324?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Current Opinion in Solid State and Materials Science, Volume 39&lt;/p&gt;&lt;p&gt;Author(s): Hao Chen, Hsiao-Hsuan Wu, Chia-Chen Li&lt;/p&gt;</description><author>ScienceDirect Publication: Current Opinion in Solid State and Materials Science</author><pubDate>Tue, 23 Dec 2025 05:55:53 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1359028625000324</guid></item><item><title>[ScienceDirect Publication: Current Opinion in Solid State and Materials Science] State-of-the-art review of additive friction stir deposition: microstructural evolution, machine learning applications, and future directions</title><link>https://www.sciencedirect.com/science/article/pii/S1359028625000300?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Current Opinion in Solid State and Materials Science, Volume 40&lt;/p&gt;&lt;p&gt;Author(s): Ashish Kumar, Lei Shi, Virendra Pratap Singh, Sudipta Mohapatra, Long Li, Chuansong Wu, Sergey Mironov, Amitava De&lt;/p&gt;</description><author>ScienceDirect Publication: Current Opinion in Solid State and Materials Science</author><pubDate>Tue, 23 Dec 2025 05:55:53 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1359028625000300</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Transfer learning-enhanced hybrid deep neural network model for accurate lithium-ion batteries health estimation in electric vehicles</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25045451?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 145&lt;/p&gt;&lt;p&gt;Author(s): Ibrahim AL-Wesabi, Hassan M. Hussein Farh, Abdullrahman A. Al-Shamma'a&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 23 Dec 2025 05:55:53 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25045451</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] UV-curable montmorillonite-enhanced gel polymer electrolyte for dendrite-free, enhanced ion transport, and flexible Zn metal batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25046353?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 145&lt;/p&gt;&lt;p&gt;Author(s): Eunyoung Jung, Gaeun Lee, Byeongjun Kim, Chanwoo Park, Yujin Nam, Jong-Seong Bae, Ji Hyeon Kim, Yong Nam Ahn, Jaehyun Hur&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 23 Dec 2025 05:55:53 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25046353</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Mechanically reinforced graphene heterostructure anodes co-optimizing ultrafast ion diffusion and high storage capacity for Li/Na-ion batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25046298?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 145&lt;/p&gt;&lt;p&gt;Author(s): Houda Khattab, Hamza Bekkali, Abdelilah Benyoussef, Abdallah El Kenz, Omar Mounkachi&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 23 Dec 2025 05:55:53 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25046298</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Thermal diffusivity and conductivity of sulfide and oxide solid electrolytes: Effects of densification and microstructural evolution</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25046407?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 145&lt;/p&gt;&lt;p&gt;Author(s): Hayoung Lee, Yuto Seki, Atsuro Okumura, Manabu Kodama&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 23 Dec 2025 05:55:53 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25046407</guid></item><item><title>[ScienceDirect Publication: Solid State Ionics] Crossover from insulating into solid electrolyte behavior in bulk CaSO&lt;sub&gt;4&lt;/sub&gt;⋅0.5H&lt;sub&gt;2&lt;/sub&gt;O material due to ion exchange processes induced by high-temperature treatment with orthophosphoric acid</title><link>https://www.sciencedirect.com/science/article/pii/S0167273825003170?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Solid State Ionics, Volume 434&lt;/p&gt;&lt;p&gt;Author(s): Ivan Nikulin, Tatiana Nikulicheva, Vitaly Vyazmin, Oleg Ivanov, Nikita Anosov, Olga Telpova&lt;/p&gt;</description><author>ScienceDirect Publication: Solid State Ionics</author><pubDate>Tue, 23 Dec 2025 05:55:51 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0167273825003170</guid></item><item><title>[ScienceDirect Publication: Solid State Ionics] First-principles elucidation of the effects of Al-doping on Li-ion diffusion in LiCoO&lt;sub&gt;2&lt;/sub&gt;</title><link>https://www.sciencedirect.com/science/article/pii/S0167273825003224?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Solid State Ionics, Volume 434&lt;/p&gt;&lt;p&gt;Author(s): Jordan A. Barr, Scott P. Beckman, Brandon C. Wood, Liwen F. Wan&lt;/p&gt;</description><author>ScienceDirect Publication: Solid State Ionics</author><pubDate>Tue, 23 Dec 2025 05:55:51 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0167273825003224</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Machine learning-driven design of polyimides with tailored dielectric constants</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007025?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 30 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 262&lt;/p&gt;&lt;p&gt;Author(s): Rongrong Zheng, Boyang Liang, Wenjia Huo, Xiang Wu, Yaoyao Bai&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 05:55:51 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007025</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] MAPAL: A python library for mapping features and properties of alloys over compositional spaces</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007037?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 30 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 262&lt;/p&gt;&lt;p&gt;Author(s): Dishant Beniwal, Pratik K. Ray&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 05:55:51 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007037</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Machine learning assisted local descriptors predicate oxygen reduction activity of transition metal@Ti&lt;sub&gt;1−&lt;em&gt;x&lt;/em&gt;&lt;/sub&gt;Zn&lt;sub&gt;&lt;em&gt;x&lt;/em&gt;&lt;/sub&gt; alloys</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625006883?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 30 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 262&lt;/p&gt;&lt;p&gt;Author(s): Tian-Zhe Wan, Shou-Heng Guo, Guang-Qiang Yu, Jun-Zhe Li, Ya-Nan Zhu, Xi-Bo Li&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 05:55:51 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625006883</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] PyVUMAT: A package to develop and deploy machine learning material models in finite element analysis simulations</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007207?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 30 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 262&lt;/p&gt;&lt;p&gt;Author(s): Joshua C. Crone&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 05:55:51 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007207</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Predicting hydrogen storage capacity of metal hydrides using novel imputation techniques and tree-based machine learning models</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007335?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 30 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 262&lt;/p&gt;&lt;p&gt;Author(s): Zaid Allal, Hassan N. Noura, Flavien Vernier, Ola Salman, Khaled Chahine&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 05:55:51 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007335</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Accelerating magnetic materials discovery using interaction matrix-based machine learning descriptors</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007384?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 30 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 262&lt;/p&gt;&lt;p&gt;Author(s): Apoorv Verma, Junaid Jami, Amrita Bhattacharya&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 05:55:51 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007384</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Effects of training machine-learning potentials for radiation damage simulations using different pseudopotentials</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007414?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 30 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 262&lt;/p&gt;&lt;p&gt;Author(s): A. 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Panda, Pan Du, Xin-Yang Liu, Jasmine Liang, Ben-Chi Ma, Jian-Xun Wang, Tengfei Luo&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 05:55:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325002780</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Machine-learning potentials for quantum and anharmonic effects in superconducting &lt;math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg" class="math"&gt;&lt;mrow&gt;&lt;mi mathvariant="bold-italic"&gt;F&lt;/mi&gt;&lt;mi mathvariant="bold-italic"&gt;m&lt;/mi&gt;&lt;mover accent="true"&gt;&lt;mn mathvariant="bold"&gt;3&lt;/mn&gt;&lt;mo&gt;‾&lt;/mo&gt;&lt;/mover&gt;&lt;mi mathvariant="bold-italic"&gt;m&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt; LaBeH&lt;sub&gt;8&lt;/sub&gt;</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325002950?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics, Volume 59&lt;/p&gt;&lt;p&gt;Author(s): Guiyan Dong, Tian Cui, Zihao Huo, Zhengtao Liu, Wenxuan Chen, Pugeng Hou, Yue-Wen Fang, Defang Duan&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 05:55:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325002950</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] A computational framework for interface design using lattice matching, machine learning potentials, and active learning: A case study on LaCoO&lt;sub&gt;3&lt;/sub&gt;/La&lt;sub&gt;2&lt;/sub&gt;NiO&lt;sub&gt;4&lt;/sub&gt;</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325002962?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics, Volume 59&lt;/p&gt;&lt;p&gt;Author(s): Guangchen Liu, Songge Yang, Yu Zhong&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 05:55:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325002962</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Thermal expansion prediction in oxide glasses via graph neural networks with temperature-encoded virtual nodes</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325002998?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics, Volume 59&lt;/p&gt;&lt;p&gt;Author(s): Huang Ming, Xiong Jingxian, Peng Yongqian, Mao Haijun, Liu Zhuofeng, Li Wei, Wang Fenglin, Zhang Weijun, Chen Xingyu&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 05:55:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325002998</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Tackling dataset curation challenges towards reliable machine learning: a case study on thermoelectric materials</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325003049?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics, Volume 59&lt;/p&gt;&lt;p&gt;Author(s): Shoeb Athar, Adrien Mecibah, Philippe Jund&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 05:55:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325003049</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Research progress of machine learning in flexible strain sensors in the context of material intelligence</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325002883?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics, Volume 59&lt;/p&gt;&lt;p&gt;Author(s): Jie Li, Zhe Li, Yan Lu, Gang Ye, Yan Hong, Li Niu, Jian Fang&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 05:55:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325002883</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] A physics-informed machine learning framework for unified prediction of superconducting transition temperatures</title><link>https://www.sciencedirect.com/science/article/pii/S254252932500327X?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics, Volume 60&lt;/p&gt;&lt;p&gt;Author(s): Ehsan Alibagheri, Mohammad Sandoghchi, Alireza Seyfi, Mohammad Khazaei, S. Mehdi Vaez Allaei&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 05:55:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S254252932500327X</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Revisiting thermoelectric transport in 122 Zintl phases: Anharmonic phonon renormalization and phonon localization effects</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325003359?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics, Volume 60&lt;/p&gt;&lt;p&gt;Author(s): Zhenguo Wang, Yinchang Zhao, Jun Ni, Zhenhong Dai&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 05:55:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325003359</guid></item><item><title>[ScienceDirect Publication: Materials Today] A facile construction of LiF interlayer and F-doping &lt;em&gt;via&lt;/em&gt; PECVD for LATP-based hybrid electrolytes: Enhanced Li-ion transport kinetics and superior lithium metal compatibility</title><link>https://www.sciencedirect.com/science/article/pii/S1369702125004249?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today, Volume 91&lt;/p&gt;&lt;p&gt;Author(s): Xian-Ao Li, Yiwei Xu, Kepin Zhu, Yang Wang, Ziqi Zhao, Shengwei Dong, Bin Wu, Hua Huo, Shuaifeng Lou, Xinhui Xia, Xin Liu, Minghua Chen, Stefano Passerini, Zhen Chen&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today</author><pubDate>Tue, 23 Dec 2025 05:55:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1369702125004249</guid></item><item><title>[ScienceDirect Publication: Materials Today] Revitalizing multifunctionality of Li-Al-O system enabling mother-powder-free sintering of garnet-type solid electrolytes</title><link>https://www.sciencedirect.com/science/article/pii/S1369702125005139?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 10 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today&lt;/p&gt;&lt;p&gt;Author(s): Hwa-Jung Kim, Jong Hoon Kim, Minseo Choi, Jung Hyun Kim, Hosun Shin, Ki Chang Kwon, Sun Hwa Park, Hyun Min Park, Seokhee Lee, Young Heon Kim, Hyeokjun Park, Seung-Wook Baek&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today</author><pubDate>Tue, 23 Dec 2025 05:55:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1369702125005139</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Monoclinic Li&lt;sub&gt;2&lt;/sub&gt;ZrO&lt;sub&gt;3&lt;/sub&gt; with cationic vacancy–based ion transport channels enhanced composite polymer electrolytes for high-rate solid-state lithium metal batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009309?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Qianyi Xu, Yanru Wang, Xiang Feng, Timing Fang, Xueyan Li, Longzhou Zhang, Lijie Zhang, Daohao Li, Dongjiang Yang&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009309</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Sulfonated ether-free polybenzimidazole membrane with fast and selective ion transport enabling ultrahigh cycle stability in vanadium redox flow batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009292?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Hui Yan, Wei Wei, Xin Li, Qi-an Zhang, Ying Li, Ao Tang&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009292</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Calendar aging of sulfide all-solid-state batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009358?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Yujing Wu, Ziqi Zhang, Dengxu Wu, Fuqiang Xu, Mu Zhou, Hong Li, Liquan Chen, Fan Wu&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009358</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Energy-efficient, high-accuracy sensing in loose-fitting textile sensor matrix for LLM-enabled human-robot collaboration</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009425?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Pengfei Deng, Yang Meng, Qilong Cheng, Yuanqiu Tan, Zhihong Chen, Tian Li&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009425</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Lithium superionic solid electrolyte: Phosphorus-free sulfide glass of LiSbGe&lt;sub&gt;(4-x)/4&lt;/sub&gt;S&lt;sub&gt;4-x&lt;/sub&gt;Cl&lt;sub&gt;x&lt;/sub&gt;</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009620?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Yuna Kim, Woojung Lee, Jiyun Han, Yeong Mu Seo, Dokyung Kim, Young Joo Lee, Byung Gon Kim, Munseok S. Chae, Sung Jin Kim, In Young Kim&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009620</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Advancing high-safety and low-cost all-solid-state batteries with polyanion cathodes: Challenges and recent progress</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009978?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 148&lt;/p&gt;&lt;p&gt;Author(s): Ali Yaghtin, Atiyeh Nekahi, Jeremy I.G. Dawkins, Xia Li, Karim Zaghib, Sixu Deng&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009978</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Enabling high-accuracy lithium-ion battery status prediction via machine learning-integrated perovskite sensors</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009851?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 148&lt;/p&gt;&lt;p&gt;Author(s): Jiaxin Sun, Xianjie Xu, Zhefu Mu, Zijun Huang, Guo Chen, Xinkai Qi, Hongwei Liu, Lei Zhu, Xiuquan Gu, Xinjian He, Sheng Huang&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009851</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Phase boundary engineering in micro-sized Sn/SnSb anode enabling superior sodium storage: Synergistic stress relief and fast ion transport</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525010249?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 148&lt;/p&gt;&lt;p&gt;Author(s): Yuhong Liang, Chengcheng He, Zhengyang Zhao, Longqing Zhang, Rui Sun, Qian Ning, Huibing He, Yang Ren, Jing Xu, Qiang Zhang, Yajie Song, Xucai Yin&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525010249</guid></item><item><title>[ScienceDirect Publication: Matter] Machine learning-driven ligand engineering decodes and controls structural distortions in 2D perovskites</title><link>https://www.sciencedirect.com/science/article/pii/S2590238525005259?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 10 October 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Matter&lt;/p&gt;&lt;p&gt;Author(s): Zhipeng Miao, Yahui Han, Qi Pan, Yipei Wang, Haibin Wang, Yunhang Xie, Jie Yu, Yapeng Shi, Rui Zhang, Yanlin Song, Pengwei Li&lt;/p&gt;</description><author>ScienceDirect Publication: Matter</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2590238525005259</guid></item><item><title>[ScienceDirect Publication: Matter] SpectroGen: A physically informed generative artificial intelligence for accelerated cross-modality spectroscopic materials characterization</title><link>https://www.sciencedirect.com/science/article/pii/S2590238525004771?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 14 October 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Matter&lt;/p&gt;&lt;p&gt;Author(s): Yanmin Zhu, Loza F. Tadesse&lt;/p&gt;</description><author>ScienceDirect Publication: Matter</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2590238525004771</guid></item><item><title>[ScienceDirect Publication: Matter] Precisely deciphering solid electrolyte interphase</title><link>https://www.sciencedirect.com/science/article/pii/S2590238525004114?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 5 November 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Matter, Volume 8, Issue 11&lt;/p&gt;&lt;p&gt;Author(s): Enhui Wang, Shaohua Ge, Wenbin Li, Beibei Fu, Fangyi Zhou, Weihua Chen&lt;/p&gt;</description><author>ScienceDirect Publication: Matter</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2590238525004114</guid></item><item><title>[ScienceDirect Publication: Matter] Rapid scalable plasma processing of thin-film Li–La–Zr–O solid-state electrolytes</title><link>https://www.sciencedirect.com/science/article/pii/S2590238525005119?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 5 November 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Matter, Volume 8, Issue 11&lt;/p&gt;&lt;p&gt;Author(s): Gabriel Badillo Crane, Thomas W. Colburn, Sarah E. Holmes, Justus Just, Yi Cui, Reinhold H. Dauskardt&lt;/p&gt;</description><author>ScienceDirect Publication: Matter</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2590238525005119</guid></item><item><title>[ScienceDirect Publication: Joule] Impact of metallic interlayers at the lithium-Li&lt;sub&gt;6&lt;/sub&gt;PS&lt;sub&gt;5&lt;/sub&gt;Cl solid electrolyte interface</title><link>https://www.sciencedirect.com/science/article/pii/S2542435125003563?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 19 November 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Joule, Volume 9, Issue 11&lt;/p&gt;&lt;p&gt;Author(s): Souhardh Kotakadi, Jack Aspinall, Matthew Burton, Yi Liang, Yuichi Aihara, Mauro Pasta&lt;/p&gt;</description><author>ScienceDirect Publication: Joule</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125003563</guid></item><item><title>[ScienceDirect Publication: Joule] Li–Si compound anodes enabling high-performance all-solid-state Li-ion batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2542435125003769?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 17 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Joule, Volume 9, Issue 12&lt;/p&gt;&lt;p&gt;Author(s): Do-Hyeon Kim, Young-Han Lee, Jeong-Myeong Yoon, Pugalenthiyar Thondaiman, Byung Chul Kim, In-Chul Choi, Jeong-Hee Choi, Ki-Joon Jeon, Cheol-Min Park&lt;/p&gt;</description><author>ScienceDirect Publication: Joule</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125003769</guid></item><item><title>[ScienceDirect Publication: Joule] Boosting ionic conductivity of fluoride electrolytes by polyanion coordination chemistry enabling 5 V-Class all-solid-state batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2542435125004143?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 19 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Joule&lt;/p&gt;&lt;p&gt;Author(s): Huaimin Jin, Xingyu Wang, Simeng Zhang, Xiangzhen Zhu, Chong Liu, Junyi Yue, Jie Qu, Bei Wu, Xu Han, Yueyue Wang, Yang Xu, Han Wu, Liyu Zhou, Mingying Zhang, Hao Lai, Shuo Wang, Jiangwen Liang, Xueliang Sun, Xiaona Li&lt;/p&gt;</description><author>ScienceDirect Publication: Joule</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125004143</guid></item><item><title>[cond-mat updates on arXiv.org] CrystalFormer-CSP: Thinking Fast and Slow for Crystal Structure Prediction</title><link>https://arxiv.org/abs/2512.18251</link><description>arXiv:2512.18251v1 Announce Type: new
Abstract: Crystal structure prediction is a fundamental problem in materials science. We present CrystalFormer-CSP, an efficient framework that unifies data-driven heuristic and physics-driven optimization approaches to predict stable crystal structures for given chemical compositions. The approach combines pretrained generative models for space-group-informed structure generation and a universal machine learning force field for energy minimization. Reinforcement fine-tuning can be employed to further boost the accuracy of the framework. We demonstrate the effectiveness of CrystalFormer-CSP on benchmark problems and showcase its usage via web interface and language model integration.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.18251v1</guid></item><item><title>[cond-mat updates on arXiv.org] Spin Reorientation Driven Renormalization of Spin-Phonon Coupling in Fe$_4$GeTe$_2$</title><link>https://arxiv.org/abs/2512.18544</link><description>arXiv:2512.18544v1 Announce Type: new
Abstract: Quasi-2D van der Waals ferromagnet Fe$_4$GeTe$_2$, featuring the simultaneous presence of high Curie temperature ($T_\mathrm{C}$ $\sim 270$ K) and a spin-reorientation transition at $T_\mathrm{SR}$ $\sim 110$ K, is a rare system where strong interplay of spin dynamics, lattice vibrations, and electronic structure leads to a wide range of interesting phenomena. Here, we investigate the lattice response of exfoliated Fe$_4$GeTe$_2$ nanoflakes using temperature-dependent Raman spectroscopy. Polarization-resolved measurements reveal that, while one Raman mode exhibits a purely out-of-plane character, the rest display mixed symmetry, reflecting interlayer vibrational nonuniformity and symmetry-driven mode degeneracies. Below $T_\mathrm{C}$, phonons harden, and the linewidth narrows, consistent with reduced anharmonicity, while across the spin reorientation transition at $T_\mathrm{SR}$ they display anomalous softening, linewidth broadening, and a peak in lifetime, which are signatures of strengthened spin-phonon coupling. Complementary DFT+DMFT calculations and atomistic spin dynamical simulations reveal temperature-dependent spin excitations whose energies overlap with the Raman-active phonons, providing a natural route for the observed magnon-phonon interaction. Together, these insights establish Fe$_4$GeTe$_2$ as a versatile platform for exploring intertwined spin, lattice, and electronic degrees of freedom, with relevance for dynamic spintronic and magneto-optic functionalities near technologically meaningful temperatures.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.18544v1</guid></item><item><title>[cond-mat updates on arXiv.org] Tackling dataset curation challenges towards reliable machine learning: a case study on thermoelectric materials</title><link>https://arxiv.org/abs/2512.18653</link><description>arXiv:2512.18653v1 Announce Type: new
Abstract: Machine Learning (ML) driven discovery of novel and efficient thermoelectric (TE) materials warrants experimental TE datasets of high volume, diversity, and quality. While the largest publicly available dataset, Starrydata2, has a high data volume, it contains inaccurate data due to the inherent limitations of Large Language Model (LLM)-assisted data curation, ambiguous nomenclature and complex formulas of materials in the literature. Another unaddressed issue is the inclusion of multi-source experimental data, with high standard deviations and without synthesis information. Using half-Heusler (hH) materials as an example, this work is aimed at first highlighting these errors and inconsistencies which cannot be filtered with conventional dataset curation workflows. We then propose a statistical round-robin error-based data filtering method to address these issues, a method that can be applied to filter any other material property. Lastly, a hybrid dataset creation workflow, involving data from Starrydata2 and manual extraction, is proposed and the resulting dataset is analyzed and compared against Starrydata2.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.18653v1</guid></item><item><title>[cond-mat updates on arXiv.org] Topological surface phonons modulate thermal transport in semiconductor thin films</title><link>https://arxiv.org/abs/2512.18757</link><description>arXiv:2512.18757v1 Announce Type: new
Abstract: While phonon topology in crystalline solids has been extensively studied, its influence on thermal transport-especially in nanostructures-remains elusive. Here, by combining first-principles-based machine learning potentials with the phonon Boltzmann transport equation and molecular dynamics simulations, we systematically investigate the role of topological surface phonons in the in-plane thermal transport of semiconductor thin films (Si, 4H -SiC, and c-BN). These topological surface phonons, originating from nontrivial acoustic phonon nodal lines, not only serve as key scattering channels for dominant acoustic phonons but also contribute substantially to the overall thermal conductivity. Remarkably, for these thin semiconductor films below 10 nm this contribution can be as large as over 30% of the in-plane thermal conductivity at 300 K, and the largest absolute contribution can reach 82 W/m-K, highlighting their significant role in nanoscale thermal transport in semiconductors. Furthermore, we demonstrate that both temperature and biaxial strain provide effective means to modulate this contribution. Our work establishes a direct link between topological surface phonons and nanoscale thermal transport, offering the first quantitative assessment of their role and paving the way for topology-enabled thermal management in semiconductors.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.18757v1</guid></item><item><title>[cond-mat updates on arXiv.org] Tuning Separator Chemistry: Improving Zn Anode Compatibility via Functionalized Chitin Nanofibers</title><link>https://arxiv.org/abs/2512.19449</link><description>arXiv:2512.19449v1 Announce Type: new
Abstract: Aqueous zinc (Zn) batteries (AZBs) face significant challenges due to the limited compatibility of Zn anodes with conventional separators, leading to dendrite growth, hydrogen evolution reaction (HER), and poor cycling stability. While separator design is crucial for optimizing battery performance, its potential remains underexplored. The commonly used glass fiber (GF) filters were not originally designed as battery separators. To address their limitations, nanochitin derived from waste shrimp shells was used to fabricate separators with varying concentrations of amine and carboxylic functional groups. This study investigates how the type and concentration of these groups influence the separator's properties and performance. In a mild acidic electrolyte that protonates the amine groups, the results showed that the density of both ammonium and carboxylic groups in the separators significantly affected water structure and ionic conductivity. Quasi-Elastic Neutron Scattering (QENS) revealed that low-functionalized chitin, particularly with only ammonium groups, promotes strongly bound water with restricted mobility, thereby enhancing Zn plating and stripping kinetics. These separators exhibit exceptional Zn stability over 2000 hours at low current densities (0.5 mA/cm2), maintaining low overpotentials and stable polarization. Additionally, the full cell consisting of Zn||NaV3O8.1.5H2O showed a cycle life of over 2000 cycles at 2 A/g, demonstrating the compatibility of the nanochitin-based separators with low concentrations of functional surface groups. These results demonstrate the importance of a simple separator design for improving the overall performance of AZBs.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.19449v1</guid></item><item><title>[cond-mat updates on arXiv.org] Long-range electrostatics for machine learning interatomic potentials is easier than we thought</title><link>https://arxiv.org/abs/2512.18029</link><description>arXiv:2512.18029v1 Announce Type: cross
Abstract: The lack of long-range electrostatics is a key limitation of modern machine learning interatomic potentials (MLIPs), hindering reliable applications to interfaces, charge-transfer reactions, polar and ionic materials, and biomolecules. In this Perspective, we distill two design principles behind the Latent Ewald Summation (LES) framework, which can capture long-range interactions, charges, and electrical response just by learning from standard energy and force training data: (i) use a Coulomb functional form with environment-dependent charges to capture electrostatic interactions, and (ii) avoid explicit training on ambiguous density functional theory (DFT) partial charges. When both principles are satisfied, substantial flexibility remains: essentially any short-range MLIP can be augmented; charge equilibration schemes can be added when desired; dipoles and Born effective charges can be inferred or finetuned; and charge/spin-state embeddings or tensorial targets can be further incorporated. We also discuss current limitations and open challenges. Together, these minimal, physics-guided design rules suggest that incorporating long-range electrostatics into MLIPs is simpler and perhaps more broadly applicable than is commonly assumed.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.18029v1</guid></item><item><title>[cond-mat updates on arXiv.org] Microstructure-based Variational Neural Networks for Robust Uncertainty Quantification in Materials Digital Twins</title><link>https://arxiv.org/abs/2512.18104</link><description>arXiv:2512.18104v1 Announce Type: cross
Abstract: Aleatoric uncertainties - irremovable variability in microstructure morphology, constituent behavior, and processing conditions - pose a major challenge to developing uncertainty-robust digital twins. We introduce the Variational Deep Material Network (VDMN), a physics-informed surrogate model that enables efficient and probabilistic forward and inverse predictions of material behavior. The VDMN captures microstructure-induced variability by embedding variational distributions within its hierarchical, mechanistic architecture. Using an analytic propagation scheme based on Taylor-series expansion and automatic differentiation, the VDMN efficiently propagates uncertainty through the network during training and prediction. We demonstrate its capabilities in two digital-twin-driven applications: (1) as an uncertainty-aware materials digital twin, it predicts and experimentally validates the nonlinear mechanical variability in additively manufactured polymer composites; and (2) as an inverse calibration engine, it disentangles and quantitatively identifies overlapping sources of uncertainty in constituent properties. Together, these results establish the VDMN as a foundation for uncertainty-robust materials digital twins.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.18104v1</guid></item><item><title>[cond-mat updates on arXiv.org] Lattice-Renormalized Tunneling Models for Superconducting Qubit Materials</title><link>https://arxiv.org/abs/2512.18156</link><description>arXiv:2512.18156v1 Announce Type: cross
Abstract: We present a lattice-renormalized formalism for configurational tunneling two-level systems (TLS) that overcomes limitations of minimum-energy-path and light-particle models. Derived from the nuclear Hamiltonian, our formulation introduces composite phonon coordinates to capture lattice distortions between degenerate potential wells. This approach resolves deficiencies in prior models and enables accurate computation of tunnel splittings and excitation spectra for hydrogen-based TLS in bcc Nb. Our results bound experimental tunnel splittings and reveal strong anharmonic couplings between tunneling atoms and lattice phonons, establishing a direct link between TLS dynamics and phonon-mediated strain interactions. The formalism further generalizes to multi-level systems (MLS), providing insight into defect-induced decoherence in superconducting qubits and guiding strategies for materials design to suppress TLS-related loss.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.18156v1</guid></item><item><title>[cond-mat updates on arXiv.org] An Agentic Framework for Autonomous Materials Computation</title><link>https://arxiv.org/abs/2512.19458</link><description>arXiv:2512.19458v1 Announce Type: cross
Abstract: Large Language Models (LLMs) have emerged as powerful tools for accelerating scientific discovery, yet their static knowledge and hallucination issues hinder autonomous research applications. Recent advances integrate LLMs into agentic frameworks, enabling retrieval, reasoning, and tool use for complex scientific workflows. Here, we present a domain-specialized agent designed for reliable automation of first-principles materials computations. By embedding domain expertise, the agent ensures physically coherent multi-step workflows and consistently selects convergent, well-posed parameters, thereby enabling reliable end-to-end computational execution. A new benchmark of diverse computational tasks demonstrates that our system significantly outperforms standalone LLMs in both accuracy and robustness. This work establishes a verifiable foundation for autonomous computational experimentation and represents a key step toward fully automated scientific discovery.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.19458v1</guid></item><item><title>[cond-mat updates on arXiv.org] Understanding the Lithium Ion Transport in Concentrated Block-Copolymer Electrolytes on a Microscopic Level</title><link>https://arxiv.org/abs/2010.11673</link><description>arXiv:2010.11673v3 Announce Type: replace
Abstract: Block-copolymer electrolytes with lamellar microstructure show promising results regarding the ion transport in experiments. Motivated by these observations we study block-copolymers consisting of a polystyrene (PS) block and a poly(ethylene oxide) (PEO) block which were assembled in a lamellar structure. The lamella was doped with various amounts of lithium-bis(trifluoromethane)sulfonimide (LiTFSI) until very high loadings with ratios of EO monomers to cations up to 1:1 were reached. We present insights into the structure and ion transport from extensive Molecular Dynamics simulations. For high salt concentrations most cations are not coordinated by PEO but rather by TFSI and THF. More specifically, LiTFSI partially separates from the PEO domain and forms a network-like structure in the middle of the lamella. This central salt-rich layer plays a decisive role to enable remarkably good cationic mobilities as well as high transport numbers in agreement with the experimental results.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2010.11673v3</guid></item><item><title>[cond-mat updates on arXiv.org] Observation of multiple flat bands and van Hove singularities in the distorted kagome metal NdTi3Bi4</title><link>https://arxiv.org/abs/2311.11488</link><description>arXiv:2311.11488v2 Announce Type: replace
Abstract: Kagome materials have attracted enormous research interest recently owing to their diverse topological phases and manifestation of electronic correlation. Here, we present the electronic structure of a distorted ferromagnetic kagome metal, NdTi3Bi4, exhibiting a transition temperature of 9 K. Our investigation employs a combination of angle-resolved photoemission spectroscopy (ARPES) measurements and density functional theory (DFT) calculations. We discover the presence of two flat bands which are found to originate from the kagome structure formed by Ti atoms with major contribution from Ti dxy and Ti dx2-y2 orbitals. We also observed multiple van Hove singularities (VHSs) in its electronic structure, with one VHS lying near the Fermi level. The ARPES data reveals the existence of Dirac cone at the K point, a finding which is corroborated by our DFT calculations. These findings present detailed electronic structure capable of hosting correlation-driven phenomenon in this novel ferromagnetic kagome metal.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2311.11488v2</guid></item><item><title>[cond-mat updates on arXiv.org] Deep Variational Free Energy Calculation of Hydrogen Hugoniot</title><link>https://arxiv.org/abs/2507.18540</link><description>arXiv:2507.18540v2 Announce Type: replace
Abstract: We develop a deep variational free energy framework to compute the equation of state of hydrogen in the warm dense matter region. This method parameterizes the variational density matrix of hydrogen nuclei and electrons at finite temperature using three deep generative models: a normalizing flow model for the Boltzmann distribution of the classical nuclei, an autoregressive transformer for the distribution of electrons in excited states, and a permutational equivariant flow model for the unitary backflow transformation of electron coordinates in Hartree-Fock states. By jointly optimizing the three neural networks to minimize the variational free energy, we obtain the equation of state and related thermodynamic properties of dense hydrogen for the temperature range where electrons occupy excited states. We compare our results with other theoretical and experimental results on the deuterium Hugoniot curve, aiming to resolve existing discrepancies. Our results bridge the gap between the results obtained by path-integral Monte Carlo calculations at high temperature and ground-state electronic methods at low temperature, thus providing a valuable benchmark for hydrogen in the warm dense matter region.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2507.18540v2</guid></item><item><title>[cond-mat updates on arXiv.org] Universal Boundary-Modes Localization from Quantum Metric Length</title><link>https://arxiv.org/abs/2509.05114</link><description>arXiv:2509.05114v3 Announce Type: replace
Abstract: The presence of localized boundary modes is an unambiguous hallmark of topological quantum matter. While these modes are typically protected by topological invariants such as the Chern number, here we demonstrate that the {\it quantum metric length} (QML), a quantity inherent in multi-band topological systems, governs the spatial extent of flat-band topological boundary modes. We introduce a framework for constructing topological flat bands from degenerate manifolds with large quantum metric and find that the boundary modes exhibit dual phases of spatial behaviors: a conventional oscillatory decay arising from bare band dispersion, followed by another exponential decay controlled by quantum geometry. Crucially, the QML, derived from the quantum metric of the degenerate manifolds, sets a lower bound on the spatial spread of boundary states in the flat-band limit. Applying our framework to concrete models, we validate the universal role of the QML in shaping the long-range behavior of topological boundary modes. Furthermore, by tuning the QML, we unveil extraordinary non-local transport phenomena, including QML-shaped quantum Hall plateaus and anomalous Fraunhofer patterns. Our theoretical framework paves the way for engineering boundary-modes localization in topological flat-band systems.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2509.05114v3</guid></item><item><title>[cond-mat updates on arXiv.org] Deep learning directed synthesis of fluid ferroelectric materials</title><link>https://arxiv.org/abs/2512.16671</link><description>arXiv:2512.16671v2 Announce Type: replace
Abstract: Fluid ferroelectrics, a recently discovered class of liquid crystals that exhibit switchable, long-range polar order, offer opportunities in ultrafast electro-optic technologies, responsive soft matter, and next-generation energy materials. Yet their discovery has relied almost entirely on intuition and chance, limiting progress in the field. Here we develop and experimentally validate a deep-learning data-to-molecule pipeline that enables the targeted design and synthesis of new organic fluid ferroelectrics. We curate a comprehensive dataset of all known longitudinally polar liquid-crystal materials and train graph neural networks that predict ferroelectric behaviour with up to 95% accuracy and achieve root mean square errors as low as 11 K for transition temperatures. A graph variational autoencoder generates de novo molecular structures which are filtered using an ensemble of high-performing classifiers and regressors to identify candidates with predicted ferroelectric nematic behaviour and accessible transition temperatures. Integration with a computational retrosynthesis engine and a digitised chemical inventory further narrows the design space to a synthesis-ready longlist. 11 candidates were synthesised and characterized through established mixture-based extrapolation methods. From which extrapolated ferroelectric nematic transitions were compared against neural network predictions. The experimental verification of novel materials augments the original dataset with quality feedback data thus aiding future research. These results demonstrate a practical, closed-loop approach to discovering synthesizable fluid ferroelectrics, marking a step toward autonomous design of functional soft materials.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.16671v2</guid></item><item><title>[cond-mat updates on arXiv.org] Anti-Correlated Noise in Epoch-Based Stochastic Gradient Descent: Implications for Weight Variances in Flat Directions</title><link>https://arxiv.org/abs/2306.05300</link><description>arXiv:2306.05300v3 Announce Type: replace-cross
Abstract: Stochastic Gradient Descent (SGD) has become a cornerstone of neural network optimization due to its computational efficiency and generalization capabilities. However, the gradient noise introduced by SGD is often assumed to be uncorrelated over time, despite the common practice of epoch-based training where data is sampled without replacement. In this work, we challenge this assumption and investigate the effects of epoch-based noise correlations on the stationary distribution of discrete-time SGD with momentum. Our main contributions are twofold: First, we calculate the exact autocorrelation of the noise during epoch-based training under the assumption that the noise is independent of small fluctuations in the weight vector, revealing that SGD noise is inherently anti-correlated over time. Second, we explore the influence of these anti-correlations on the variance of weight fluctuations. We find that for directions with curvature of the loss greater than a hyperparameter-dependent crossover value, the conventional predictions of isotropic weight variance under stationarity, based on uncorrelated and curvature-proportional noise, are recovered. Anti-correlations have negligible effect here. However, for relatively flat directions, the weight variance is significantly reduced, leading to a considerable decrease in loss fluctuations compared to the constant weight variance assumption. Furthermore, we present a numerical experiment where training with these anti-correlations enhances test performance, suggesting that the inherent noise structure induced by epoch-based training may play a role in finding flatter minima that generalize better.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2306.05300v3</guid></item><item><title>[cond-mat updates on arXiv.org] Unified Micromechanics Theory of Composites</title><link>https://arxiv.org/abs/2503.14529</link><description>arXiv:2503.14529v2 Announce Type: replace-cross
Abstract: We consider the matrix composite materials (CM) of either random (statistically homogeneous or inhomogeneous), periodic, or deterministic (neither random nor periodic) structures. CMs exhibit linear or nonlinear behavior, coupled or uncoupled multi-physical phenomena, locally elastic, weakly nonlocal (strain gradient and stress gradient), or strongly nonlocal (strain-type and displacement-type, peridynamics) phase properties. A modified Computational Analytical Micromechanics (CAM) approach introduces an exact Additive General Integral Equation (AGIE) for CMs of any structure and phase properties mentioned above. The unified iteration solution of static AGIEs is adapted to the body force with compact support serving as a fundamentally new universal training parameter. The approach also establishes a critical threshold for filtering out unsuitable sub-datasets of effective parameters through a novel Representative Volume Element (RVE) concept, which extends Hill's classical framework. This RVE concept eliminates sample size, boundary layer, and edge effects, making it applicable to CMs of any structure and phase properties, regardless of local or nonlocal, linear or nonlinear. Incorporating this new RVE concept into machine learning and neural network techniques enables the construction of any unpredefined surrogate nonlocal operators. The methodology is structured as a modular, block-based framework, allowing independent development and refinement of software components. This flexible, robust AGIE-CAM framework integrates data-driven, multi-scale, and multi-physics modeling, accelerating research in CM of any microtopology and phase properties considered. The AGIE-CAM framework represents a groundbreaking paradigm shift in the micromechanics of composites, redefining the very philosophy that underpins our understanding of their behavior at the microscopic level.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2503.14529v2</guid></item><item><title>[cond-mat updates on arXiv.org] Separating water content from network dynamics in cell nuclei with Brillouin microscopy</title><link>https://arxiv.org/abs/2504.17362</link><description>arXiv:2504.17362v2 Announce Type: replace-cross
Abstract: Probing forces, deformations and generally speaking the mechanical properties of cells is the hallmark of mechanobiology. In the last two decades many techniques have been developed to this end that are largely based on deforming the cells and measuring the reaction force. In cells, an alternative approach has been implemented mid 2010's, based on Brillouin Light Scattering (BLS) that produces a spectrum that can be interpreted as the response of the sample to an infinitesimal uniaxial compression at picosecond timescales. In all of these measurements, the response of the cell is quantified with a colloquial "stiffness" that encompasses both the contribution of load-bearing structures and volume changes, much to confusion. To clarify the interpretation of the hypersonic data obtained from BLS spectra, we vary the relative volume fraction of intracellular water and solid network by applying osmotic compressions to single cells. In the nucleus, we observe a non-linear increase in the sound velocity and attenuation with increasing osmotic pressure that we fit to a poroelastic model, providing an estimate of the friction coefficient between the water phase and the network. By comparing BLS data to volume measurements, our approach demonstrates clearly that BLS shift alone is mostly sensitive to water content while the additional analysis of the linewidth allows identifying the contribution of the biopolymer-based network dynamics in living cells.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2504.17362v2</guid></item><item><title>[cond-mat updates on arXiv.org] BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models</title><link>https://arxiv.org/abs/2505.01912</link><description>arXiv:2505.01912v2 Announce Type: replace-cross
Abstract: Data-driven molecular discovery leverages artificial intelligence/machine learning (AI/ML) and generative modeling to filter and design novel molecules. Discovering novel molecules requires accurate out-of-distribution (OOD) predictions, but ML models struggle to generalize OOD. Currently, no systematic benchmarks exist for molecular OOD prediction tasks. We present $\mathbf{BOOM}$, $\mathbf{b}$enchmarks for $\mathbf{o}$ut-$\mathbf{o}$f-distribution $\mathbf{m}$olecular property predictions: a chemically-informed benchmark for OOD performance on common molecular property prediction tasks. We evaluate over 150 model-task combinations to benchmark deep learning models on OOD performance. Overall, we find that no existing model achieves strong generalization across all tasks: even the top-performing model exhibited an average OOD error 3x higher than in-distribution. Current chemical foundation models do not show strong OOD extrapolation, while models with high inductive bias can perform well on OOD tasks with simple, specific properties. We perform extensive ablation experiments, highlighting how data generation, pre-training, hyperparameter optimization, model architecture, and molecular representation impact OOD performance. Developing models with strong OOD generalization is a new frontier challenge in chemical ML. This open-source benchmark is available at https://github.com/FLASK-LLNL/BOOM</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2505.01912v2</guid></item><item><title>[cond-mat updates on arXiv.org] PLaID++: A Preference Aligned Language Model for Targeted Inorganic Materials Design</title><link>https://arxiv.org/abs/2509.07150</link><description>arXiv:2509.07150v3 Announce Type: replace-cross
Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a promising approach to improve correctness in LLMs, however, in many scientific problems, the objective is not necessarily to produce the correct answer, but instead to produce a diverse array of candidates which satisfy a set of constraints. We study this challenge in the context of materials generation. To this end, we introduce PLaID++, an LLM post-trained for stable and property-guided crystal generation. We find that performance hinges on our crystallographic representation and reward formulation. First, we introduce a compact, symmetry-informed Wyckoff text representation which improves computational efficiency and encourages generalization from physical priors. Second, we demonstrate that temperature scaling acts as an entropy regularizer which counteracts mode collapse and encourages exploration. By encoding symmetry constraints directly into text and guiding model outputs towards desirable chemical space, PLaID++ generates structures that are thermodynamically stable, unique, and novel at a $\sim$50\% greater rate than prior methods and conditionally generates structures with desired space group properties. Our work demonstrates the potential of adapting post-training techniques from natural language processing to materials design, paving the way for targeted and efficient discovery of novel materials.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2509.07150v3</guid></item><item><title>[cond-mat updates on arXiv.org] Nonreciprocal yet Symmetric Multi-Species Active Matter: Emergence of Chirality and Species Separation</title><link>https://arxiv.org/abs/2512.18749</link><description>arXiv:2512.18749v1 Announce Type: new
Abstract: Nonreciprocal active matter systems typically feature an asymmetric role among interacting agents, such as a pursuer-evader relationship. We propose a multi-species nonreciprocal active matter model that is invariant under permutations of the particle species. The nonreciprocal, yet symmetric, interactions emerge from a constant phase shift in the velocity alignment interactions, rather than from an asymmetric coupling matrix. This system possessing permutation symmetry displays rich collective behaviors, including a species-mixed chiral phase with quasi-long-range polar order and a species separation phase characterized by vortex cells. The system also displays a coexistence phase of the chiral and the species separation phases, in which intriguing dynamic patterns emerge. These rich collective behaviors are a consequence of the interplay between nonreciprocity and permutation symmetry.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.18749v1</guid></item><item><title>[cond-mat updates on arXiv.org] Enhanced sinterability and in vitro bioactivity of diopside through fluoride doping</title><link>https://arxiv.org/abs/2512.19014</link><description>arXiv:2512.19014v1 Announce Type: new
Abstract: In this work, diopside (CaMgSi2O6) was doped with fluoride at a level of 1 mol.%, without the formation of any second phase, by a wet chemical precipitation method. The sintered structure of the synthesized nanopowders was studied by X-ray diffraction, Fourier transform infrared spectroscopy and field-emission scanning electron microscopy. Also, the samples' in vitro apatite-forming ability in a simulated body fluid was comparatively evaluated by electron microscopy, inductively coupled plasma spectroscopy and Fourier transform infrared spectroscopy. According to the results, the material's sinterability was improved by fluoride doping, as realized from the further development of sintering necks. It was also found that compared to the undoped bioceramic, a higher amount of apatite was deposited on the surface of the doped sample. It is concluded that fluoride can be considered as a doping agent in magnesium-containing silicates to improve biological, particularly bioactivity, behaviors.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.19014v1</guid></item><item><title>[cond-mat updates on arXiv.org] Bridging the divide: Economic exchange and segregation in dual-income cities</title><link>https://arxiv.org/abs/2512.18680</link><description>arXiv:2512.18680v1 Announce Type: cross
Abstract: Segregation is a growing concern around the world. One of its main manifestations is the creation of ghettos, whose inhabitants have difficult access to well-paid jobs, which are often located far from their homes. In order to study this phenomenon, we propose an extension of Schelling's model of segregation to take into account the existence of economic exchanges. To approximate a geographical model of the city, we consider a small-world network with a defined real estate market. The evolution of the system has also been studied, finding that economic exchanges follow exponential laws and relocations are approximated by power laws. In addition to this, we consider the existence of delays in the actions of the agents, which mainly affect the happiness of those with fewer economic resources. Besides, the size of the economic exchange plays a crucial role in overall segregation. Despite its simplicity, we find that our model reproduces real-world situations such as the separation between favoured and handicapped economic areas, the importance of economic contacts between them to improve the distribution of wealth, and the existence of efficient and cheap transport to break the poverty cycles typical of disadvantaged zones.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.18680v1</guid></item><item><title>[cond-mat updates on arXiv.org] Information Supercurrents in Chiral Active Matter</title><link>https://arxiv.org/abs/2512.16884</link><description>arXiv:2512.16884v2 Announce Type: replace
Abstract: Recent minimalist modeling has demonstrated that overdamped polar chiral active matter can support emergent, inviscid Euler turbulence, despite the system's strictly dissipative microscopic nature. In this letter, we establish the statistical mechanical foundation for this emergent inertial regime by deriving a formal isomorphism between the model's agent dynamics and the overdamped Langevin equation for disordered Josephson junctions. We identify the trapped agent state as carrying non-dissipative (phase rigidity) information supercurrents, analogous to a macroscopic superconducting phase governed by the Adler equation. The validity of this mapping is confirmed analytically and empirically by demonstrating a disorder-broadened Adler-Ohmic crossover in the system's slip velocity, corresponding to the saddle-node bifurcation of phase-locking systems. These results define the new minimal chiral flocking model as a motile, disordered Josephson array, bridging active turbulence and superconductivity.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.16884v2</guid></item><item><title>[Communications Materials] In situ polymerization for high performance solid-state lithium-sulfur batteries</title><link>https://www.nature.com/articles/s43246-025-01035-3</link><description>&lt;p&gt;Communications Materials, Published online: 23 December 2025; &lt;a href="https://www.nature.com/articles/s43246-025-01035-3"&gt;doi:10.1038/s43246-025-01035-3&lt;/a&gt;&lt;/p&gt;Solid-state lithium-sulfur batteries promise high energy density, long-term performance, and enhanced safety, but face challenges with interfacial issues due to poor solid–solid contact. Here, the authors review the benefits and challenges of in situ polymerization, discussing its potential to enhance electrode-electrolyte integration and improve battery performance, and proposing future prospects for multifunctional polymer solid-state electrolytes.</description><author>Communications Materials</author><pubDate>Tue, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s43246-025-01035-3</guid></item><item><title>[ChemRxiv] Molecular Dynamics Simulations for Organic Chemists – It’s About Time!</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-fclzt?rft_dat=source%3Ddrss</link><description>Molecular dynamics simulations model chemical reactions as continuous changes in molecular structure over time in-stead of static minima and transition states. This perspective argues that time-dependent structural change is a crucial, but often overlooked, mechanistic feature as many reactions simply do not follow a single, equilibrated minimum-energy path. We highlight examples where traditional transition state theory fails, typically cases involving short-lived interme-diates, non-equilibrium solvation, momentum-controlled selectivity, post-transition state bifurcations, and “hidden” dy-namic intermediates and show how molecular dynamics can reveal the actual sequence of structural change which gov-erns a reaction outcome. We also discuss emerging machine learning-based molecular dynamics which have found appli-cations in photochemistry and solvent modelling. While molecular dynamics will not replace methods based on transi-tion state theory, it offers organic chemists a time-resolved view of molecular structure which can be crucial to under-standing a given reaction. However, a central barrier for organic chemists is to understand when and why to apply an ad-vanced computational technique such as molecular dynamics simulations. In this perspective, we aim to introduce the methodology in sufficient detail to enable organic chemists to make this assessment and gain an appreciation for the im-portance of time in reaction mechanisms.</description><author>ChemRxiv</author><pubDate>Tue, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-fclzt?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Characterisation and immobilisation study of the Haloperoxidase from Curvularia inaequalis: application to phenol derivatives</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-jcmpt?rft_dat=source%3Ddrss</link><description>In the past decades, usage of enzymes to catalyse halogenation reactions has emerged as a greener approach compared to usual ones using toxic reagents and yielding to a bad atom economy. Vanadium dependent haloperoxidases (VDHals) are the most commonly used enzymes for such transformations in organic chemistry due to their robustness. Among them, haloperoxidase from Curvularia inaequalis (CiVCPO) is the most cited in the literature but only barely characterized aside from its substrate spectrum and kinetic parameters. In the present study, we evaluated the melting temperature, thermostability, thermoactivity and solvent stability of CiVCPO in order to have a better overview of its potential in organic synthesis. We have also performed the first immobilisation study of this enzyme on 3 types of supports: 3 EziG™ glass beads coated with different polymers, 5 Relizyme™ polymethacrylate beads with different functional groups, the commercial Amberlite IRA900 and Dowex 50WX8, and 3 metal-organic frameworks from the UiO-66(Zr) family. As a result, we could retain 50% of total immobilized activity on 2 Relizyme supports (ethylamine (EA403) &amp; iminodiacetic (IDA403), with 55% activity kept over 5 recycling steps. The unconventional UiO-66(Zr) family also proved to be an interesting material for this enzyme. Finally, we show that free enzyme and supported enzyme are suitable for bromination and chlorination of a range of phenolic compounds with excellent yields with up to 65% conversion in 24 h in the tested conditions.</description><author>ChemRxiv</author><pubDate>Tue, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-jcmpt?rft_dat=source%3Ddrss</guid></item><item><title>[Nature Nanotechnology] Nanostructured niobium-doped nickel-rich multiphase positive electrode active material for high-power lithium-based batteries</title><link>https://www.nature.com/articles/s41565-025-02092-y</link><description>&lt;p&gt;Nature Nanotechnology, Published online: 23 December 2025; &lt;a href="https://www.nature.com/articles/s41565-025-02092-y"&gt;doi:10.1038/s41565-025-02092-y&lt;/a&gt;&lt;/p&gt;A two-step doping strategy for preparing Nb-doped Ni-rich positive electrode active materials forms nanosized grains and enables reversible multiphase transitions, improving lithium-ion transport and high-power performance of Li-based batteries.</description><author>Nature Nanotechnology</author><pubDate>Tue, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41565-025-02092-y</guid></item><item><title>[ChemRxiv] Study of anion transport by amide-based macrocycles of different sizes</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-g4gds?rft_dat=source%3Ddrss</link><description>Synthetic anion receptors can transport anions across lipid bilayer membranes. Many anion transporters have been reported, including macrocyclic compounds. However, there are no systematic studies on how the size of macrocycles impacts their anion transport activity and selectivity. Therefore, we prepared eight amide-based macrocyclic compounds with ring sizes ranging from 18 to 26 atoms, as well as four bis-amides. We studied these compounds as anion receptors using chloride titrations and molecular modelling, and as anion transporters using liposomes with various fluorescent probes. Neither the size of the macrocycles nor the affinity for chloride were found to be determining factors for chloride transport activity; preorganisation appears to play a more important role. Fluorination was found to have a clear positive effect on anion transport rates, with the bis-amides performing surprisingly well compared to the macrocycles. The smallest non-fluorinated macrocycle exhibited selectivity for chloride over hydroxide and nitrate, whereas the pentafluorophenyl bis-amide demonstrated effective transport of bicarbonate and nitrate, likely due to anion-pi interactions in addition to hydrogen bonding. The insights from this study will shape the design of future anionophores.</description><author>ChemRxiv</author><pubDate>Tue, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-g4gds?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Ion-Conductive Vitrimers Based on Backbone-Type Triazolium Poly(ionic liquid)s: Counterion-Dependent Dynamics and Backbone Flexibility</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-2mmg1?rft_dat=source%3Ddrss</link><description>To simultaneously achieve high ionic conductivity and recyclability, vitrimers were prepared using backbone-type triazolium poly(ionic liquid)s (TPILs) that integrate ionic transport with dynamic network rearrangement via trans-N-alkylation. TPIL elastomers bearing I⁻, BF₄⁻, PF₆⁻, and TFSI⁻ counteranions were synthesized from “clickable” ionic liquid monomers, and their glass transition temperature (Tg), ionic conductivity, and vitrimeric dynamics were compared. Only the I⁻-based network exhibited stress relaxation at 170 °C, indicating that nucleophilic anions are important for bond exchange. However, a trade-off was observed between ionic transport and dynamic network rearrangement. Furthermore, mixed-anion TPIL elastomers using I⁻+TFSI⁻ exhibited lower Tg and higher ionic conductivity than I⁻-based elastomer, while still maintaining vitrimer-like relaxation. The segmental relaxation was decoupled from Arrhenius-type bond-exchange dynamics. Ionic conduction was dominated by segmental motion, with minimal contribution from cross-link exchange. This design combining flexible polymer backbones and cooperative anion engineering can create recyclable, highly conductive polymer electrolytes.</description><author>ChemRxiv</author><pubDate>Tue, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-2mmg1?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Advances in Calcium Isotope Purification and Analysis Using Cutting-Edge Signal Amplifiers for Matrix-Diverse Reference Materials</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-ddk2z?rft_dat=source%3Ddrss</link><description>Stable calcium (Ca) isotopes are increasingly applied across geosciences, medical sciences, ecology, paleontology, and archaeology. However, the deployment speed of Ca isotope applications worldwide is hampered by three major challenges: 1) the necessity for complex Ca purification procedures prior to analysis; 2) expensive instrumentation (typically TIMS or ICP-MS) requiring specific configurations and fine-tuning to generate reliable data; and 3) the exhaustion of some of the most widely used reference materials for cross-laboratory comparisons. In this study we present methodological advances aimed at lifting some of these barriers. First, we refined existing chromatography methods for purifying Ca by developing a branching procedure based on commercially available labware to allow faster method transfer and to minimize resin and reagent consumption for a variety of sample matrices. Our adjustments drastically improved strontium (Sr) separation from Ca, including for Sr-rich samples such as seawater. Second, we explored the potential of 10¹³Ω Faraday cup amplifiers for improving Ca isotope measurements. Our results show improved precision in 43Ca measurements under low ionic transmission configurations with δ43/42Ca standard deviation value reduced by half. This expands the list of ICP-MS configurations capable of producing reliable Ca isotope measurements and delineates a path for less sample-destructive methods (i.e., lower Ca analytical requirements). These amplifiers also markedly enhanced the correction of Sr²⁺ interferences typically affecting Ca ion beams. In this configuration, accurate and precise Ca isotopic measurements were obtained without prior Sr removal for Sr/Ca concentration ratios up to 10⁻². Lastly, using these technical advancements we analyzed existing and new international certified reference materials (SRM1486, SRM1400, IAPSO, CACB-1, DOLT-5, DORM-5, TORT-3), complementing existing and out-of-stock standards of the Ca isotope toolbox, notably for Ca carbonate and marine soft tissues. Together, these advances open the door of Ca isotope research to more laboratories and pave the way for future developments and applications.</description><author>ChemRxiv</author><pubDate>Tue, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-ddk2z?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Predicting Sequence Dependent Fluorescence with Classic Machine Learning Models</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-hs62c?rft_dat=source%3Ddrss</link><description>Terminally labeled DNA oligonucleotides have wide applications in modern biology and biotechnological applications. It has been observed that the fluorescent intensity of light released from these fluorescent labels is heavily influenced by the terminal sequence of nucleotides. Recent studies have assayed and published the raw fluorescent values of Cy3 and Cy5 as a function of the most adjacent 5 nucleotides resulting in 1024 data points. While experimentally tractable, an increase in the sequence space will vastly increase the experimental and time cost. Machine Learning is well suited to addressing the issue of experimental tractability however there is a wide design space in the choice of algorithms. In this work we use classic machine learning models such as Support Vector Machine, Multilayer Perceptrons and Random Forests to both predict the raw intensity value and classify the intensity magnitude of the fluorophore using the sequence as input. We demonstrate that the performance of these models is heavily dependent on the numerical transformation of the sequence and that Random Forest consistently outperforms all other models in both regression and classification tasks irrespective of the sequence transformation.</description><author>ChemRxiv</author><pubDate>Tue, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-hs62c?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Machine learning driven advances in molecular dynamics of bulk and interfacial aqueous systems</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-6vdl2-v2?rft_dat=source%3Ddrss</link><description>Molecular dynamics (MD) simulations have been widely applied to investigate various physical and chemical processes in aqueous and interfacial environments, which are crucial for the design of energy materials and for understanding several chemical processes at the heart of life itself. However, the applicability of MD simulations has been constrained by several inherent challenges, including the accuracy of force fields, limitations in simulation size and timescales. One promising solution to these challenges is the integration of machine learning (ML) methods, both for improved description of the nature of interactions in aqueous systems as well as for enhanced sampling. In this review, we discuss the principles, implementation, and applications of ML force fields (MLFFs) and ML enhanced sampling methods to the study of aqueous, interfacial systems. We discuss five key categories of applications that use MLFFs, ML-enhanced sampling, and ML-driven data analytics. We first discuss how MLFFs are enabling quantum level accuracy at classical level cost for large scale simulations of complex aqueous and interfacial systems, and then highlight how coupling them with enhanced sampling and advanced data analytics, especially graph based approaches for featurizing such systems, can be used both for enhancing simulations and for understanding them by yielding reliable low dimensional reaction coordinates that improve the interpretation of high dimensional MD data. The discussed applications include investigations into the structure and dynamics of bulk water and aqueous interfaces, proton transfer, catalysis, phase transitions, and the prediction of vibrational spectra. In each case, we highlight how ML-based methods enable simulations that were previously computationally prohibitive and provide new physical insights into aqueous solutions and interfaces. For instance, MLFFs allow nanosecond-scale simulations with thousands of atoms while maintaining quantum chemistry accuracy. Additionally, ML-enhanced sampling facilitates the crossing of large reaction barriers and enables the exploration of extensive configuration spaces. Moreover, ML models trained on simulation data uncover previously overlooked factors, such as the role of solvent dynamics in phase transitions. The combination of MLFFs with enhanced sampling techniques makes the calculation of high-dimensional free energy surfaces feasible, significantly improving our understanding of chemical reactions. Finally, we discuss the current challenges in this field and outline potential future research directions to further advance the integration of ML in MD simulations.</description><author>ChemRxiv</author><pubDate>Tue, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-6vdl2-v2?rft_dat=source%3Ddrss</guid></item><item><title>[RSC - Chem. Sci. latest articles] Automated Closed-Loop Continuous Flow Block Copolymer Synthesizer</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07307C</link><description>&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/D5SC07307C, 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/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.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;WeiNian Wong, Daniel Phillips, MD Taifur Rahman, Tanja Junkers&lt;br /&gt;A fully automated continuous flow synthesizer for diblock copolymer (BCP) synthesis was constructed comprising elements of flow chemistry, automation, machine learning and in-line monitoring. A new method using in-line FTIR...&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, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07307C</guid></item><item><title>[RSC - Chem. Sci. latest articles] Node-Equivariant Message Passing for Efficient and Accurate Machine Learning Interatomic Potentials</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07248D</link><description>&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/D5SC07248D, 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/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.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Yaolong Zhang, Hua Guo&lt;br /&gt;Machine learned interatomic potentials, particularly equivariant message-passing (MP) models, have demonstrated high fidelity in representing first-principles data, revolutionizing computational studies in materials science, biophysics, and catalysis. However, these equivariant MP...&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, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07248D</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500092?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Mon, 22 Dec 2025 17:43:04 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500092</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Sulfur‐Doped Bi2O3 Nanorods with Asymmetric S−Bi−O Active Sites for Highly Efficient Electrosynthesis of Formic Acid</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202527887?af=R</link><description>Advanced Functional Materials, EarlyView.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Mon, 22 Dec 2025 14:14:10 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202527887</guid></item><item><title>[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Density Effects on the Thermal Decomposition of LLM-105 Explored by Neural Network Potential</title><link>http://dx.doi.org/10.1021/acs.jpcc.5c06140</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpcc.5c06140/asset/images/medium/jp5c06140_0016.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.5c06140&lt;/div&gt;</description><author>The Journal of Physical Chemistry C: Latest Articles (ACS Publications)</author><pubDate>Mon, 22 Dec 2025 14:01:00 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpcc.5c06140</guid></item><item><title>[Accounts of Chemical Research: Latest Articles (ACS Publications)] [ASAP] Accelerating Materials Discovery Through Sparse Gaussian Process Machine Learning Potentials</title><link>http://dx.doi.org/10.1021/acs.accounts.5c00667</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.accounts.5c00667/asset/images/medium/ar5c00667_0009.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;Accounts of Chemical Research&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acs.accounts.5c00667&lt;/div&gt;</description><author>Accounts of Chemical Research: Latest Articles (ACS Publications)</author><pubDate>Mon, 22 Dec 2025 13:59:15 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.accounts.5c00667</guid></item><item><title>[Wiley: Small: Table of Contents] Revealing Electronic Structure–Chemisorption Relationships for Accelerated Discovery of Aqueous Zinc Battery Additives Through Machine Learning</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202510034?af=R</link><description>Small, EarlyView.</description><author>Wiley: Small: Table of Contents</author><pubDate>Mon, 22 Dec 2025 12:24:02 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202510034</guid></item><item><title>[Wiley: Small: Table of Contents] Ultra‐High Capacity Density Lithium Metal Battery is Effectuated via Coupling Single Lithium‐Ion Conductor and Lithium‐Ion Sieve Within Yttrium‐Organic Framework</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202511276?af=R</link><description>Small, EarlyView.</description><author>Wiley: Small: Table of Contents</author><pubDate>Mon, 22 Dec 2025 11:34:32 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202511276</guid></item><item><title>[Wiley: Small: Table of Contents] Boosting Gaseous Mercury Detection via Photooxidation‐Enrichment Fluorescent Membrane with Machine Learning</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202513585?af=R</link><description>Small, EarlyView.</description><author>Wiley: Small: Table of Contents</author><pubDate>Mon, 22 Dec 2025 11:29:41 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202513585</guid></item><item><title>[Recent Articles in Phys. Rev. Lett.] Gate-Tunable Spectrum and Charge Dispersion Mitigation in a Graphene Superconducting Qubit</title><link>http://link.aps.org/doi/10.1103/3gy7-2r3n</link><description>Author(s): Nicolas Aparicio, Simon Messelot, Edgar Bonet-Orozco, Eric Eyraud, Kenji Watanabe, Takashi Taniguchi, Johann Coraux, and Julien Renard&lt;br /&gt;&lt;p&gt;Controlling the energy spectrum of quantum-coherent superconducting circuits, i.e., the energies of excited states, the circuit anharmonicity, and the states’ charge dispersion, is essential for designing performant qubits. This control is usually achieved by adjusting the circuit’s geometry. &lt;i&gt;In sit…&lt;/i&gt;&lt;/p&gt;&lt;br /&gt;[Phys. Rev. Lett. 135, 266001] Published Mon Dec 22, 2025</description><author>Recent Articles in Phys. Rev. Lett.</author><pubDate>Mon, 22 Dec 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/3gy7-2r3n</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Potential‐Tailored Aryl–Sodium Reagent with Moderate Ionic Binding Strength Enables Precise yet Fast Hard Carbon Presodiation</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202519792?af=R</link><description>Angewandte Chemie International Edition, EarlyView.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Mon, 22 Dec 2025 05:36:53 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202519792</guid></item><item><title>[npj Computational Materials] Machine learning interatomic potential can infer electrical response</title><link>https://www.nature.com/articles/s41524-025-01911-z</link><description>&lt;p&gt;npj Computational Materials, Published online: 22 December 2025; &lt;a href="https://www.nature.com/articles/s41524-025-01911-z"&gt;doi:10.1038/s41524-025-01911-z&lt;/a&gt;&lt;/p&gt;Machine learning interatomic potential can infer electrical response</description><author>npj Computational Materials</author><pubDate>Mon, 22 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41524-025-01911-z</guid></item><item><title>[Applied Physics Letters Open Issues] High-throughput sensing of single-cell properties using parallel multi-stage cell deformation</title><link>https://pubs.aip.org/aip/apl/article/127/25/253702/3375461/High-throughput-sensing-of-single-cell-properties</link><description>&lt;span class="paragraphSection"&gt;Biophysical properties of single cells serve as label-free, noninvasive biomarkers for phenotyping. However, most techniques measure quantities dependent on multiple biophysical properties rather than individual ones, limiting their biological/clinical relevance. Here, we present a single-cell biophysical phenotyping technique that quantifies size (&lt;span style="font-style: italic;"&gt;Dc&lt;/span&gt;) and elastic modulus (&lt;span style="font-style: italic;"&gt;E&lt;/span&gt;) of cells deforming at different levels along constriction microchannels. A physical model is developed to resolve features of multiple deformation stages into &lt;span style="font-style: italic;"&gt;Dc&lt;/span&gt; and &lt;span style="font-style: italic;"&gt;E&lt;/span&gt;. Parallel-channel device design achieves a reasonably high throughput of ∼10&lt;sup&gt;4&lt;/sup&gt; cells/min. The measurement employs lock-in amplification-assisted electrokinetic sensing via embedded electrodes across three channel sections with varying constriction widths, inducing distinct cell deformations. We demonstrate the technique by profiling normal and cancerous breast cell lines (MCF-10A, MCF-7, and MDA-MB-231), as well as drug-treated cancer cells (cytochalasin D, cetuximab, and lysophosphatidic acid). The biophysical phenotyping enables cell classification with high accuracy (&amp;gt;95.45% via principal component analysis; &amp;gt;97.32% via machine learning). This approach offers robust and high-throughput cell classification, with potential applications in basic research and clinical diagnostics.&lt;/span&gt;</description><author>Applied Physics Letters Open Issues</author><pubDate>Mon, 22 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apl/article/127/25/253702/3375461/High-throughput-sensing-of-single-cell-properties</guid></item><item><title>[Applied Physics Letters Open Issues] Coherent phonon tunneling-driven ultralow and non-monotonic thermal conductivity in quasi-0D Cs 3 Cu 2 I 5</title><link>https://pubs.aip.org/aip/apl/article/127/25/252201/3375357/Coherent-phonon-tunneling-driven-ultralow-and-non</link><description>&lt;span class="paragraphSection"&gt;Low-dimensional copper halides have emerged as promising thermoelectric materials due to their phonon-glass electron-crystal behavior, yet their thermal transport mechanisms remain insufficiently understood. Using &lt;span style="font-style: italic;"&gt;ab initio&lt;/span&gt; calculations and a unified thermal transport theory, we identify that Cs&lt;sub&gt;3&lt;/sub&gt;Cu&lt;sub&gt;2&lt;/sub&gt;I&lt;sub&gt;5&lt;/sub&gt; possesses an ultralow lattice thermal conductivity (&lt;span style="font-style: italic;"&gt;κ&lt;/span&gt;&lt;sub&gt;L&lt;/sub&gt;) of 0.126 W/(m K) at room temperature (RT)—one of the lowest among metal halides. Above RT, coherent phonon tunneling dominates over particle-like propagation, resulting in glass-like &lt;span style="font-style: italic;"&gt;κ&lt;/span&gt;&lt;sub&gt;L&lt;/sub&gt; in all directions. Intriguingly, the competing contributions of coherent and incoherent terms induce an anomalous non-monotonic temperature dependence of &lt;span style="font-style: italic;"&gt;κ&lt;/span&gt;&lt;sub&gt;L&lt;/sub&gt; along the &lt;span style="font-style: italic;"&gt;c&lt;/span&gt; axis, with an initial decrease followed by an unexpected rise. This behavior arises from strong anharmonicity and dense flat phonon dispersions, driven by hierarchical bonding and structural complexity. Our work uncovers unconventional heat transport mechanisms in complex crystals with strong anharmonicity.&lt;/span&gt;</description><author>Applied Physics Letters Open Issues</author><pubDate>Mon, 22 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apl/article/127/25/252201/3375357/Coherent-phonon-tunneling-driven-ultralow-and-non</guid></item><item><title>[Applied Physics Letters Open Issues] Machine learning-assisted performance prediction of ZnMSnO-based thin-film transistors</title><link>https://pubs.aip.org/aip/apl/article/127/25/251903/3375349/Machine-learning-assisted-performance-prediction</link><description>&lt;span class="paragraphSection"&gt;Amorphous oxide semiconductors (AOS) are highly promising for optoelectronic devices due to their exceptional electrical properties and optical transparency. However, a significant barrier to their development is the lack of a comprehensive materials database, which hinders systematic studies and the discovery of emergent AOS materials. This study addresses this gap by constructing a dedicated database of zinc-tin-based doped oxide semiconductors (Zn–M–Sn–O) and their thin-film transistor (TFT) performance parameters, compiled from an extensive review of existing literature. Our research aims to perform a systematic analysis of the correlations between key material properties and device performance. We summarize and analyze existing Zn–M–Sn–O based optoelectronic devices, extracting key features such as material compositions, processing parameters, and performance metrics. These features are then used to construct feature vectors. By applying a machine learning algorithm to this dataset, we establish a performance prediction model for Zn–M–Sn–O TFTs. This machine learning-assisted approach allows us to efficiently screen materials and predict the optimal M element for high-performance devices. This methodology significantly accelerates the discovery and development of advanced AOS materials, paving the way for next-generation optoelectronic technologies.&lt;/span&gt;</description><author>Applied Physics Letters Open Issues</author><pubDate>Mon, 22 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apl/article/127/25/251903/3375349/Machine-learning-assisted-performance-prediction</guid></item><item><title>[Applied Physics Reviews Current Issue] Thermal conductivity limits of MoS 2 and MoSe 2 : Revisiting high-order anharmonic lattice dynamics with machine learning potentials</title><link>https://pubs.aip.org/aip/apr/article/12/4/041424/3375425/Thermal-conductivity-limits-of-MoS2-and-MoSe2</link><description>&lt;span class="paragraphSection"&gt;Group-VI transition metal dichalcogenides (TMDs), MoS&lt;sub&gt;2&lt;/sub&gt; and MoSe&lt;sub&gt;2&lt;/sub&gt;, have emerged as prototypical low-dimensional systems with distinctive phononic and electronic properties, making them attractive for applications in nanoelectronics, optoelectronics, and thermoelectrics. However, their reported lattice thermal conductivities ( κ) remain highly inconsistent, with experimental values and theoretical predictions differing by more than an order of magnitude. These discrepancies stem from uncertainties in measurement techniques, variations in computational protocols, and ambiguities in the treatment of higher-order anharmonic processes. In this study, we critically review these inconsistencies, first by mapping the spread of experimental and modeling results, and then by identifying the methodological origins of divergence. To this end, we bridge first-principles calculations, molecular dynamics simulations, and state-of-the-art machine learning force fields (MLFFs), including recently developed foundation models. We train and benchmark GAP, MACE, NEP, and HIPHIVE against density functional theory and rigorously evaluate the impact of third- and fourth-order phonon scattering processes on κ. The computational efficiency of MLFFs enables us to extend convergence tests beyond conventional limits and to validate predictions through homogeneous nonequilibrium molecular dynamics as well. Our analysis demonstrates that, contrary to some recent claims, fully converged four-phonon processes contribute negligibly to the intrinsic thermal conductivity of both MoS&lt;sub&gt;2&lt;/sub&gt; and MoSe&lt;sub&gt;2&lt;/sub&gt;. These findings not only refine the intrinsic transport limits of 2D TMDs but also establish MLFF-based approaches as a robust and scalable framework for predictive modeling of phonon-mediated thermal transport in low-dimensional materials.&lt;/span&gt;</description><author>Applied Physics Reviews Current Issue</author><pubDate>Mon, 22 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/12/4/041424/3375425/Thermal-conductivity-limits-of-MoS2-and-MoSe2</guid></item><item><title>[ChemRxiv] Missense mutations in cancer: in silico predictions, developing treatments, and overcoming cell resistance.</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-6928d?rft_dat=source%3Ddrss</link><description>Targeted therapies built around specific genetic driver mutations have become a cornerstone of precision oncology. These mutations, often found in oncogenes such as KRAS or tumor suppressors such as TP53, contribute to tumor initiation, progression, and therapeutic resistance. Recent successes with KRAS inhibitors targeting G12C and G12D mutations highlight the clinical potential of mutation-specific drug design. Concurrently, advances in machine learning have enhanced the prediction of missense variant effects by integrating amino acid dynamics, structural perturbations, and pathogenicity scoring. This review synthesizes current computational tools and emerging therapeutic strategies, including small-molecule inhibitors, protein degraders, proximity-based therapeutics, and gene or cellular therapies, to provide a comprehensive framework linking structure–function relationships to the rational design of next-generation cancer treatments.</description><author>ChemRxiv</author><pubDate>Mon, 22 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-6928d?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Domain Oriented Universal Machine Learning Potential Enables Fast Exploration of Chemical Space of Battery Electrolytes</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-fnw1w-v2?rft_dat=source%3Ddrss</link><description>Li-ion batteries, widely used in electronic devices, electric vehicles, and aviation, demand high energy density, fast charging capabilities, and broad operating temperature ranges. Computations combined with experiments have gained increasing attention for electrolyte development. However, the inherent complexity of electrolytes poses a significant challenge. Classical molecular dynamics often fails due to inaccuracies in force field parameters, while ab initio calculationsarelimitedbyhighcomputationalcosts. Recently, machinelearning molecular dynamics has emerged as an efficient and accurate alternative. However, its application is hindered by limited transferability of machine learning potentials. In this work, we developed a universal machine learning potential for electrolytes using an iterative training approach on randomly composed datasets, enabling the accurate computation of key properties for a broad range of electrolytes via molecular dynamics. Furthermore, coordination dynamics analysis of Li ion, by quantifying the coordination lifetime, provides a direct, quantitative measure of solvation strength. The universal machine learning potential for electrolytes facilitates the prediction and optimization of electrolyte properties, offering a powerful tool for electrolyte design.</description><author>ChemRxiv</author><pubDate>Mon, 22 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-fnw1w-v2?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Large-scale structure- and sequence-based comparative analysis enables functional annotation of animal venom peptides</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-9gd3c?rft_dat=source%3Ddrss</link><description>Animal venoms constitute one of the most chemically diverse and pharmacologically rich peptide repertoires in nature, yet the vast majority of venom peptides remain structurally and functionally unannotated due to limited material availability and a paucity of experimentally solved structures. This knowledge gap has significantly constrained both mechanistic understanding and translational applications of venom-derived molecules. Here we conduct a comprehensive structure- and sequence-based comparative analysis of close to 4,000 venom peptides (i.e. teretoxins and conotoxins) from marine snails using AlphaFold2. While conotoxins have been studied for over six decades, teretoxins remain largely unexplored. Our study provides the first comprehensive structural classification and functional annotation of the entire known teretoxin repertoire, substantially expanding the venom peptide landscape. Structural clustering analysis revealed that both teretoxins and conotoxins form a large number of structural clusters (225 and 307 clusters, respectively), with each cluster characterized by conserved cysteine frameworks and disulfide connectivity. Importantly, combining structural clusters with phylogenetic analysis revealed that structure prediction together with cysteine frameworks offer an improved strategy for venom peptide classification. We used predicted disulfide connectivity from derived from AlphaFold2 models to annotate the majority of uncharacterized conotoxins on ConoServer database, addressing a long-standing classification bottleneck in venom peptide classification efforts. As a concrete demonstration of functional prediction, co-clustering of predicted teretoxin and conotoxin structures led to the identification of teretoxins structurally homologous to conotoxins with described activities, suggesting shared functional activities such as Kunitz-type peptide activity, neurotoxicity, and ion channel inhibition. Using docking and molecular dynamics (MD) simulations on our Kunitz-type peptide co-cluster, we observed that conotoxin P07849 and teretoxin Tar2.9 engage trypsin in a manner similar to the Kunitz Domain 1 (KD1) of the Alzheimer’s amyloid beta-protein precursor (APPI), a previously unappreciated protease inhibitory role for this venom peptide family. Overall, this work establishes a scalable, structure-centric framework that combines structure prediction, structural clustering, co-clustering, and phylogenetics for deorphanizing venom peptides, enabling predicted annotation and functional inference for future experimental, pharmacological, and therapeutic exploration.</description><author>ChemRxiv</author><pubDate>Mon, 22 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-9gd3c?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Performance of dispersion models in predicting ambient hydrocarbon concentrations at a regional air quality monitor in an oil and gas producing region</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-xkhh9?rft_dat=source%3Ddrss</link><description>The performance of four widely used dispersion models (AERMOD, a single equation Gaussian formulation, and two versions of CALPUFF) for predicting ambient hydrocarbon concentrations at a regional air quality monitor in the Eagle Ford Shale oil and gas production region was assessed. Model performance is found to vary considerably based on the performance objective, meteorological conditions, and temporal resolution. Among the models evaluated in this work, the methods used to estimate the dispersion coefficients and whether the model was plume- or puff-based strongly influenced model performance. Uncertainties in meteorological and emissions inputs also played an important role in model performance, but the significance of their impact varied depending on the performance objective. Techniques to identify and address model uncertainties and for selecting the best performing model for a given application are suggested.</description><author>ChemRxiv</author><pubDate>Mon, 22 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-xkhh9?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Machine Learning-Guided Scope Selection to Balance Performance and Substrate Similarity</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-r0sst?rft_dat=source%3Ddrss</link><description>The determination of a reaction substrate scope enables downstream users to decide whether the reaction in question is suitable for their envisioned application. The information content of the scope, as demonstrated by its performance and diversity, is crucial to inform the quality of this decision. Herein, we report a broadly applicable and easy to use machine learning algorithm, ScopeBO, for the selection of scopes that balance these two aspects. We use the Vendi score as a metric for scope diversity and establish a scope score that quantifies scope performance within the context of a specific chemical search space. The hyperparameters of ScopeBO are optimized using these metrics, and its performance is validated with several reaction datasets, demonstrating favorable performance against that of alternative selection methods. Through this quantitative optimization, ScopeBO provides an approach towards objective and standardized scope selection that maximizes the information content of the evaluated substrates.</description><author>ChemRxiv</author><pubDate>Mon, 22 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-r0sst?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Relationship Between Local Disorder and Atomic Motion in an Antiperovskite Solid Electrolyte</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-dxdtz?rft_dat=source%3Ddrss</link><description>Solid-state electrolytes are an alternative to conventional liquid systems for safer and more efficient batteries, whereas a shift from Li to Na would unlock systems with increased resource availability and simplified supply chains. Antiperovskite Na2[NH2][BH4] recently emerged as a candidate that showcases that internal polyanion dynamics can facilitate Na transport. However, experimental validation of the predicted mechanisms of atomic motion remains scarce. To this end, we investigate the intricate relationship between local atomic disorder and dynamic motion. Maps of atomic disorder from total scattering data reproduce the predictions of polyanion rotation and Na translation by ab initio molecular dynamics (AIMD). The comparison also reveals the concurrence of BD4- translations, which were overlooked in previous analyses, and the existence of static disorder due to the different degrees of freedom of each anion, even while maintaining their ideal shape. This complex interplay refines mechanisms governing ion dynamics that determine solid-state electrolyte functionality. Realistic design strategies for rotor-based electrolytes must explicitly account for polyanion translation and static disorder, rather than optimizing rotational freedom in isolation. The combination of total scattering experiments with AIMD provides a route to screen potential polyanion-based candidates for favorable multi-modal disorder, thus steering the discovery of new phases with transformational ionic conductivity.</description><author>ChemRxiv</author><pubDate>Mon, 22 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-dxdtz?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Multi-Objective Catalyst Discovery in High-Entropy Alloy Composition Space: The Role of Noble Metals on the Pareto Front for Oxygen Reduction Reaction</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-cq92m?rft_dat=source%3Ddrss</link><description>Discovering new materials for electrocatalytic energy conversion reactions is a key step toward energy sustainability. However, for catalysts to be viable in practice, they must perform in multiple, potentially conflicting objectives. We demonstrate this challenge for the acidic oxygen reduction reaction (ORR), where activity, stability, and material cost must be balanced. Using the continuous composition space of high-entropy alloys (HEAs) together with our established models for activity and dissolution, we identify a Pareto-optimal set of ORR catalysts within the Ag–Au–Cu–Ir–Pd–Pt–Rh–Ru system via multi-objective Bayesian optimization. Additionally, we introduce a fine-tuned machine learning model that predicts adsorption energies for alloys spanning 12 elements and 9 adsorbates. Our results show that alloying expands the hypervolume spanned by the Pareto front, consisting of low- to medium-entropy alloys composed primarily of Ag, Au, Cu, Pd, and Pt. We further propose an approach for analyzing the Pareto front by quantifying the loss in hypervolume when critical elements (Au, Pd, and Pt) are removed, clarifying their relative contributions to optimal performance. This work highlights the need to consider all relevant objectives in catalyst optimization and the advantage of HEAs as a powerful platform for multi-objective catalyst discovery.</description><author>ChemRxiv</author><pubDate>Mon, 22 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-cq92m?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Agent-based framework for modeling hyperlocal urban air quality</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-v1lhr?rft_dat=source%3Ddrss</link><description>Urban air quality exhibits significant spatial and temporal heterogeneity at hyperlocal scales, necessitating advanced modeling paradigms that can bridge the gap between computationally intensive physics-based models and empirically-driven statistical approaches. This paper introduces a novel agent-based modeling framework specifically designed for hyperlocal air quality assessment, capable of providing descriptive, predictive, and prescriptive analysis. The proposed framework discretizes urban environments into interacting agents, with pollutant dynamics governed by a parameterized mass balance that preserves fundamental physics while maintaining computational efficiency. The framework is demonstrated through a case study in Chennai, India, using mobile monitoring data across a 25 km route with 250 m spatial resolution. Geospatial features (traffic, land use, meteorology) are encoded as agent properties through physically interpretable parameters. This enables transparent attribution of pollution sources and transport pathways, thereby strengthening the framework’s descriptive capabilities. The approach successfully captures complex spatio-temporal pollution dynamics and describes pollution hotspots by attributing them to source and transport influences. Predictive capabilities are demonstrated through spatio-temporal interpolation and temporal forecasting. Spatio-temporal formulation of the framework enables it to outperform regular unidimensional methods. The discrete agent structure facilitates prescriptive applications, demonstrated here through identification of least-exposure routes between locations. The unification of descriptive, predictive and prescriptive capabilities within a single interpretable framework makes it a potentially valuable tool for urban environmental management and real-time decision support systems.</description><author>ChemRxiv</author><pubDate>Mon, 22 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-v1lhr?rft_dat=source%3Ddrss</guid></item><item><title>[iScience] Widely Targeted Metabolomics and Machine Learning Identify Succinate as a Key Metabolite in Sepsis-Associated Encephalopathy</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02781-6?rss=yes</link><description>Sepsis-associated encephalopathy (SAE) is a common and serious complication of sepsis that leads to acute brain dysfunction and long-term cognitive impairment. We used widely targeted LC–MS/MS plasma metabolomics in 29 healthy controls, 32 sepsis patients, and 27 SAE patients, combined with machine learning, to define metabolic patterns across these groups. This approach identified 12 discriminatory metabolites, with succinate showing a stepwise increase from health to sepsis to SAE and associations with clinical severity scores.</description><author>iScience</author><pubDate>Mon, 22 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02781-6?rss=yes</guid></item><item><title>[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Feature-Selective Preprocessing with Electrically Robust Boron Nitride-Based Dynamic Memristors for Reliable Lightweight Neural Networks</title><link>http://dx.doi.org/10.1021/acsnano.5c16967</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsnano.5c16967/asset/images/medium/nn5c16967_0007.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.5c16967&lt;/div&gt;</description><author>ACS Nano: Latest Articles (ACS Publications)</author><pubDate>Sun, 21 Dec 2025 18:05:25 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsnano.5c16967</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Interpreting X-ray Diffraction Patterns of Metal–Organic Frameworks via Generative Artificial Intelligence</title><link>http://dx.doi.org/10.1021/jacs.5c16416</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c16416/asset/images/medium/ja5c16416_0007.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.5c16416&lt;/div&gt;</description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Sat, 20 Dec 2025 15:03:45 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c16416</guid></item><item><title>[npj Computational Materials] Free-energy perturbation in the exchange-correlation space accelerated by machine learning: application to silica polymorphs</title><link>https://www.nature.com/articles/s41524-025-01874-1</link><description>&lt;p&gt;npj Computational Materials, Published online: 20 December 2025; &lt;a href="https://www.nature.com/articles/s41524-025-01874-1"&gt;doi:10.1038/s41524-025-01874-1&lt;/a&gt;&lt;/p&gt;Free-energy perturbation in the exchange-correlation space accelerated by machine learning: application to silica polymorphs</description><author>npj Computational Materials</author><pubDate>Sat, 20 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41524-025-01874-1</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] A Transformative Molecular Muscle Solid Electrolyte</title><link>http://dx.doi.org/10.1021/jacs.5c18427</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c18427/asset/images/medium/ja5c18427_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.5c18427&lt;/div&gt;</description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Fri, 19 Dec 2025 20:12:03 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c18427</guid></item><item><title>[Wiley: Small Structures: Table of Contents] Li6−xFe1−xAlxCl8 Solid Electrolytes for Cost‐Effective All‐Solid‐State LiFePO4 Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/sstr.202500728?af=R</link><description>Small Structures, EarlyView.</description><author>Wiley: Small Structures: Table of Contents</author><pubDate>Fri, 19 Dec 2025 18:40:34 GMT</pubDate><guid isPermaLink="true">10.1002/sstr.202500728</guid></item><item><title>[Wiley: Small: Table of Contents] Unravelling Electronic Structure and Molecular Vibrations of Proteins in Virus Using Novel Correlated Plasmon‐Enhanced Raman Spectroscopy With Machine Learning</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202506967?af=R</link><description>Small, EarlyView.</description><author>Wiley: Small: Table of Contents</author><pubDate>Fri, 19 Dec 2025 11:08:23 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202506967</guid></item><item><title>[Recent Articles in Phys. Rev. B] Vision transformer neural quantum states for impurity models</title><link>http://link.aps.org/doi/10.1103/8n2h-p7w5</link><description>Author(s): Xiaodong Cao, Zhicheng Zhong, and Yi Lu&lt;br /&gt;&lt;p&gt;Transformer neural networks, known for their ability to recognize complex patterns in high-dimensional data, offer a promising framework for capturing many-body correlations in quantum systems. We employ an adapted vision transformer (ViT) architecture to model quantum impurity models, optimizing it…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 112, 235155] Published Fri Dec 19, 2025</description><author>Recent Articles in Phys. Rev. B</author><pubDate>Fri, 19 Dec 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/8n2h-p7w5</guid></item><item><title>[Recent Articles in Phys. Rev. B] Renormalized quantum anharmonicity enhanced electron-phonon coupling in the ambient-pressure compound $\mathrm{Rb}{\mathrm{H}}_{6}$</title><link>http://link.aps.org/doi/10.1103/q8sc-phdp</link><description>Author(s): Zhongyu Wan, Guo-Hua Zhong, Ruiqin Zhang, and Hai-Qing Lin&lt;br /&gt;&lt;p&gt;Hydrogen-based compounds are promising candidates for room-temperature superconductivity. However, hydrogen-related anharmonic quantum effects have created a huge gap between experiments and theories. The compound $\mathrm{Rb}{\mathrm{H}}_{6}$ exemplifies the impacts of quantum fluctuations and latt…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 112, L220504] Published Fri Dec 19, 2025</description><author>Recent Articles in Phys. Rev. B</author><pubDate>Fri, 19 Dec 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/q8sc-phdp</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Dual‐Function Antibacterial and Antibiofilm Agent Based on a Confinement‐Activated Fluorescent System in Water</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202521285?af=R</link><description>Angewandte Chemie International Edition, EarlyView.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Fri, 19 Dec 2025 05:42:28 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202521285</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Quantifying Additive Manufacturing Vapor Plumes Using Laser‐Induced Breakdown Spectroscopy, Synchrotron X‐Ray Radiography and Simulations</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202513652?af=R</link><description>Advanced Science, EarlyView.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Fri, 19 Dec 2025 03:30:01 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202513652</guid></item><item><title>[npj Computational Materials] Publisher Correction: Machine learning potentials for alloys: a detailed workflow to predict phase diagrams and benchmark accuracy</title><link>https://www.nature.com/articles/s41524-025-01913-x</link><description>&lt;p&gt;npj Computational Materials, Published online: 19 December 2025; &lt;a href="https://www.nature.com/articles/s41524-025-01913-x"&gt;doi:10.1038/s41524-025-01913-x&lt;/a&gt;&lt;/p&gt;Publisher Correction: Machine learning potentials for alloys: a detailed workflow to predict phase diagrams and benchmark accuracy</description><author>npj Computational Materials</author><pubDate>Fri, 19 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41524-025-01913-x</guid></item><item><title>[npj Computational Materials] Alloy design integrating natural language processing and machine learning: breakthrough development of low-cost, high-performance Ni-based single-crystal superalloys</title><link>https://www.nature.com/articles/s41524-025-01906-w</link><description>&lt;p&gt;npj Computational Materials, Published online: 19 December 2025; &lt;a href="https://www.nature.com/articles/s41524-025-01906-w"&gt;doi:10.1038/s41524-025-01906-w&lt;/a&gt;&lt;/p&gt;Alloy design integrating natural language processing and machine learning: breakthrough development of low-cost, high-performance Ni-based single-crystal superalloys</description><author>npj Computational Materials</author><pubDate>Fri, 19 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41524-025-01906-w</guid></item><item><title>[Joule] Boosting ionic conductivity of fluoride electrolytes by polyanion coordination chemistry enabling 5 V-Class all-solid-state batteries</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00414-3?rss=yes</link><description>This study developed a novel class of highly conductive amorphous fluoride solid-state electrolytes (SSEs) LixTi(PO4)x/3F4, using a polyanion coordination strategy. The optimized Li1.3Ti(PO4)1.3/3F4 achieves a Li+ conductivity of 1.16 × 10−5 S cm−1, two orders of magnitude higher than that of analogous Li2TiF6. Combining the inherent high-voltage stability of fluoride and the excellent ionic conductivity of Li1.3Ti(PO4)1.3/3F4, this material enables superior 5 V-class all-solid-state battery performance. This work opens a new avenue for designing high-conductivity fluoride SSEs and advancing the performance of 5 V-class all-solid-state batteries.</description><author>Joule</author><pubDate>Fri, 19 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00414-3?rss=yes</guid></item><item><title>[RSC - Digital Discovery latest articles] Adsorb-Agent: Autonomous Identification of Stable Adsorption Configurations via Large Language Model Agent</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00298B</link><description>&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2025, Accepted Manuscript&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00298B, Paper&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;Janghoon Ock, Radheesh Sharma Meda, Tirtha Vinchurkar, Yayati Jadhav, Amir Barati Farimani&lt;br /&gt;Adsorption energy is a key reactivity descriptor in catalysis. Determining adsorption energy requires evaluating numerous adsorbate-catalyst configurations, making it computationally intensive. Current methods rely on exhaustive sampling, which does not...&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Fri, 19 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00298B</guid></item><item><title>[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Instant Prediction of Moire Superlattice Relaxation in Twisted Bilayers of Transition Metal Dichalcogenides Using Different Neural Network Architectures</title><link>http://dx.doi.org/10.1021/acs.jpcc.5c07169</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpcc.5c07169/asset/images/medium/jp5c07169_0008.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.5c07169&lt;/div&gt;</description><author>The Journal of Physical Chemistry C: Latest Articles (ACS Publications)</author><pubDate>Thu, 18 Dec 2025 11:50:58 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpcc.5c07169</guid></item><item><title>[Recent Articles in Phys. Rev. Lett.] Variational Machine Learning Model for Electronic Structure Optimization via the Density Matrix</title><link>http://link.aps.org/doi/10.1103/wl9w-8g8r</link><description>Author(s): Luqi Dong, Shuxiang Yang, Su-Huai Wei, and Yunhao Lu&lt;br /&gt;&lt;p&gt;We present a novel approach that combines machine learning with direct variational energy optimization via the density matrix to solve the Kohn-Sham equation in density functional theory. Instead of relying on the conventional self-consistent field method, our approach directly optimizes the ground …&lt;/p&gt;&lt;br /&gt;[Phys. Rev. Lett. 135, 256403] Published Thu Dec 18, 2025</description><author>Recent Articles in Phys. Rev. Lett.</author><pubDate>Thu, 18 Dec 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/wl9w-8g8r</guid></item><item><title>[Recent Articles in Phys. Rev. B] One-defect one-potential strategy for accurate machine learning prediction of phonons in defect-containing supercells</title><link>http://link.aps.org/doi/10.1103/kr3z-4nzv</link><description>Author(s): Junjie Zhou, Xinpeng Li, Menglin Huang, and Shiyou Chen&lt;br /&gt;&lt;p&gt;Atomic vibrations play a critical role in phonon-assisted electronic transitions at defects in solids. However, accurate phonon calculations in defect-laden systems are often hindered by the high computational cost of large-supercell first-principles calculations. Recently, foundation models, such a…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 112, 235205] Published Thu Dec 18, 2025</description><author>Recent Articles in Phys. Rev. B</author><pubDate>Thu, 18 Dec 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/kr3z-4nzv</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Polymorphism Crystal Structure Prediction with Adaptive Space Group Diversity Control</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202510792?af=R</link><description>Advanced Science, Volume 12, Issue 47, December 18, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 18 Dec 2025 09:38:21 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202510792</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Computationally‐Guided Development of Sulfide Solid Electrolyte Powder Coatings for Enhanced Stability and Performance of Solid‐State Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202513191?af=R</link><description>Advanced Science, Volume 12, Issue 47, December 18, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 18 Dec 2025 09:38:21 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202513191</guid></item><item><title>[Proceedings of the National Academy of Sciences: Physical Sciences] Uncovering inequalities in new knowledge learning by large language models across different languages</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2514626122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 51, December 2025. &lt;br /&gt;SignificanceLarge language models (LLMs) are transforming daily life, yet users across different languages may not benefit equally. Our study shows that LLMs face greater challenges in learning new knowledge and resisting incorrect or misleading ...</description><author>Proceedings of the National Academy of Sciences: Physical Sciences</author><pubDate>Thu, 18 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2514626122?af=R</guid></item><item><title>[Proceedings of the National Academy of Sciences: Physical Sciences] Heavy-tailed update distributions arise from information-driven self-organization in nonequilibrium learning</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2523012122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 51, December 2025. &lt;br /&gt;SignificanceArtificial neural networks can adapt to tasks while freely exploring possible solutions, similar to how humans balance curiosity with goal-driven behavior. We show that during training, such networks naturally operate near a critical state. ...</description><author>Proceedings of the National Academy of Sciences: Physical Sciences</author><pubDate>Thu, 18 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2523012122?af=R</guid></item><item><title>[AAAS: Science: Table of Contents] State-independent ionic conductivity</title><link>https://www.science.org/doi/abs/10.1126/science.adk0786?af=R</link><description>Science, Volume 390, Issue 6779, Page 1254-1258, December 2025. &lt;br /&gt;</description><author>AAAS: Science: Table of Contents</author><pubDate>Thu, 18 Dec 2025 07:00:11 GMT</pubDate><guid isPermaLink="true">https://www.science.org/doi/abs/10.1126/science.adk0786?af=R</guid></item><item><title>[AAAS: Science: Table of Contents] Scientific production in the era of large language models</title><link>https://www.science.org/doi/abs/10.1126/science.adw3000?af=R</link><description>Science, Volume 390, Issue 6779, Page 1240-1243, December 2025. &lt;br /&gt;</description><author>AAAS: Science: Table of Contents</author><pubDate>Thu, 18 Dec 2025 07:00:11 GMT</pubDate><guid isPermaLink="true">https://www.science.org/doi/abs/10.1126/science.adw3000?af=R</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Homogeneous Microphase Structure and Polymer‐Dominated Ion Transport Network Enable Durable Quasi‐Solid‐State Sodium Metal Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202527023?af=R</link><description>Advanced Functional Materials, EarlyView.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Thu, 18 Dec 2025 05:55:56 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202527023</guid></item><item><title>[Nature Machine Intelligence] A psychometric framework for evaluating and shaping personality traits in large language models</title><link>https://www.nature.com/articles/s42256-025-01115-6</link><description>&lt;p&gt;Nature Machine Intelligence, Published online: 18 December 2025; &lt;a href="https://www.nature.com/articles/s42256-025-01115-6"&gt;doi:10.1038/s42256-025-01115-6&lt;/a&gt;&lt;/p&gt;Serapio-García, Safdari and colleagues develop a method based on psychometric tests to measure and validate personality-like traits in LLMs. Large, instruction-tuned models give reliable personality measurement results, and specific personality profiles can be mimicked in downstream tasks.</description><author>Nature Machine Intelligence</author><pubDate>Thu, 18 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s42256-025-01115-6</guid></item><item><title>[Wiley: Small: Table of Contents] Probing Lattice Anharmonicity and Thermal Transport in Ultralow‐κ Materials Using Machine Learning Interatomic Potentials</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202513476?af=R</link><description>Small, EarlyView.</description><author>Wiley: Small: Table of Contents</author><pubDate>Wed, 17 Dec 2025 20:22:25 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202513476</guid></item><item><title>[Wiley: Small: Table of Contents] Ion Migration Control in Lead‐Free Halide Perovskite Transistors for Logic and Neuromorphic Circuits</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202509737?af=R</link><description>Small, EarlyView.</description><author>Wiley: Small: Table of Contents</author><pubDate>Wed, 17 Dec 2025 19:52:40 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202509737</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Convergent Evolution: Self-Assembly of Small Molecule, Polymeric, and Inorganic Contrast Agents toward Advanced MRI</title><link>http://dx.doi.org/10.1021/jacs.4c11767</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.4c11767/asset/images/medium/ja4c11767_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.4c11767&lt;/div&gt;</description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Wed, 17 Dec 2025 19:33:22 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.4c11767</guid></item><item><title>[ACS Nano: Latest Articles (ACS Publications)] [ASAP] van Hove Source for Ultralow Power Field-Effect Transistors</title><link>http://dx.doi.org/10.1021/acsnano.5c17157</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsnano.5c17157/asset/images/medium/nn5c17157_0005.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.5c17157&lt;/div&gt;</description><author>ACS Nano: Latest Articles (ACS Publications)</author><pubDate>Wed, 17 Dec 2025 18:12:49 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsnano.5c17157</guid></item><item><title>[ACS Nano: Latest Articles (ACS Publications)] [ASAP] An Ultralong-Circulating Tantalum-Based Computed Tomography Contrast Agent for Vascular Imaging in Large Animals</title><link>http://dx.doi.org/10.1021/acsnano.5c13773</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsnano.5c13773/asset/images/medium/nn5c13773_0007.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.5c13773&lt;/div&gt;</description><author>ACS Nano: Latest Articles (ACS Publications)</author><pubDate>Wed, 17 Dec 2025 17:49:52 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsnano.5c13773</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Machine Learning‐assisted Ultrasensitive SERS Immunoassays Across Wide Concentration Ranges Toward Clinical Ovarian Cancer Diagnosis</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202509813?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 51, December 16, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Wed, 17 Dec 2025 14:49:25 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202509813</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Prediction and Fine Screening of Small Molecular Passivation Materials for High‐Efficiency Perovskite Solar Cells Via an Enhanced Machine Learning Workflow</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202511549?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 51, December 16, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Wed, 17 Dec 2025 14:49:25 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202511549</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Tailoring Graphite Interlayers with Electron‐Acceptor Bridges Raises Ion Diffusion Kinetics for Ultrafast Charging Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202509207?af=R</link><description>Advanced Materials, Volume 37, Issue 50, December 17, 2025.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Wed, 17 Dec 2025 14:13:34 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202509207</guid></item><item><title>[Wiley: Small: Table of Contents] Interfacial Catalysis Engineering of Solid Electrolyte Interphase Toward High‐Performance Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202509725?af=R</link><description>Small, Volume 21, Issue 50, December 17, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Wed, 17 Dec 2025 12:51:53 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202509725</guid></item><item><title>[Wiley: Small: Table of Contents] In Situ Construction of Dual‐Functional UiO‐66‐NH2 Coated Li1.3Al0.3Ti1.7(PO4)3 to Achieve Lithium Metal Cells with Efficient Ion Transport in Quasi‐Solid Electrolytes</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202506170?af=R</link><description>Small, Volume 21, Issue 50, December 17, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Wed, 17 Dec 2025 12:51:53 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202506170</guid></item><item><title>[Wiley: Small: Table of Contents] 1D Lithium‐Ion Transport in a LiMn2O4 Nanowire Cathode during Charge–Discharge Cycles</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202507305?af=R</link><description>Small, Volume 21, Issue 50, December 17, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Wed, 17 Dec 2025 12:51:53 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202507305</guid></item><item><title>[Wiley: Small: Table of Contents] A Reversible Zinc Metal Anode with an Inorganic/Organic Solid Electrolyte Interphase Enriched for Epitaxial Deposition Along the Zn (101) Plane</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202510895?af=R</link><description>Small, Volume 21, Issue 50, December 17, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Wed, 17 Dec 2025 12:51:53 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202510895</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Smart Wound Management System Capable of On‐Chip Machine Learning and Closed‐Loop Therapeutic Feedback</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202522329?af=R</link><description>Advanced Functional Materials, EarlyView.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Wed, 17 Dec 2025 06:06:36 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202522329</guid></item><item><title>[Nature Materials] Probing frozen solid electrolyte interphases</title><link>https://www.nature.com/articles/s41563-025-02443-z</link><description>&lt;p&gt;Nature Materials, Published online: 17 December 2025; &lt;a href="https://www.nature.com/articles/s41563-025-02443-z"&gt;doi:10.1038/s41563-025-02443-z&lt;/a&gt;&lt;/p&gt;Probing frozen solid electrolyte interphases</description><author>Nature Materials</author><pubDate>Wed, 17 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41563-025-02443-z</guid></item><item><title>[Cell Reports Physical Science] Accelerated inorganic materials design with generative AI agents</title><link>https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00618-6?rss=yes</link><description>Takahara et al. introduce MatAgent, a generative AI agent for inorganic materials design that integrates large language model reasoning with diffusion-based generation and property prediction. The human-inspired reasoning process facilitates interpretable and property-directed discovery of inorganic materials.</description><author>Cell Reports Physical Science</author><pubDate>Wed, 17 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00618-6?rss=yes</guid></item><item><title>[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Fluorinated Halide Solid Electrolytes for High-Voltage All-Solid-State Sodium-Ion Batteries Enabling Reversible Oxygen Redox</title><link>http://dx.doi.org/10.1021/acsenergylett.5c03248</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsenergylett.5c03248/asset/images/medium/nz5c03248_0005.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.5c03248&lt;/div&gt;</description><author>ACS Energy Letters: Latest Articles (ACS Publications)</author><pubDate>Tue, 16 Dec 2025 20:00:00 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsenergylett.5c03248</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Highly Accurate and Fast Prediction of MOF Free Energy via Machine Learning</title><link>http://dx.doi.org/10.1021/jacs.5c13960</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c13960/asset/images/medium/ja5c13960_0011.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.5c13960&lt;/div&gt;</description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Tue, 16 Dec 2025 17:08:56 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c13960</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Mechanically Robust Bilayer Solid Electrolyte Interphase Enabled by Sequential Decomposition Mechanism for High‐Performance Micron‐Sized SiOx Anodes</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202514076?af=R</link><description>Angewandte Chemie International Edition, Volume 64, Issue 51, December 15, 2025.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Tue, 16 Dec 2025 15:14:44 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202514076</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Machine Learning‐Driven Automated Synthesis of Polysubstituted Gentisaldehydes</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202515595?af=R</link><description>Angewandte Chemie International Edition, Volume 64, Issue 51, December 15, 2025.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Tue, 16 Dec 2025 15:14:44 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202515595</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Uphill Anion Transporters with Ultrahigh Efficiency Based on N‐Heterocyclic Carbene Metal Complexes</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202518136?af=R</link><description>Angewandte Chemie International Edition, Volume 64, Issue 51, December 15, 2025.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Tue, 16 Dec 2025 15:14:44 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202518136</guid></item><item><title>[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Ionic Liquids in Quasi-Solid-State Li–S Batteries with Sulfide-Based Solid Electrolytes: A Density Functional Theory and Ab Initio Molecular Dynamics Study</title><link>http://dx.doi.org/10.1021/acs.jpcc.5c05916</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpcc.5c05916/asset/images/medium/jp5c05916_0019.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.5c05916&lt;/div&gt;</description><author>The Journal of Physical Chemistry C: Latest Articles (ACS Publications)</author><pubDate>Tue, 16 Dec 2025 14:13:16 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpcc.5c05916</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] How Machine Learning Has Driven the Development of Rechargeable Ion Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202504095?af=R</link><description>Advanced Energy Materials, Volume 15, Issue 47, December 16, 2025.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 16 Dec 2025 10:18:19 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202504095</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Interplay Between the Dissolved Mn2+ and Solid Electrolyte Interphases of Graphite Anode</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202503489?af=R</link><description>Advanced Energy Materials, Volume 15, Issue 47, December 16, 2025.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 16 Dec 2025 10:18:19 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202503489</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] From HF Scavenging to Li‐Ion Transport Enhancement: Multifunctional Separator Enabling Stable Li Metal Batteries in Carbonate‐Based Electrolytes</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202505601?af=R</link><description>Advanced Energy Materials, EarlyView.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 16 Dec 2025 09:58:08 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202505601</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Insight Into All‐Solid‐State Lithium‐Sulfur Batteries: Challenges and Interface Engineering at the Electrode‐Sulfide Solid Electrolyte Interface</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202504926?af=R</link><description>Advanced Energy Materials, EarlyView.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 16 Dec 2025 09:45:18 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202504926</guid></item><item><title>[Proceedings of the National Academy of Sciences: Physical Sciences] Designing strongly coupled polaritonic structures via statistical machine learning</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2526690122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 51, December 2025. &lt;br /&gt;SignificanceStrong coupling photonics enables precise control of light at subwavelength scales, offering transformative potential in energy conversion and optical information processing. However, designing these systems remains a significant challenge due ...</description><author>Proceedings of the National Academy of Sciences: Physical Sciences</author><pubDate>Tue, 16 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2526690122?af=R</guid></item><item><title>[Applied Physics Letters Current Issue] Ultrafast laser-induced anharmonic lattice dynamics and nonlinear optical modulation in croconic acid</title><link>https://pubs.aip.org/aip/apl/article/127/24/241102/3374918/Ultrafast-laser-induced-anharmonic-lattice</link><description>&lt;span class="paragraphSection"&gt;Ultrafast laser excitation offers a powerful means to modulate material properties on femtosecond timescales. Here, we investigate croconic acid, a hydrogen-bonded organic ferroelectric, using real-time time-dependent density functional theory to uncover the microscopic mechanisms of light-induced structural transitions and nonlinear optical responses. High-order harmonic generation in croconic acid is found to be highly sensitive to proton displacement within hydrogen bonds, with polarization switching reshaping internal electronic asymmetry and modulating intersite electron currents. Subangstrom-scale lattice distortions induce marked enhancements or suppressions in the harmonics, highlighting the extreme sensitivity of the nonlinear response to hydrogen-bond configuration. These results reveal a light-driven electron–proton–lattice interaction mechanism in organic ferroelectrics, providing a route toward tunable ultrafast photonic and optoelectronic devices based on molecular materials.&lt;/span&gt;</description><author>Applied Physics Letters Current Issue</author><pubDate>Tue, 16 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apl/article/127/24/241102/3374918/Ultrafast-laser-induced-anharmonic-lattice</guid></item><item><title>[Applied Physics Letters Current Issue] Spin-splitting-torque-driven field-free perpendicular magnetization switching in RuO 2 /synthetic antiferromagnet heterostructures for spintronic convolutional neural networks</title><link>https://pubs.aip.org/aip/apl/article/127/24/242405/3374916/Spin-splitting-torque-driven-field-free</link><description>&lt;span class="paragraphSection"&gt;With the growing demand for low-power and high-speed spintronic devices, the development of advanced material systems with efficient spin control capabilities has emerged as a central focus in spintronics research. Here, we propose a fully antiferromagnetic device architecture based on a magnetically compensated RuO&lt;sub&gt;2&lt;/sub&gt;/synthetic antiferromagnet heterostructure, achieving fully electrical writing and reading functionalities. This design, characterized by its negligible stray field and deterministic field-free switching, is inherently suitable for large-scale neuromorphic integration. In a proof-of-concept demonstration, we showcase the implementation of an all-spintronic convolutional neural network using this architecture, achieving a high recognition accuracy of 98.7% on the handwritten digit classification task.&lt;/span&gt;</description><author>Applied Physics Letters Current Issue</author><pubDate>Tue, 16 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apl/article/127/24/242405/3374916/Spin-splitting-torque-driven-field-free</guid></item><item><title>[iScience] What Makes a Scent Trigger a Memory? A Cognitive Decomposition of Odor-Evoked Retrieval</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02728-2?rss=yes</link><description>A single scent can unlock vivid memories. This study investigates the factors that make some odors more evocative than others. We examined odor-evoked episodic memory in 106 participants who experienced odors embedded in distinct visuospatial contexts, and whose memory was tested 24–72 hours later. The protocol empirically dissociates odor recognition (“I’ve already smelled this scent”) and associative memory (“It evokes a memory”) processes. Using machine learning with SHapley Additive exPlanations, we identified distinct predictors for each process.</description><author>iScience</author><pubDate>Tue, 16 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02728-2?rss=yes</guid></item><item><title>[iScience] Integrative Analysis of Transcriptomic Data Reveals a Predictive Gene Signature for Chemoradiotherapy Response in Rectal Cancer</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02716-6?rss=yes</link><description>Locally advanced rectal cancer (LARC) is treated with neoadjuvant chemoradiotherapy (nCRT), but only a minority of patients achieve a pathological complete response (pCR). Predictive biomarkers of response could help guide treatment decisions, yet none have reached clinical practice. In this exploratory study, we integrated six publicly available transcriptomic datasets and applied machine learning to derive a 186-gene signature predictive of nCRT response. The signature showed good performance in cross-validation (AUC 0.80) and was associated with consensus molecular (CMS4) and immune (iCMS3) subtypes enriched in responders.</description><author>iScience</author><pubDate>Tue, 16 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02716-6?rss=yes</guid></item><item><title>[iScience] Combining DNA Methylation Features and Clinical Characteristics Predicts Ketamine Treatment Response for PTSD</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02706-3?rss=yes</link><description>Post-traumatic stress disorder (PTSD) exhibits extensive clinical and biological variability, making treatment challenging. The Consortium to Alleviate PTSD (CAP)-ketamine trial, the largest randomized study of ketamine for PTSD, found no overall benefit of ketamine over placebo, underscoring the necessity to identify responsive subgroups. Using pre-treatment blood DNA methylation profiles and clinical measures from the CAP-ketamine trial, we applied machine learning to predict treatment response.</description><author>iScience</author><pubDate>Tue, 16 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02706-3?rss=yes</guid></item><item><title>[Chem] In situ cryogenic X-ray photoelectron spectroscopy unveils metastable components of the solid electrolyte interphase in Li-ion batteries</title><link>https://www.cell.com/chem/fulltext/S2451-9294(25)00428-0?rss=yes</link><description>Nguyen et al. integrate cryogenic X-ray photoelectron spectroscopy (XPS) with residual gas analysis to study the solid electrolyte interphase (SEI) on graphite anodes in Li-ion batteries. The cryo-state preserves metastable SEI components, such as LiPOxFy, which decompose into stable products, such as LiF, with gas release upon warming. Discussions highlight critical XPS conditions, including ultrahigh vacuum exposure, X-ray-induced damage, and neutralizer settings, which could alter the detection and characterization of SEI components.</description><author>Chem</author><pubDate>Tue, 16 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/chem/fulltext/S2451-9294(25)00428-0?rss=yes</guid></item><item><title>[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Anharmonic Coupling in a Strong Intramolecular H-Bond System: Contributions to Static and Time-Resolved Vibrational Spectra</title><link>http://dx.doi.org/10.1021/acs.jpclett.5c03487</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpclett.5c03487/asset/images/medium/jz5c03487_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.5c03487&lt;/div&gt;</description><author>The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)</author><pubDate>Mon, 15 Dec 2025 17:03:38 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpclett.5c03487</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Rhodium-Catalyzed Atroposelective Alkyne Oxyamidation Using Non-Nitrene NH Acyloxyamide Reagents</title><link>http://dx.doi.org/10.1021/jacs.5c18495</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c18495/asset/images/medium/ja5c18495_0010.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.5c18495&lt;/div&gt;</description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Mon, 15 Dec 2025 15:14:00 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c18495</guid></item><item><title>[Proceedings of the National Academy of Sciences: Physical Sciences] CHARGE-MAP: An integrated framework to study the multicriteria EV charging infrastructure expansion problem</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2514184122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 51, December 2025. &lt;br /&gt;SignificanceThe surge in electric vehicle (EV) adoption has presented us the challenge of developing accessible and cost-effective charging infrastructures. To address this challenge, we present a frameworkcharge-map. It consists of: i) an agent-based ...</description><author>Proceedings of the National Academy of Sciences: Physical Sciences</author><pubDate>Mon, 15 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2514184122?af=R</guid></item><item><title>[Wiley: Chinese Journal of Chemistry: Table of Contents] 2‐Trifluoromethyl‐Benzimidazolium Salt as a Dual‐Function Reagent for Deoxytrifluoromethylation of Benzyl Alcohols</title><link>https://onlinelibrary.wiley.com/doi/10.1002/cjoc.70286?af=R</link><description>Chinese Journal of Chemistry, Volume 44, Issue 2, Page 177-182, 15 January 2026.</description><author>Wiley: Chinese Journal of Chemistry: Table of Contents</author><pubDate>Mon, 15 Dec 2025 07:33:14 GMT</pubDate><guid isPermaLink="true">10.1002/cjoc.70286</guid></item><item><title>[Applied Physics Letters Current Issue] Covalent bond chemistry enabling M 2 CN 2 MXenes as anode materials for halide-ion batteries</title><link>https://pubs.aip.org/aip/apl/article/127/24/243902/3374792/Covalent-bond-chemistry-enabling-M2CN2-MXenes-as</link><description>&lt;span class="paragraphSection"&gt;The development of halide-ion batteries is limited by the lack of efficient electrode materials. Two-dimensional M&lt;sub&gt;2&lt;/sub&gt;CN&lt;sub&gt;2&lt;/sub&gt; MXenes are promising anode candidates due to their structural flexibility and low molar mass, yet their stability and storage mechanism remain unclear. Using first-principles calculations, we identify Ti&lt;sub&gt;2&lt;/sub&gt;CN&lt;sub&gt;2&lt;/sub&gt;, Nb&lt;sub&gt;2&lt;/sub&gt;CN&lt;sub&gt;2&lt;/sub&gt;, and Ta&lt;sub&gt;2&lt;/sub&gt;CN&lt;sub&gt;2&lt;/sub&gt; as stable MXenes. Ti&lt;sub&gt;2&lt;/sub&gt;CN&lt;sub&gt;2&lt;/sub&gt; exhibits excellent performance with low voltages (0.07 V for F&lt;sup&gt;−&lt;/sup&gt;) and high specific capacities (394.8 mAh/g for F&lt;sup&gt;−&lt;/sup&gt;). The storage mechanism involves covalent bonding between surface N and halide-ions, where adsorption strength is governed by the energy difference between occupied &lt;span style="font-style: italic;"&gt;σ&lt;/span&gt;&lt;sup&gt;*&lt;/sup&gt; and unoccupied &lt;span style="font-style: italic;"&gt;π&lt;/span&gt;&lt;sup&gt;*&lt;/sup&gt; orbitals and their electron overlap. Moreover, O or Zr doping significantly enhances halide-ion diffusion kinetics. This work elucidates the covalent bond-mediated storage in M&lt;sub&gt;2&lt;/sub&gt;CN&lt;sub&gt;2&lt;/sub&gt; MXenes and guides the design of high-performance halide-ion battery electrodes.&lt;/span&gt;</description><author>Applied Physics Letters Current Issue</author><pubDate>Mon, 15 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apl/article/127/24/243902/3374792/Covalent-bond-chemistry-enabling-M2CN2-MXenes-as</guid></item><item><title>[Applied Physics Letters Current Issue] Measurement of multiple mechanical properties from multi-dimensional signals in nanosecond laser ablation via PINN</title><link>https://pubs.aip.org/aip/apl/article/127/24/244101/3374784/Measurement-of-multiple-mechanical-properties-from</link><description>&lt;span class="paragraphSection"&gt;Accurate evaluation of mechanical properties in steels under ageing or service conditions remains a major challenge. We propose a thermo-mechanical coupling framework for nanosecond laser ablation based on energy conservation, which is embedded into a physics-informed neural network (PINN) to enable simultaneous inversion of multiple mechanical properties. A thermo-mechanical coupling coefficient is defined to uniformly describe the dynamic allocation of input laser energy among thermal diffusion, mechanical work, and plasma shielding across different deformation stages under laser irradiation. Furthermore, hard-to-measure physical characteristics in the coupled equation are replaced with experimentally accessible features obtained through the simultaneous acquisition of spectroscopic, shockwave, and surface-wave signals. Using 210 experimental datasets, the framework simultaneously recovers Young's modulus, yield strength, ultimate tensile strength, and micro-Vickers hardness with high accuracy (R&lt;sup&gt;2&lt;/sup&gt; = 0.9927, 0.9912, 0.9916, and 0.9959, respectively), significantly outperforming the baseline method (ultrasonic velocity regression for &lt;span style="font-style: italic;"&gt;E&lt;/span&gt;, R&lt;sup&gt;2&lt;/sup&gt; = 0.0012). Comparisons with linear normalization and unconstrained neural networks demonstrate that PINN achieves near-unity accuracy through the embedding of conservation-law constraints. Partial dependency analysis further uncovers the nonlinear coupling laws between input features and mechanical properties. The proposed paradigm, integrating conservation laws, measurable features, and physics-informed learning, offers a universal approach for non-contact, high-precision, and physically consistent multi-to-multi inversion of multiple material properties under nanosecond laser ablation conditions.&lt;/span&gt;</description><author>Applied Physics Letters Current Issue</author><pubDate>Mon, 15 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apl/article/127/24/244101/3374784/Measurement-of-multiple-mechanical-properties-from</guid></item><item><title>[RSC - Digital Discovery latest articles] Deep learning based SEM image analysis for predicting ionic conductivity in LiZr2(PO4)3-based solid electrolytes</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00232J</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00232J" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2026, Advance Article&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00232J, Paper&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/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.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Kento Murakami, Yudai Yamaguchi, Yo Kato, Kazuki Ishikawa, Naoto Tanibata, Hayami Takeda, Masanobu Nakayama, Masayuki Karasuyama&lt;br /&gt;Lithium-ion-conductive oxide materials have attracted considerable attention as solid electrolytes for all-solid-state batteries.&lt;br /&gt;To cite this article before page numbers are assigned, use the DOI form of citation above.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Mon, 15 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00232J</guid></item><item><title>[RSC - Digital Discovery latest articles] Hierarchical Attention Graph Learning with LLMs Enhancement for Molecular Solubility Prediction</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00407A</link><description>&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2025, Accepted Manuscript&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00407A, Paper&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;Yangxin Fan, Yinghui Wu, Roger French, Danny Perez, Michael Taylor, Ping Yang&lt;br /&gt;Solubility quantifies the concentration of a molecule that can dissolve in a given solvent. Accurate prediction of solubility is essential for optimizing drug efficacy, improving chemical and separation processes, waste...&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Mon, 15 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00407A</guid></item><item><title>[iScience] Interpretable Machine Learning for Accessible Dysphagia Screening and Staging in Older Adults</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02712-9?rss=yes</link><description>Dysphagia in older adults causes serious complications, and efficient and scalable screening are needed. This prospective multicenter study developed interpretable machine learning (ML) models for the early identification and staging of dysphagia. Nine ML models were built using the clinical data from 1,235 patients and externally validated on 720 patients. All patients were older adults from seven Suzhou hospitals whose dysphagia was confirmed via Videofluoroscopic Swallowing Studies. Features were selected via random forest, and model interpretability was analyzed with SHapley Additive exPlanations (SHAP).</description><author>iScience</author><pubDate>Mon, 15 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02712-9?rss=yes</guid></item><item><title>[Joule] Dendrite suppression in garnet electrolytes via thermally induced compressive stress</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00413-1?rss=yes</link><description>Thermal gradients induce compressive stress in garnet solid electrolytes, mechanically toughening them against lithium penetration. The resulting 3-fold increase in critical current density demonstrates that stress engineering can increase critical current densities in solid-state batteries and isolates the role of mechanical stress as a dominant factor in dendrite suppression.</description><author>Joule</author><pubDate>Mon, 15 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00413-1?rss=yes</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Unraveling the Humidity-Induced Phase Transition in CALF-20 via Machine Learning Potentials</title><link>http://dx.doi.org/10.1021/jacs.5c18944</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c18944/asset/images/medium/ja5c18944_0010.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.5c18944&lt;/div&gt;</description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Sat, 13 Dec 2025 14:29:51 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c18944</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Machine Learning Descriptors for Mapping Structure‐Property‐Performance Relationships of Perovskite Solar Cells</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202505294?af=R</link><description>Advanced Energy Materials, EarlyView.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Sat, 13 Dec 2025 07:01:43 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202505294</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Vinylsilanes as Chain-Transfer Agents in Ethylene Polymerization: Direct Synthesis of Heterotelechelic Polyolefins</title><link>http://dx.doi.org/10.1021/jacs.5c15808</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c15808/asset/images/medium/ja5c15808_0009.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.5c15808&lt;/div&gt;</description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Fri, 12 Dec 2025 16:44:30 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c15808</guid></item><item><title>[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Compression-Induced Lattice Tilting Quenches Ion Migration at Metal Halide Perovskite Grain Boundaries: A Machine Learning Molecular Dynamics Study</title><link>http://dx.doi.org/10.1021/acs.jpclett.5c03637</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpclett.5c03637/asset/images/medium/jz5c03637_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.5c03637&lt;/div&gt;</description><author>The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)</author><pubDate>Fri, 12 Dec 2025 15:21:13 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpclett.5c03637</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Machine Learning-Assisted Crystal Structure Prediction of Solid-State Electrolytes Reveals Superior Ionic Conductivity in Metastable Edge-Sharing Phases</title><link>http://dx.doi.org/10.1021/jacs.5c15665</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c15665/asset/images/medium/ja5c15665_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.5c15665&lt;/div&gt;</description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Fri, 12 Dec 2025 14:36:09 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c15665</guid></item><item><title>[ACS Materials Letters: Latest Articles (ACS Publications)] [ASAP] Reaction Pathway Informed Strategy for Fast Solid-State Synthesis of Garnet-Type Solid Electrolyte</title><link>http://dx.doi.org/10.1021/acsmaterialslett.5c01262</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsmaterialslett.5c01262/asset/images/medium/tz5c01262_0005.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;ACS Materials Letters&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acsmaterialslett.5c01262&lt;/div&gt;</description><author>ACS Materials Letters: Latest Articles (ACS Publications)</author><pubDate>Fri, 12 Dec 2025 14:10:55 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsmaterialslett.5c01262</guid></item><item><title>[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Structural Aspects, Ionic Conductivity, and Electrochemical Properties of New Bromine-Substituted Alkali-Based Crystalline Phases MTa(Nb)X6–yBry (M = Li, Na, K; X = Cl, F)</title><link>http://dx.doi.org/10.1021/acsenergylett.5c02904</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsenergylett.5c02904/asset/images/medium/nz5c02904_0007.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.5c02904&lt;/div&gt;</description><author>ACS Energy Letters: Latest Articles (ACS Publications)</author><pubDate>Fri, 12 Dec 2025 13:47:45 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsenergylett.5c02904</guid></item><item><title>[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Spiking world model with multicompartment neurons for model-based reinforcement learning</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2513319122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 50, December 2025. &lt;br /&gt;SignificanceDendritic computation is key to the brain’s ability to integrate information over long timescales. Inspired by this, this study proposes a spiking neural network model that embeds dendritic mechanisms to enhance long-term memory and planning. ...</description><author>Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents</author><pubDate>Fri, 12 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2513319122?af=R</guid></item><item><title>[APL Machine Learning Current Issue] Smart detection of plant nutrient deficiencies using machine learning and image fusion</title><link>https://pubs.aip.org/aip/aml/article/3/4/046111/3374702/Smart-detection-of-plant-nutrient-deficiencies</link><description>&lt;span class="paragraphSection"&gt;Plant nutrient deficiencies are a continual challenge for enhancing global crop output and food security. Historically, manual inspections have been plagued by subjectivity, inefficiency, and restricted scalability, necessitating the development of enhanced detection algorithms. This research introduces a novel approach utilizing image processing and machine learning to enhance detection accuracy and practical applicability. This strategy promotes classification stability by integrating machine learning classifiers, including k-Nearest Neighbors, Artificial Neural Networks, Decision Trees, and Linear Discriminant Analysis, with fusion techniques such as Majority Voting and Mean Fusion. The experiments utilize Leave-One-Out Cross-Validation for model evaluation to address dataset variability and deliver thorough assessments. The study’s results indicate that the suggested system surpasses existing systems in accuracy, precision, recall, and F1 score, attaining an overall accuracy of 98.17%. The method is effective across various noise and resolution parameters, allowing for scalability in precision agriculture applications. This discovery not only enhances the diagnosis of plant nutrient deficiencies but also enables further investigations into real-time plant health monitoring.&lt;/span&gt;</description><author>APL Machine Learning Current Issue</author><pubDate>Fri, 12 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/046111/3374702/Smart-detection-of-plant-nutrient-deficiencies</guid></item><item><title>[Nature Machine Intelligence] LLM use in scholarly writing poses a provenance problem</title><link>https://www.nature.com/articles/s42256-025-01159-8</link><description>&lt;p&gt;Nature Machine Intelligence, Published online: 12 December 2025; &lt;a href="https://www.nature.com/articles/s42256-025-01159-8"&gt;doi:10.1038/s42256-025-01159-8&lt;/a&gt;&lt;/p&gt;LLM use in scholarly writing poses a provenance problem</description><author>Nature Machine Intelligence</author><pubDate>Fri, 12 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s42256-025-01159-8</guid></item><item><title>[RSC - Digital Discovery latest articles] PEMD: a high-throughput simulation and analysis framework for solid polymer electrolytes</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00454C</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00454C" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2026, Advance Article&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00454C, Paper&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;Shendong Tan, Bochun Liang, Dexin Lu, Chaoyuan Ji, Wenke Ji, Zihui Li, Tingzheng Hou&lt;br /&gt;PEMD is an open-source Python framework that integrates polymer construction, force-field parameterization, multiscale simulation, and property analysis, with standardized workflows for screening and data-driven design of solid polymer electrolytes.&lt;br /&gt;To cite this article before page numbers are assigned, use the DOI form of citation above.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Fri, 12 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00454C</guid></item><item><title>[AI for Science - latest papers] Investigating CO adsorption on Cu(111) and Rh(111) surfaces using machine learning exchange-correlation functionals</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae21fa</link><description>The ‘CO adsorption puzzle’, a persistent failure of utilizing generalized gradient approximations in density functional theory to replicate CO’s experimental preference for top-site adsorption on transition-metal surfaces, remains a critical barrier in surface chemistry. While hybrid functionals such as HSE06 partially resolve this discrepancy, their prohibitive computational cost limits broader applications. We tackle this issue by adopting the Deep Kohn–Sham (DeePKS) method to train machine-learned exchange-correlation functionals. Principal component analysis reveals that the input descriptors for electronic structures separate distinctly across different chemical environments, enabling the DeePKS models to generalize to multi-element systems. We train system-specific DeePKS models for transition-metal surfaces Cu(111) and Rh(111). These models successfully recover experimental site preferences, yielding adsorption energy differences of about 10 meV compared to HSE06, and can be applied to study unseen adsorbate coverages. Furthermore, a single model for the two surfaces is trained, and the model achieves comparable accuracy in predicting not only adsorption energies and site preference but also potential energy surfaces and relaxed surface adsorption structures. The work demonstrates a promising path towards universal models, enabling catalyst exploration with hybrid functional accuracy at substantially reduced cost.</description><author>AI for Science - latest papers</author><pubDate>Fri, 12 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae21fa</guid></item><item><title>[iScience] Contextualized biomedical language processing enhances ICU survival prediction</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02703-8?rss=yes</link><description>Accurate prediction of intensive care unit (ICU) survival remains challenging due to heterogeneous clinical data. This study shows that contextualized biomedical language processing markedly enhances ICU survival prediction. Multimodal models integrating structured laboratory data with unstructured text (chief complaints and ICD entries) were trained and validated using MIMIC-IV, MIMIC-III, and eICU datasets. The BioBERT-enhanced convolutional neural network achieved AUROCs of 0.889 (Strict Cohort, n=5,795) and 0.974 (Lenient Cohort, n=58,615) during external validation.</description><author>iScience</author><pubDate>Fri, 12 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02703-8?rss=yes</guid></item><item><title>[iScience] Consensus machine learning identifies cell death gene signature for carotid artery stenosis diagnosis</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02658-6?rss=yes</link><description>Carotid artery stenosis (CAS) is a major contributor to ischemic stroke, and molecular tools for its early detection remain limited. To address this need, we integrated one in-house RNA-seq cohort with eight public datasets comprising 696 samples, together with proteomic profiling, qRT-PCR, single-cell sequencing, and FYCO1 silencing experiments. From 1,258 curated cell death–related genes, candidates were filtered by logistic regression across cohorts, and ten machine learning algorithms were combined into 105 model configurations to derive a consensus diagnostic classifier.</description><author>iScience</author><pubDate>Fri, 12 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02658-6?rss=yes</guid></item><item><title>[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Dual Structure-Directing Agents for Superstructure Formation in PtCoCu Ternary Alloy Electrocatalysts</title><link>http://dx.doi.org/10.1021/acs.jpcc.5c06519</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpcc.5c06519/asset/images/medium/jp5c06519_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.5c06519&lt;/div&gt;</description><author>The Journal of Physical Chemistry C: Latest Articles (ACS Publications)</author><pubDate>Thu, 11 Dec 2025 15:46:59 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpcc.5c06519</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] A Cost‐Effective and Scalable Machine Learning Approach for Quality Assessment of Fresh Maize Kernel Using NIR Spectroscopy</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202512750?af=R</link><description>Advanced Science, Volume 12, Issue 46, December 11, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 11 Dec 2025 09:23:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202512750</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] High‐Performance Zinc–Bromine Rechargeable Batteries Enabled by In‐Situ Formed Solid Electrolyte Interphase</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202508646?af=R</link><description>Advanced Science, Volume 12, Issue 46, December 11, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 11 Dec 2025 09:23:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202508646</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Nonalcoholic Fatty Liver Disease Exacerbates the Advancement of Renal Fibrosis by Modulating Renal CCR2+PIRB+ Macrophages Through the ANGPTL8/PIRB/ALOX5AP Axis</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202509351?af=R</link><description>Advanced Science, Volume 12, Issue 46, December 11, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 11 Dec 2025 09:23:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202509351</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Inverse Design of Metal‐Organic Frameworks for CH4/N2 Separation Enabled by Coupled Machine Learning and Genetic Algorithms</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202513146?af=R</link><description>Advanced Science, Volume 12, Issue 46, December 11, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 11 Dec 2025 09:23:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202513146</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Synergistic Effect of Dual‐Functional Groups in MOF‐Modified Separators for Efficient Lithium‐Ion Transport and Polysulfide Management of Lithium‐Sulfur Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202515034?af=R</link><description>Advanced Science, Volume 12, Issue 46, December 11, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 11 Dec 2025 09:23:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202515034</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] H2S Is a Potential Universal Reducing Agent for Prx6‐Type Peroxiredoxins</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202507214?af=R</link><description>Advanced Science, Volume 12, Issue 46, December 11, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 11 Dec 2025 09:23:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202507214</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Luminescent Nanocucurbits Enable Spatiotemporal Co‐Delivery of Hydrophilic and Hydrophobic Chemotherapeutic Agents</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202509782?af=R</link><description>Advanced Science, Volume 12, Issue 46, December 11, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 11 Dec 2025 09:23:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202509782</guid></item><item><title>[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Evaluating large language models in biomedical data science challenges through a classroom experiment</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2521062122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 50, December 2025. &lt;br /&gt;SignificanceLarge language models (LLMs) are increasingly used in science and engineering, yet their real-world effectiveness in data analysis remains unclear. In this study, graduate students used LLMs to tackle biomedical data challenges on Kaggle, a ...</description><author>Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents</author><pubDate>Thu, 11 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2521062122?af=R</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Red Phosphorus@SnSe0.5S0.5 Core‐Shell Heterostructure Enables Superior Sodium Storage via Synergistic Ion Diffusion and Polyphosphides Trapping</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202510369?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 50, December 9, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Thu, 11 Dec 2025 06:58:45 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202510369</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Dual‐Site Ni Nanoparticles‐Ru Clusters Anchored on Hierarchical Carbon with Decoupled Gas and Ion Diffusion Channels Enabling Low‐Overpotential, Highly Stable Li‐CO2 Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202514453?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 50, December 9, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Thu, 11 Dec 2025 06:58:45 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202514453</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Surface Fluidic Microneedle Patches for Lymphatic Delivery of Diagnostic and Therapeutic Agents (Adv. Funct. Mater. 50/2025)</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.72865?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 50, December 9, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Thu, 11 Dec 2025 06:58:45 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.72865</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Bonyzymes: Efficient Anti‐Inflammatory, Antibacterial and Osteogenic Agents for Peri‐Implantitis Reconstruction Treatment</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202503585?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 50, December 9, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Thu, 11 Dec 2025 06:58:45 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202503585</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Surface Fluidic Microneedle Patches for Lymphatic Delivery of Diagnostic and Therapeutic Agents</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202513324?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 50, December 9, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Thu, 11 Dec 2025 06:58:45 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202513324</guid></item><item><title>[RSC - Digital Discovery latest articles] Toward smart CO2 capture by the synthesis of metal organic frameworks using large language models</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00446B</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00446B" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2026, Advance Article&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00446B, Paper&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/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.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Hossein Mashhadimoslem, Mohammad Ali Abdol, Kourosh Zanganeh, Ahmed Shafeen, Encheng Liu, Sohrab Zendehboudi, Ali Elkamel, Aiping Yu&lt;br /&gt;This research focuses on collecting experimental CO&lt;small&gt;&lt;sub&gt;2&lt;/sub&gt;&lt;/small&gt; adsorption data from 433 scientific papers to address the challenges of MOF synthesis methods and the correlation of MOF structure and the effect of their structure on CO&lt;small&gt;&lt;sub&gt;2&lt;/sub&gt;&lt;/small&gt; adsorption using LLMs.&lt;br /&gt;To cite this article before page numbers are assigned, use the DOI form of citation above.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Thu, 11 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00446B</guid></item><item><title>[Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)] [ASAP] A Universal Augmentation Framework for Long-Range Electrostatics in Machine Learning Interatomic Potentials</title><link>http://dx.doi.org/10.1021/acs.jctc.5c01400</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jctc.5c01400/asset/images/medium/ct5c01400_0015.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.5c01400&lt;/div&gt;</description><author>Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)</author><pubDate>Wed, 10 Dec 2025 10:12:04 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jctc.5c01400</guid></item><item><title>[Wiley: ENERGY &amp; ENVIRONMENTAL MATERIALS: Table of Contents] Tailoring Thermophysical Properties and Multiscale Machine Learning Modeling of 2D Nanomaterial‐Infused Beeswax as a Green NePCM for Sustainable Thermal Management Systems</title><link>https://onlinelibrary.wiley.com/doi/10.1002/eem2.70194?af=R</link><description>ENERGY &amp;amp;ENVIRONMENTAL MATERIALS, EarlyView.</description><author>Wiley: ENERGY &amp; ENVIRONMENTAL MATERIALS: Table of Contents</author><pubDate>Wed, 10 Dec 2025 09:54:56 GMT</pubDate><guid isPermaLink="true">10.1002/eem2.70194</guid></item><item><title>[Applied Physics Reviews Current Issue] Construction of polar topological nanodevices for neuromorphic computing</title><link>https://pubs.aip.org/aip/apr/article/12/4/041420/3374577/Construction-of-polar-topological-nanodevices-for</link><description>&lt;span class="paragraphSection"&gt;The research field of polar topological domains has witnessed rapid expansion in recent years, inspired by the vast application potentials for future topological electronic devices. Nonetheless, such topological devices remain elusive. In this study, we implemented the polar topological domain structures as neuromorphic computing elements, and present 12-state non-volatile ferroelectric topological nanodevices that demonstrate exceptional neuromorphic computing capabilities through the controlled formation and erasure of walls. These nanodevices exhibit near-linear long-term potentiation and long-term depression characteristics under repetitive voltage pulses, achieving a remarkable dynamic range. Simulations using a convolutional neural network model with these devices attain 95% recognition accuracy on the Modified National Institute of Standards and Technology handwritten digits dataset within 100 epochs. These results expand the functional scope of polar topological electronic devices to future neuromorphic computing systems.&lt;/span&gt;</description><author>Applied Physics Reviews Current Issue</author><pubDate>Wed, 10 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/12/4/041420/3374577/Construction-of-polar-topological-nanodevices-for</guid></item><item><title>[RSC - Digital Discovery latest articles] Optimizing Data Extraction from Materials Science Literature: A Study of Tools Using Large Language Models</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00482A</link><description>&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2025, Accepted Manuscript&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00482A, Paper&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;Wenkai Ning, Jeffrey Robert Reimers, Musen Li, Rika Kobayashi&lt;br /&gt;Large Language Models (LLMs) are increasingly utilized for large-scale extraction and organization of unstructured data owing to their exceptional Natural Language Processing (NLP) capabilities. Empowering materials design, vast amounts of...&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Wed, 10 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00482A</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Machine Learning-Guided Decoding Bioelectronic Signals of Photosynthetic Cyanobacterial Cells by Conducting Polymers</title><link>http://dx.doi.org/10.1021/jacs.5c13150</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c13150/asset/images/medium/ja5c13150_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.5c13150&lt;/div&gt;</description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Tue, 09 Dec 2025 15:33:56 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c13150</guid></item><item><title>[Recent Articles in Phys. Rev. Lett.] Connection between Memory Performance and Optical Absorption in Quantum Reservoir Computing</title><link>http://link.aps.org/doi/10.1103/vp79-8t1l</link><description>Author(s): Niclas Götting, Steffen Wilksen, Alexander Steinhoff, Frederik Lohof, and Christopher Gies&lt;br /&gt;&lt;p&gt;Quantum reservoir computing (QRC) offers a promising paradigm for harnessing quantum systems for machine learning tasks, especially in the era of noisy intermediate-scale quantum devices. While information-theoretical benchmarks like short-term memory capacity (STMC) are widely used to evaluate QRC …&lt;/p&gt;&lt;br /&gt;[Phys. Rev. Lett. 135, 240403] Published Tue Dec 09, 2025</description><author>Recent Articles in Phys. Rev. Lett.</author><pubDate>Tue, 09 Dec 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/vp79-8t1l</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Ultrahigh‐Rate Lithium Storage in MoS2 Enabled by Isotropic Ion Transport and Fe‐Atomic Site Conversion</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202505600?af=R</link><description>Advanced Energy Materials, EarlyView.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 09 Dec 2025 08:32:12 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202505600</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202505470?af=R</link><description>Advanced Energy Materials, EarlyView.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 09 Dec 2025 07:26:17 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202505470</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Enhancing Synaptic Plasticity in Strontium Titanate‐Based Sensory Processing Devices: A Study on Oxygen Vacancy Modulation and Performance in Artificial Neural Networks</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500028?af=R</link><description>Advanced Intelligent Discovery, Volume 1, Issue 3, December 2025.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Tue, 09 Dec 2025 01:16:19 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500028</guid></item><item><title>[RSC - Digital Discovery latest articles] Multi-agentic AI framework for end-to-end atomistic simulations</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00435G</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00435G" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2026, Advance Article&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00435G, Paper&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/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.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Aikaterini Vriza, Uma Kornu, Aditya Koneru, Henry Chan, Subramanian K. R. S. Sankaranarayanan&lt;br /&gt;Autonomous multi-agent AI system coordinates specialized agents to perform complex materials property calculations from natural language prompts.&lt;br /&gt;To cite this article before page numbers are assigned, use the DOI form of citation above.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Tue, 09 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00435G</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Quantifying Phase Contributions to Ion Transport in Organic–Inorganic Composite Electrolytes</title><link>http://dx.doi.org/10.1021/jacs.5c11634</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c11634/asset/images/medium/ja5c11634_0007.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.5c11634&lt;/div&gt;</description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Mon, 08 Dec 2025 19:45:10 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c11634</guid></item><item><title>[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Chatbot Voting Advice Applications inform but seldom sway young unaligned voters</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2515516122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 50, December 2025. &lt;br /&gt;SignificanceMany people struggle to identify where political parties stand on the issues that matter most to them. This study introduces a Voting Advice Application (VAA) Bot powered by generative AI, that provides information about party positions using ...</description><author>Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents</author><pubDate>Mon, 08 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2515516122?af=R</guid></item><item><title>[Proceedings of the National Academy of Sciences: Physical Sciences] Local equations describe unreasonably efficient stochastic algorithms in random K-SAT</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2510153122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 49, December 2025. &lt;br /&gt;SignificanceThe difficulties of algorithmic dynamics in highly nonconvex landscapes are central in several research areas, from hard combinatorial optimization to machine learning. However, it is unclear why and how some particular algorithms find ...</description><author>Proceedings of the National Academy of Sciences: Physical Sciences</author><pubDate>Fri, 05 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2510153122?af=R</guid></item><item><title>[ACS Materials Letters: Latest Articles (ACS Publications)] [ASAP] Beyond Conventional Sodium Superionic Conductor: Fe-Substituted Na3V2(PO4)2F3 Cathodes with Accelerated Charge Transport via Polyol Reflux for Sodium-Ion Batteries</title><link>http://dx.doi.org/10.1021/acsmaterialslett.5c01502</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsmaterialslett.5c01502/asset/images/medium/tz5c01502_0006.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;ACS Materials Letters&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acsmaterialslett.5c01502&lt;/div&gt;</description><author>ACS Materials Letters: Latest Articles (ACS Publications)</author><pubDate>Thu, 04 Dec 2025 13:33:58 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsmaterialslett.5c01502</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Non‐Monotonic Ion Conductivity in Lithium‐Aluminum‐Chloride Glass Solid‐State Electrolytes Explained by Cascading Hopping</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202509205?af=R</link><description>Advanced Science, Volume 12, Issue 45, December 4, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 04 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202509205</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Re‐Purposing a Modular Origami Manipulator Into an Adaptive Physical Computer for Machine Learning and Robotic Perception</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202509389?af=R</link><description>Advanced Science, Volume 12, Issue 45, December 4, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 04 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202509389</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Ultrastable Calcium Metal Anodes Enabled by a Strongly Coordinated Electrolyte Derived Bilayer Solid Electrolyte Interphase</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202510711?af=R</link><description>Advanced Materials, Volume 37, Issue 48, December 3, 2025.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Thu, 04 Dec 2025 07:04:36 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202510711</guid></item><item><title>[APL Machine Learning Current Issue] Multi-resolution physics-aware recurrent convolutional neural network for complex flows</title><link>https://pubs.aip.org/aip/aml/article/3/4/046110/3374061/Multi-resolution-physics-aware-recurrent</link><description>&lt;span class="paragraphSection"&gt;We present MRPARCv2, Multi-resolution Physics-Aware Recurrent Convolutional Neural Network, designed to model complex flows by embedding the structure of advection–diffusion–reaction equations and leveraging a multi-resolution architecture. MRPARCv2 introduces hierarchical discretization and cross-resolution feature communication to improve the accuracy and efficiency of flow simulations. We evaluate the model on a challenging 2D turbulent radiative layer dataset from The Well multi-physics benchmark repository and demonstrate significant improvements when compared to the single resolution baseline model, in both Variance Scaled Root Mean Squared Error and physics-driven metrics, including turbulent kinetic energy spectra and mass–temperature distributions. Despite having 30% fewer trainable parameters, MRPARCv2 outperforms its predecessor by up to 50% in roll-out prediction error and 86% in spectral error. A preliminary study on uncertainty quantification was performed, and we also analyzed the model’s performance under different levels of abstractions of the flow, specifically on sampling subsets of field variables. We find that the absence of physical constraints on the equation of state (EOS) in the network architecture leads to degraded accuracy. A variable substitution experiment confirms that this issue persists regardless of which physical quantity is predicted directly. Our findings highlight the advantages of multi-resolution inductive bias for capturing multi-scale flow dynamics and suggest the need for future PIML models to embed EOS knowledge to enhance physical fidelity.&lt;/span&gt;</description><author>APL Machine Learning Current Issue</author><pubDate>Thu, 04 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/046110/3374061/Multi-resolution-physics-aware-recurrent</guid></item><item><title>[RSC - Digital Discovery latest articles] Understanding and mitigating distribution shifts for universal machine learning interatomic potentials</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00260E</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00260E" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2026, Advance Article&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00260E, Paper&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/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.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Tobias Kreiman, Aditi S. Krishnapriyan&lt;br /&gt;We find common distribution shifts that pose challenges for universal machine learning interatomic potentials (MLIPs). We develop test-time refinement strategies that mitigate the shifts and provide insights into why MLIPs struggle to generalize.&lt;br /&gt;To cite this article before page numbers are assigned, use the DOI form of citation above.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Thu, 04 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00260E</guid></item><item><title>[iScience] Physical Cognition in Altered Gravity: Link Between Sensorimotor and Cognitive Adaptability</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02604-5?rss=yes</link><description>A hallmark of human intelligence is rapid adaptation to changing environments. Yet the link between sensorimotor recalibration to new physical conditions and cognitive updating of internal models remains unclear. We addressed this using altered gravity as a model system. In a within-subject study, 25 adults completed a virtual-reality task requiring motor adjustment to non-terrestrial gravities and an online problem-solving task requiring physical reasoning under matched gravity manipulations. Adaptability in each domain was computed relative to performance under terrestrial gravity.</description><author>iScience</author><pubDate>Thu, 04 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02604-5?rss=yes</guid></item><item><title>[iScience] Meteorological and Socioeconomic Impacts on China Ozone Past and Future Analysis</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02596-9?rss=yes</link><description>Chen et al. quantify seasonal ozone dynamics across China using machine learning and climate projections, revealing northern warm‑season O3 peaks driven by temperature, humidity, and mid‑tropospheric winds, and regionally divergent future trajectories under SSPs, highlighting the need for location‑specific, climate‑aware ozone control.</description><author>iScience</author><pubDate>Thu, 04 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02596-9?rss=yes</guid></item><item><title>[Applied Physics Reviews Current Issue] Gate-tunable dual-mode BiOI photodetector for precise object identification</title><link>https://pubs.aip.org/aip/apr/article/12/4/041418/3374123/Gate-tunable-dual-mode-BiOI-photodetector-for</link><description>&lt;span class="paragraphSection"&gt;The controllable growth of large-sized and high-quality semiconductor single crystals is an important guarantee for the realization of high-performance electronic and optoelectronic devices. Herein, we synthesized layered BiOI transparent single crystals through a tellurium-assisted chemical vapor transport strategy. Systematic investigation reveals that tellurium acts as a critical transport agent, directly modulating the crystallization dynamics and enabling the growth of high-quality 1-cm single crystals with precise size control. The layered BiOI crystals demonstrate excellent broadband (254–940 nm) photoresponse performance, achieving a remarkable responsivity of 123.7 A·W&lt;sup&gt;−1&lt;/sup&gt; and specific detectivity of 7.2 × 10&lt;sup&gt;13&lt;/sup&gt; Jones. Notably, the implementation of gate voltage regulation allows dynamic control of carrier transport mechanisms, achieving efficient regulation of the photoresponse of the device. This unique gate-tunable characteristic enables dual-mode operation in image recognition systems, simultaneously supporting both high-sensitivity detection and programmable contrast enhancement. The combination of scalable crystal growth and multifunctional optoelectronic properties positions BiOI as a promising candidate for next-generation intelligent photodetection technologies.&lt;/span&gt;</description><author>Applied Physics Reviews Current Issue</author><pubDate>Thu, 04 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/12/4/041418/3374123/Gate-tunable-dual-mode-BiOI-photodetector-for</guid></item><item><title>[Wiley: Small: Table of Contents] Label‐Free Detection of Nuclear Envelope Nucleoporation using 2D Morphological Embeddings and Machine Learning</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202504402?af=R</link><description>Small, Volume 21, Issue 48, December 3, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Wed, 03 Dec 2025 15:24:49 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202504402</guid></item><item><title>[Wiley: Small: Table of Contents] Reagentless Real‐Time ATP Monitoring with New DNA Aptamers</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202508898?af=R</link><description>Small, Volume 21, Issue 48, December 3, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Wed, 03 Dec 2025 15:24:49 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202508898</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Machine Learning‐Enabled Polymer Discovery for Enhanced Pulmonary siRNA Delivery</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202502805?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 49, December 2, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Wed, 03 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202502805</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Enhanced Potassium Ion Diffusion and Interface Stability Enabled by Potassiophilic rGO/CNTs/NaF Micro‐Lattice Aerogel for High‐Performance Potassium Metal Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202508586?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 49, December 2, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Wed, 03 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202508586</guid></item><item><title>[Nature Reviews Physics] Predicting high-entropy alloy phases with machine learning</title><link>https://www.nature.com/articles/s42254-025-00903-8</link><description>&lt;p&gt;Nature Reviews Physics, Published online: 03 December 2025; &lt;a href="https://www.nature.com/articles/s42254-025-00903-8"&gt;doi:10.1038/s42254-025-00903-8&lt;/a&gt;&lt;/p&gt;Omokhuwele Umoru explains how generative adversarial networks can help to predict the phases of high-entropy alloys.</description><author>Nature Reviews Physics</author><pubDate>Wed, 03 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s42254-025-00903-8</guid></item><item><title>[RSC - Digital Discovery latest articles] Evaluating the transfer learning from metals to oxides with GAME-Net-Ox</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00331H</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00331H" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2026, Advance Article&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00331H, Paper&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;Thomas Van Hout, Oliver Loveday, Jordi Morales-Vidal, Santiago Morandi, Núria López&lt;br /&gt;GAME-Net-Ox, an extension of the GAME-Net graph neural network, enables fast prediction of adsorption energies for molecules with key organic functional groups on conductive and semiconductive rutile metal oxides and metal surfaces.&lt;br /&gt;To cite this article before page numbers are assigned, use the DOI form of citation above.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Wed, 03 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00331H</guid></item><item><title>[iScience] United multi-omics and machine learning refine regulatory T cell-defined hepatocellular carcinoma subtypes</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02589-1?rss=yes</link><description>Hepatocellular carcinoma (HCC) is highly heterogeneous and aggressive, and the absence of precision individual treatment regimen enables repeated immune escape. Exploiting Treg-marker genes as key classifiers, we used 10 clustering algorithms to integrate the multi-omics HCC patients data and combined with 10 machine learning (ML) algorithms to delineate molecular subtypes to define molecular subtypes predictive of prognosis and immune response. We identified two cancer subtypes (CSs) that are associated with prognosis, with the second subtype (CS2) showing the most favourable prognostic outcomes.</description><author>iScience</author><pubDate>Wed, 03 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02589-1?rss=yes</guid></item><item><title>[iScience] ABCA1 acts as a protective modulator in amyotrophic lateral sclerosis</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02581-7?rss=yes</link><description>Amyotrophic lateral sclerosis (ALS) is a progressive motor neuron disease lacking reliable biomarkers and effective therapeutic targets. We performed an integrative multiscale analysis combining global epidemiology, whole-blood transcriptomics, machine learning, and Mendelian randomization (MR). We developed a nine-gene diagnostic signature (AUC = 0.75 in external validation) and identified ATP-binding cassette transporter A1 (ABCA1) as a central feature. MR analyses supported a protective causal relationship between increased ABCA1 expression and reduced ALS risk (OR = 0.93, p = 0.02).</description><author>iScience</author><pubDate>Wed, 03 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02581-7?rss=yes</guid></item><item><title>[Matter] Unknowium, beyond the banana, and AI discovery in materials science</title><link>https://www.cell.com/matter/fulltext/S2590-2385(25)00557-0?rss=yes</link><description>Recently, the proportion of papers implementing some sort of artificial intelligence (AI) or machine learning (ML) methods in materials science has been growing. It’s hard to ignore such a powerful and exciting tool. Relatedly, I have just returned from the Pujiang Innovation Forum held in Shanghai, China, where I participated in the “AI for Materials Science” session (Figure 1A), speaking as a lowly editor among global experts in the field.</description><author>Matter</author><pubDate>Wed, 03 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/matter/fulltext/S2590-2385(25)00557-0?rss=yes</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Taming Metal–Solid Electrolyte Interface Instability via Metal Strain Hardening</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202303500?af=R</link><description>Advanced Energy Materials, Volume 15, Issue 45, December 2, 2025.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 02 Dec 2025 13:55:59 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202303500</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Self‐Liquefying Conformal Nanocoatings via Phase‐Convertible Ion Conductors for Stable All‐Solid‐State Batteries (Adv. Energy Mater. 45/2025)</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.70345?af=R</link><description>Advanced Energy Materials, Volume 15, Issue 45, December 2, 2025.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 02 Dec 2025 13:55:59 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.70345</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Multiscale Design Strategies of Interface‐Stabilized Solid Electrolytes and Dynamic Interphase Decoding from Atomic‐to‐Macroscopic Perspectives</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202502938?af=R</link><description>Advanced Energy Materials, Volume 15, Issue 45, December 2, 2025.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 02 Dec 2025 13:55:59 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202502938</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Self‐Liquefying Conformal Nanocoatings via Phase‐Convertible Ion Conductors for Stable All‐Solid‐State Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202503562?af=R</link><description>Advanced Energy Materials, Volume 15, Issue 45, December 2, 2025.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 02 Dec 2025 13:55:59 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202503562</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] LoRA-Chem: Modular Machine Learning for Multitask Prediction in Organic Reactions</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202506542?af=R</link><description>&lt;p&gt;&lt;img src="https://www.chinesechemsoc.org/cms/asset/e0757683-26df-454a-a9ca-b322a8b7935e/keyimage.jpg" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;CCS Chemistry, Ahead of Print.&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI:10.31635/ccschem.025.202506542&lt;/div&gt;The prediction of organic chemical reactions has historically presented significant challenges owing to the inherent complexity and mechanistic diversity of reaction processes. In this work, we present LoRA-Chem, an innovative modular framework that ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Tue, 02 Dec 2025 04:48:31 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202506542?af=R</guid></item><item><title>[iScience] Dimensionality modulated generative AI for safe biomedical dataset augmentation</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02582-9?rss=yes</link><description>Generative artificial intelligence can expand small biomedical datasets but may amplify noise and distort statistical relationships. We developed genESOM, a framework integrating an error control system into a generative AI method based on emergent self-organizing maps. By separating structure learning from data synthesis, genESOM enables dimensionality modulation and injection of engineered diagnostic features, i.e., permuted versions of real variables, as negative controls that track feature importance stability.</description><author>iScience</author><pubDate>Tue, 02 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02582-9?rss=yes</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500147?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Mon, 01 Dec 2025 22:39:43 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500147</guid></item><item><title>[APL Machine Learning Current Issue] RTNinja : A generalized machine learning framework for analyzing random telegraph noise signals in nanoelectronic devices</title><link>https://pubs.aip.org/aip/aml/article/3/4/046109/3373556/RTNinja-A-generalized-machine-learning-framework</link><description>&lt;span class="paragraphSection"&gt;Random telegraph noise is a prevalent variability phenomenon in nanoelectronic devices, arising from stochastic carrier exchange at defect sites and critically impacting device reliability and performance. Conventional analysis techniques often rely on restrictive assumptions or manual interventions, limiting their applicability to complex, noisy datasets. Here, we introduce &lt;span style="font-style: italic;"&gt;RTNinja&lt;/span&gt;, a generalized, fully automated machine learning framework for the unsupervised analysis of random telegraph noise signals. &lt;span style="font-style: italic;"&gt;RTNinja&lt;/span&gt; deconvolves complex signals to identify the number and characteristics of hidden individual sources without requiring prior knowledge of the system. The framework comprises two modular components: &lt;span style="font-style: italic;"&gt;LevelsExtractor&lt;/span&gt;, which uses Bayesian inference and model selection to denoise and discretize the signal, and &lt;span style="font-style: italic;"&gt;SourcesMapper&lt;/span&gt;, which infers source configurations through probabilistic clustering and optimization. To evaluate performance, we developed a Monte Carlo simulator that generates labeled datasets spanning broad signal-to-noise ratios and source complexities; across 7000 such datasets, &lt;span style="font-style: italic;"&gt;RTNinja&lt;/span&gt; consistently demonstrated high-fidelity signal reconstruction and accurate extraction of source amplitudes and activity patterns. Our results demonstrate that &lt;span style="font-style: italic;"&gt;RTNinja&lt;/span&gt; offers a robust, scalable, and device-agnostic tool for random telegraph noise characterization, enabling large-scale statistical benchmarking, reliability-centric technology qualification, predictive failure modeling, and device physics exploration in next-generation nanoelectronics.&lt;/span&gt;</description><author>APL Machine Learning Current Issue</author><pubDate>Mon, 01 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/046109/3373556/RTNinja-A-generalized-machine-learning-framework</guid></item><item><title>[iScience] A pilot study: Incorporating Treponema pallidum antigens into machine learning models for accurate syphilis treatment outcome assessment</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02536-2?rss=yes</link><description>Health informatics; disease; artificial intelligence applications</description><author>iScience</author><pubDate>Mon, 01 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02536-2?rss=yes</guid></item><item><title>[iScience] Bathymetry of the Philippine Sea with Convolution Neural Network from Multisource Marine Geodetic Data</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02546-5?rss=yes</link><description>This study developed a deep learning-based method for high resolution bathymetry prediction in the Philippine Sea, aiming to improve the accuracy of seafloor depth estimation using multi-source marine geodetic data. The method integrates geographic coordinates with auxiliary features such as bathymetry, sea-land marks, seafloor slope and orientation, gravity anomaly, vertical gravity gradient, mean dynamic topography, deflection of the vertical, mean sea surface and sedimentary thickness. These inputs were extracted from an 8×8 arcminute region around each training point and the model was trained to predict depth residuals.</description><author>iScience</author><pubDate>Fri, 28 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02546-5?rss=yes</guid></item><item><title>[Wiley: Small Methods: Table of Contents] Water‐Dispersible Metal Oxide Nanoparticles Synthesized Via Hydrogen‐Bond‐Mediated Aqueous Solution: Gd2O3 for High‐Performance T1 Magnetic Resonance Imaging Contrast Agent</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smtd.202501947?af=R</link><description>Small Methods, EarlyView.</description><author>Wiley: Small Methods: Table of Contents</author><pubDate>Thu, 27 Nov 2025 07:52:59 GMT</pubDate><guid isPermaLink="true">10.1002/smtd.202501947</guid></item><item><title>[iScience] Interpretable Machine Learning for Urothelial Cells Classification and Risk Scoring in Urine Cytology</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02520-9?rss=yes</link><description>Urine cytology is widely used for detecting urothelial carcinoma (UC), though its performance is constrained by limited sensitivity and substantial inter-observer variability. An interpretable machine learning framework was developed to classify urothelial cells and to estimate slide-level risk of high-grade urothelial carcinoma. 10,230 expert-annotated urothelial cells were used to extract 20 quantitiative feature representing cytomorphologic criteric defined by the Paris System. Ordinal logistic regression and random forest models were trained and validated, achiving over 90% accuracy for classifying cells into Normal, Atypical, or Suspicious categories.</description><author>iScience</author><pubDate>Thu, 27 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02520-9?rss=yes</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Machine Learning‐Assisted Second‐Order Perturbation Theory for Chemical Potential Correction Toward Hubbard U Determination</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500160?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Wed, 26 Nov 2025 03:49:32 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500160</guid></item><item><title>[RSC - Digital Discovery latest articles] Toward accelerating rare-earth metal extraction using equivariant neural networks</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00286A</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00286A" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2026, Advance Article&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00286A, Paper&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/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.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Ankur K. Gupta, Caitlin V. Hetherington, Wibe A. de Jong&lt;br /&gt;A high-throughput workflow leveraging equivariant GNNs and a diverse dataset of rare-earth complexes to predict binding affinities and accelerate critical metal separation.&lt;br /&gt;To cite this article before page numbers are assigned, use the DOI form of citation above.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Wed, 26 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00286A</guid></item><item><title>[RSC - Digital Discovery latest articles] Automated synthesis and fragment descriptor-based machine learning for retention time prediction in supercritical fluid chromatography</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00437C</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00437C" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2026, Advance Article&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00437C, Paper&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/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.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Sitanan Sartyoungkul, Balasubramaniyan Sakthivel, Pavel Sidorov, Yuuya Nagata&lt;br /&gt;Integration of automated synthesis and fragment descriptor-based machine learning enables accurate prediction of SFC retention times and accelerates column characterization.&lt;br /&gt;To cite this article before page numbers are assigned, use the DOI form of citation above.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Wed, 26 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00437C</guid></item><item><title>[RSC - Digital Discovery latest articles] Mol2Raman: a graph neural network model for predicting Raman spectra from SMILES representations</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00210A</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00210A" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2025, Advance Article&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00210A, Paper&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;Salvatore Sorrentino, Alessandro Gussoni, Francesco Calcagno, Gioele Pasotti, Davide Avagliano, Ivan Rivalta, Marco Garavelli, Dario Polli&lt;br /&gt;Mol2Raman is a graph neural network that predicts Raman spectra from molecular SMILES. Trained on &amp;gt;31k DFT-calculated spectra, it localizes peaks within 15 cm&lt;small&gt;&lt;sup&gt;−1&lt;/sup&gt;&lt;/small&gt; with 64% accuracy, outperforming current SOTA deep learning algorithms on Raman spectra.&lt;br /&gt;To cite this article before page numbers are assigned, use the DOI form of citation above.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Tue, 25 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00210A</guid></item><item><title>[RSC - Chem. Sci. latest articles] Data-driven approach to elucidate the correlation between photocatalytic activity and rate constants from excited states</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC06465A</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5SC06465A" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Chem. Sci.&lt;/b&gt;&lt;/i&gt;, 2026, Advance Article&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5SC06465A, 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/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.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Ryuga Kunisada, Manami Hayashi, Tabea Rohlfs, Taiki Nagano, Koki Sano, Naoto Inai, Naoki Noto, Takuya Ogaki, Yasunori Matsui, Hiroshi Ikeda, Olga García Mancheño, Takeshi Yanai, Susumu Saito&lt;br /&gt;A data-driven framework integrating machine learning and quantum chemical calculations enables elucidation of how rate constants from excited states govern the photocatalytic activity of organic photosensitizers.&lt;br /&gt;To cite this article before page numbers are assigned, use the DOI form of citation above.&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, 25 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC06465A</guid></item><item><title>[Cell Reports Physical Science] Repurposing clinical iron oxide agents for mild hyperthermia-assisted cancer therapy</title><link>https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00576-4?rss=yes</link><description>Zha et al. have developed a photothermal- or ultrasound-assisted therapeutic platform that endows a clinically approved iron oxide reagent with profound anticancer efficacy. The platform can induce immunogenic cell death in multiple xenograft models, reverse immunosuppressive tumor microenvironments, and trigger robust anticancer immune responses with enhanced immunological memory.</description><author>Cell Reports Physical Science</author><pubDate>Tue, 25 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00576-4?rss=yes</guid></item><item><title>[JACS Au: Latest Articles (ACS Publications)] [ASAP] Constant-Potential MD with Neural Network Potentials Reveals Cation Effects on CO2 Reduction at Au-Water Interfaces</title><link>http://dx.doi.org/10.1021/jacsau.5c01198</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacsau.5c01198/asset/images/medium/au5c01198_0007.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;JACS Au&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/jacsau.5c01198&lt;/div&gt;</description><author>JACS Au: Latest Articles (ACS Publications)</author><pubDate>Mon, 24 Nov 2025 12:22:43 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacsau.5c01198</guid></item><item><title>[Proceedings of the National Academy of Sciences: Physical Sciences] Modeling feasible locomotion of nanobots for cancer detection and treatment</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2510036122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 48, December 2025. &lt;br /&gt;SignificanceWe present a mathematical model of nanorobots moving in a colloidal environment within the human body to locate a single, targeted cancer site and deliver localized treatment. The capabilities and behavior of individual agents are inspired by ...</description><author>Proceedings of the National Academy of Sciences: Physical Sciences</author><pubDate>Mon, 24 Nov 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2510036122?af=R</guid></item><item><title>[Wiley: Chinese Journal of Chemistry: Table of Contents] Locked Coplanar Conformation Boosts Rapid Electron/Ion Transport in Linear Polyimide Cathodes for Sodium‐Ion Storage</title><link>https://onlinelibrary.wiley.com/doi/10.1002/cjoc.70366?af=R</link><description>Chinese Journal of Chemistry, EarlyView.</description><author>Wiley: Chinese Journal of Chemistry: Table of Contents</author><pubDate>Mon, 24 Nov 2025 07:33:36 GMT</pubDate><guid isPermaLink="true">10.1002/cjoc.70366</guid></item><item><title>[APL Machine Learning Current Issue] A hybrid neural architecture: Online attosecond x-ray characterization</title><link>https://pubs.aip.org/aip/aml/article/3/4/046107/3373060/A-hybrid-neural-architecture-Online-attosecond-x</link><description>&lt;span class="paragraphSection"&gt;The emergence of high-repetition-rate x-ray free-electron lasers (XFELs), such as SLAC’s LCLS-II, serves as our canonical example for autonomous controls that necessitate high-throughput diagnostics paired with streaming computational pipelines capable of single-shot analysis with extremely low latency. We present the deterministic characterization with an integrated parallelizable hybrid resolver architecture, a hybrid machine learning framework designed for fast, accurate analysis of XFEL diagnostics using angular streaking-based sinogram images. This architecture integrates convolutional neural networks and bidirectional long short-term memory models to denoise input, identify x-ray sub-spike features, and extract sub-spike relative delays with sub-30 attosecond temporal resolution. Deployed on low-latency hardware, it achieves over 10 kHz throughput with 168.3 &lt;span style="font-style: italic;"&gt;μ&lt;/span&gt;s inference latency, indicating scalability to 14 kHz with field-programmable gate array integration. By transforming regression tasks into classification problems and leveraging optimized error encoding, we achieve high precision with low-latency performance that is critical for real-time streaming event selection and experimental control feedback signals. This represents a key development in real-time control pipelines for next-generation autonomous science, generally, and high repetition-rate x-ray experiments in particular.&lt;/span&gt;</description><author>APL Machine Learning Current Issue</author><pubDate>Fri, 21 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/046107/3373060/A-hybrid-neural-architecture-Online-attosecond-x</guid></item><item><title>[Joule] Accelerated discovery of CO2-to-C3-hydrocarbon electrocatalysts with human-in-the-loop</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00394-0?rss=yes</link><description>Despite advances in automation and AI, accelerating discovery in heterogeneous electrocatalysts remains hindered by the experimental challenges of building integrated platforms for synthesis and evaluation, as well as limited performance-relevant data. This work integrates accelerated experimentation, machine learning, and domain expertise to efficiently explore CO2-to-C3 electrocatalysts, adding new mechanistic and data-driven insights to energy science.</description><author>Joule</author><pubDate>Fri, 21 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00394-0?rss=yes</guid></item><item><title>[Joule] Redox-mediated solid-state doping of Spiro-OMeTAD for efficient and robust perovskite photovoltaics</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00398-8?rss=yes</link><description>The volatile and unstable nature of conventional dopants severely limits the performance and operational lifetime of perovskite solar cells. Here, we demonstrate a solid-state doping strategy, enabling a uniform nanoscale doping profile while effectively suppressing ion migration. This strategy yields perovskite solar cells with a certified efficiency of 26.34% and high device stability.</description><author>Joule</author><pubDate>Fri, 21 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00398-8?rss=yes</guid></item><item><title>[AI for Science - latest papers] Learning to be simple</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae1d98</link><description>In this work we employ machine learning to understand structured mathematical data involving finite groups and derive a theorem about necessary properties of generators of finite simple groups. We create a database of all two-generated subgroups of the symmetric group on n-objects and conduct a classification of finite simple groups among them using shallow feed-forward neural networks. We show that this neural network classifier can decipher the property of simplicity with varying accuracies depending on the features. Our neural network model leads to a natural conjecture concerning the generators of a finite simple group. We subsequently prove this conjecture. This new toy theorem comments on the necessary properties of generators of finite simple groups. We show this explicitly for a class of sporadic groups for which the result holds. Our work further makes the case for a machine motivated study of algebraic structures in pure mathematics and highlights the possibility of generating new conjectures and theorems in mathematics with the aid of machine learning.</description><author>AI for Science - latest papers</author><pubDate>Thu, 20 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae1d98</guid></item><item><title>[RSC - Chem. Sci. latest articles] Development of a glutamine-responsive MRI contrast agent</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC05987A</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5SC05987A" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Chem. Sci.&lt;/b&gt;&lt;/i&gt;, 2026, Advance Article&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5SC05987A, 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/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.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Charles A. Wilson, Austin T. Bruchs, Saman Fatima, David G. Boggs, Jennifer Bridwell-Rabb, Lisa Olshansky&lt;br /&gt;This report details the development of a conformationally switchable artificial metalloprotein (swArM) that provides differential MRI contrast signal in the presence and absence of the key biomarker glutamine.&lt;br /&gt;To cite this article before page numbers are assigned, use the DOI form of citation above.&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>Thu, 20 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC05987A</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Taguchi–Bayesian Sampling: A Roadmap for Polymer Database Construction Toward Small Representative Machine Learning</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500150?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Wed, 19 Nov 2025 05:00:22 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500150</guid></item><item><title>[RSC - Digital Discovery latest articles] Democratizing machine learning in chemistry with community-engaged test sets</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00424A</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00424A" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2026, Advance Article&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00424A, Paper&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;Jason L. Wu, David M. Friday, Changhyun Hwang, Seungjoo Yi, Tiara C. Torres-Flores, Martin D. Burke, Ying Diao, Charles M. Schroeder, Nicholas E. Jackson&lt;br /&gt;Machine learning (ML) is increasingly central to chemical discovery, yet most efforts remain confined to distributed and isolated research groups, limiting external validation and community engagement.&lt;br /&gt;To cite this article before page numbers are assigned, use the DOI form of citation above.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Wed, 19 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00424A</guid></item><item><title>[iScience] An Explainable Machine Learning Model Predicts 30-Day Readmission after Vertebral Augmentation</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02400-9?rss=yes</link><description>Osteoporotic vertebral compression fracture (OVCF) patients face high 30-day readmission risks after vertebral augmentation procedures (VAPs). Using electronic health records (EHRs) of 3,947 OVCF patients who underwent VAPs (2019–2024), we developed an interpretable machine learning model to identify readmission predictors. Eight algorithms were evaluated via 10-fold cross-validation, and XGBoost showed the best performance (area under the curve [AUC], sensitivity, specificity, F1 score, and decision curve analysis).</description><author>iScience</author><pubDate>Wed, 19 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02400-9?rss=yes</guid></item><item><title>[Wiley: SmartMat: Table of Contents] Machine Learning Design of Soft Magnetoresponsive Materials With Multidirectional Fields</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smm2.70051?af=R</link><description>SmartMat, Volume 6, Issue 6, December 2025.</description><author>Wiley: SmartMat: Table of Contents</author><pubDate>Tue, 18 Nov 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">10.1002/smm2.70051</guid></item><item><title>[RSC - Digital Discovery latest articles] Advancing metal organic framework and covalent organic framework design via the digital-intelligent paradigm</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00401B</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00401B" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2026, Advance Article&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00401B, Review 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;Bing Ma, Na Qin, Qianqian Yan, Wei Zhou, Sheng Zhang, Xiao Wang, Lipiao Bao, Xing Lu&lt;br /&gt;AI and machine learning combined with multiscale simulations accelerate framework materials design. This review summarizes AI-assisted strategies for synthesis prediction, condition optimization, and inverse functional design.&lt;br /&gt;To cite this article before page numbers are assigned, use the DOI form of citation above.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Tue, 18 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00401B</guid></item><item><title>[RSC - Digital Discovery latest articles] Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00294J</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00294J" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2026, Advance Article&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00294J, Paper&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/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.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Ilgar Baghishov, Jan Janssen, Graeme Henkelman, Danny Perez&lt;br /&gt;Simultaneously tuning DFT convergence, data selection, and energy-force weights reveals a Pareto front of optimal MLIPs. This minimizes costs by tailoring the MLIP to the specific accuracy requirements of the target application.&lt;br /&gt;To cite this article before page numbers are assigned, use the DOI form of citation above.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Tue, 18 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00294J</guid></item><item><title>[Applied Physics Reviews Current Issue] Synergistic integration of metasurfaces and quantum photonics: Pathways to next-generation technologies</title><link>https://pubs.aip.org/aip/apr/article/12/4/041318/3372590/Synergistic-integration-of-metasurfaces-and</link><description>&lt;span class="paragraphSection"&gt;The convergence of metamaterials and quantum optics heralds a transformative era in photonic technologies, poised to revolutionize applications ranging from information processing and imaging to sensing and beyond. This review explores the synergistic integration of metasurfaces—engineered sub-wavelength planar structures—and quantum optics, which exploits quantum mechanical principles to manipulate light at the most granular level. We outline the design principles, fabrication processes, and computational challenges involved in creating quantum metasurfaces, discussing both forward and inverse design approaches. Advances in nanofabrication and intelligent optimization techniques, such as machine learning and topology optimization, have enabled the development of metasurfaces with unparalleled control over electromagnetic waves. We examine recent progress in using quantum metasurfaces for single-photon and multi-photon generation, quantum imaging, and quantum sensing, showcasing how these innovations achieve unprecedented precision and novel functionalities. Additionally, we highlight the integration of metasurfaces into quantum light manipulation, emphasizing their role in enhancing wavefront shaping and entanglement control. By providing a comprehensive survey of current advancements and future research directions, this review highlights the vast potential of metasurfaces and quantum optics at the crossroads, setting the stage for next-generation technological innovations that will define the forthcoming decade.&lt;/span&gt;</description><author>Applied Physics Reviews Current Issue</author><pubDate>Mon, 17 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/12/4/041318/3372590/Synergistic-integration-of-metasurfaces-and</guid></item><item><title>[AI for Science - latest papers] Universal machine learning potentials for systems with reduced dimensionality</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae1208</link><description>We present a benchmark designed to evaluate the predictive capabilities of universal machine learning interatomic potentials (MLIPs) across systems of varying dimensionality. Specifically, our benchmark tests zero- (molecules, atomic clusters, etc), one- (nanowires, nanoribbons, nanotubes, etc), two- (atomic layers and slabs) and three-dimensional (3D) (bulk materials) compounds. The benchmark reveals that while all tested models demonstrate excellent performance for 3D systems, accuracy degrades progressively for lower-dimensional structures. The best performing models for geometry optimization are orbital version 2, equiformerV2, and the equivariant Smooth Energy Network, with the equivariant Smooth Energy Network also providing the most accurate energies. Our results indicate that the best models yield, on average, errors in the atomic positions in the range of 0.01–0.02 Å and errors in the energy below 10 meV atom−1 across all dimensionalities. These results demonstrate that state-of-the-art universal MLIPs have reached sufficient accuracy to serve as direct replacements for density functional theory calculations, at a small fraction of the computational cost, in simulations spanning the full range from isolated atoms to bulk solids. More significantly, the best performing models already enable efficient simulations of complex systems containing subsystems of mixed dimensionality, opening new possibilities for modeling realistic materials and interfaces.</description><author>AI for Science - latest papers</author><pubDate>Mon, 17 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae1208</guid></item><item><title>[Cell Reports Physical Science] Conjugated polyelectrolyte-aptamer hybrid for organic-electrochemical-transistor-based sensing</title><link>https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00564-8?rss=yes</link><description>Jiang et al. present biofunctionalized organic mixed ionic-electronic conductors (OMIECs), specifically single-component materials that integrate high specificity with semiconducting properties, exemplified by p(NDI-T-ZI/EG)-aptamer. This hybrid design enables covalent attachment of diverse functional units, thereby expanding the library of sensory OMIECs for future diagnostic applications.</description><author>Cell Reports Physical Science</author><pubDate>Mon, 17 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00564-8?rss=yes</guid></item><item><title>[Cell Reports Physical Science] CatBench framework for benchmarking machine learning interatomic potentials in adsorption energy predictions for heterogeneous catalysis</title><link>https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00567-3?rss=yes</link><description>Moon et al. introduce CatBench, a framework that systematically evaluates machine learning interatomic potentials for predicting molecular adsorption from small to large molecules on catalyst surfaces. Testing 13 state-of-the-art models across ≥47,000 reactions, they identify optimal accuracy-speed trade-offs and provide quantitative guidance for selecting models for practical catalyst discovery.</description><author>Cell Reports Physical Science</author><pubDate>Mon, 17 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00567-3?rss=yes</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] An O2-Independent Copper(II) Phototherapeutic Agent for Photoactivating H2O2 to Enhance Antitumor Immunotherapy</title><link>http://dx.doi.org/10.1021/jacs.5c14960</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c14960/asset/images/medium/ja5c14960_0009.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.5c14960&lt;/div&gt;</description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Sun, 16 Nov 2025 13:54:08 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c14960</guid></item><item><title>[Wiley: ENERGY &amp; ENVIRONMENTAL MATERIALS: Table of Contents] Liquid‐Phase Synthesis of Halide Solid Electrolytes for All‐Solid‐State Batteries Using Organic Solvents</title><link>https://onlinelibrary.wiley.com/doi/10.1002/eem2.70184?af=R</link><description>ENERGY &amp;amp;ENVIRONMENTAL MATERIALS, EarlyView.</description><author>Wiley: ENERGY &amp; ENVIRONMENTAL MATERIALS: Table of Contents</author><pubDate>Fri, 14 Nov 2025 14:05:17 GMT</pubDate><guid isPermaLink="true">10.1002/eem2.70184</guid></item><item><title>[Proceedings of the National Academy of Sciences: Physical Sciences] How public involvement can improve the science of AI</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2421111122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 48, December 2025. &lt;br /&gt;As AI systems from decision-making algorithms to generative AI are deployed more widely, computer scientists and social scientists alike are being called on to provide trustworthy quantitative evaluations of AI safety and reliability. These calls have ...</description><author>Proceedings of the National Academy of Sciences: Physical Sciences</author><pubDate>Fri, 14 Nov 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2421111122?af=R</guid></item><item><title>[AI for Science - latest papers] TorchSim: an efficient atomistic simulation engine in PyTorch</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae1799</link><description>We introduce TorchSim, an open-source atomistic simulation engine tailored for the machine learned interatomic potential (MLIP) era. By rewriting core atomistic simulation primitives in PyTorch, TorchSim can achieve orders of magnitude acceleration for popular MLIPs. Unlike existing molecular dynamics (MD) packages, which simulate one system at a time, TorchSim performs batched simulations that efficiently utilize modern GPUs by evolving multiple systems concurrently. TorchSim supports MD integrators, structural relaxation optimizers, both machine-learned and classical interatomic potentials (such as Lennard–Jones, Morse, soft-sphere), batching with automatic memory management, differentiable simulation, and integration with popular materials informatics tools.</description><author>AI for Science - latest papers</author><pubDate>Fri, 14 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae1799</guid></item><item><title>[AI for Science - latest papers] Graph learning metallic glass discovery from Wikipedia</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae1b20</link><description>Synthesizing new materials efficiently is highly demanded in various research fields. However, this process is usually slow and expensive, especially for metallic glasses, whose formation strongly depends on the optimal combinations of multiple elements to resist crystallization. This constraint renders only several thousands of candidates explored in the vast material space since 1960. Recently, data-driven approaches armed by advanced machine learning techniques provided alternative routes for intelligent materials design. Due to data scarcity and immature material encoding, the conventional tabular data is usually mined by statistical learning algorithms, giving limited model predictability and generalizability. Here, we propose sophisticated data learning from material network representations. The node elements are encoded from the Wikipedia by a language model. Graph neural networks with versatile architectures are designed to serve as recommendation systems to explore hidden relationships among materials. By employing Wikipedia embeddings from different languages, we assess the capability of natural languages in materials design. Our study proposes a new paradigm to harvesting new amorphous materials and beyond with artificial intelligence.</description><author>AI for Science - latest papers</author><pubDate>Fri, 14 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae1b20</guid></item><item><title>[ACS Materials Au: Latest Articles (ACS Publications)] [ASAP] Molecular Modeling-Based Machine Learning for Accurate Prediction of Gas Diffusivity and Permeability in Metal–Organic Frameworks</title><link>http://dx.doi.org/10.1021/acsmaterialsau.5c00111</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsmaterialsau.5c00111/asset/images/medium/mg5c00111_0007.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;ACS Materials Au&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acsmaterialsau.5c00111&lt;/div&gt;</description><author>ACS Materials Au: Latest Articles (ACS Publications)</author><pubDate>Wed, 12 Nov 2025 18:15:35 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsmaterialsau.5c00111</guid></item><item><title>[Recent Articles in PRX Energy] Dynamic Vacancy Levels in ${\mathrm{Cs}\mathrm{Pb}\mathrm{Cl}}_{3}$ Obey Equilibrium Defect Thermodynamics</title><link>http://link.aps.org/doi/10.1103/dxmb-8s96</link><description>Author(s): Irea Mosquera-Lois and Aron Walsh&lt;br /&gt;&lt;p&gt;This study of halide perovskites uses advanced molecular dynamics simulations with machine learning force fields to identify dynamic defect levels and their impact on the material’s optoelectronic properties.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/dxmb-8s96.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 043008] Published Wed Nov 12, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Wed, 12 Nov 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/dxmb-8s96</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Accelerating Primary Screening of USP8 Inhibitors from Drug Repurposing Databases with Tree‐Based Machine Learning</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500108?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Tue, 11 Nov 2025 04:16:54 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500108</guid></item><item><title>[RSC - Digital Discovery latest articles] Design of simple-structured conjugated polymers for organic solar cells by machine learning-assisted structural modification and experimental validation</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00418G</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00418G" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2025, &lt;b&gt;4&lt;/b&gt;,3774-3781&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00418G, Paper&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/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.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Shogo Tadokoro, Ryosuke Kamimura, Fumitaka Ishiwari, Akinori Saeki&lt;br /&gt;We explore simple-structured p-type polymers for organic photovoltaics using machine learning (ML) based on the primitive use of the molecular size and synthetic accessibility. Experimental validation shows good agreement with the ML prediction.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Tue, 11 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00418G</guid></item><item><title>[Joule] Entropy-guided discovery of denary trirutile antimonates for electrocatalytic chlorine evolution</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00381-2?rss=yes</link><description>Entropy-guided trirutile antimonates are developed as highly efficient catalysts for the chlorine evolution reaction. By integrating machine learning, DFT calculations, and operando experiments, this work uncovers atomic-level mechanisms governing catalytic activity and stability.</description><author>Joule</author><pubDate>Tue, 11 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00381-2?rss=yes</guid></item><item><title>[AI for Science - latest papers] Surface stability modeling with universal machine learning interatomic potentials: a comprehensive cleavage energy benchmarking study</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae1408</link><description>Machine learning interatomic potentials (MLIPs) have revolutionized computational materials science by bridging the gap between quantum mechanical accuracy and classical simulation efficiency, enabling unprecedented exploration of materials properties across the periodic table. Despite their remarkable success in predicting bulk properties, no systematic evaluation has assessed how well these universal MLIPs (uMLIPs) can predict cleavage energies, a critical property governing fracture, catalysis, surface stability, and interfacial phenomena. Here, we present a comprehensive benchmark of 19 state-of-the-art uMLIPs for cleavage energy prediction using our previously established density functional theory database of 36 718 slab structures spanning elemental, binary, and ternary metallic compounds. We evaluate diverse architectural paradigms, analyzing their performance across chemical compositions, crystal systems, thickness, and surface orientations. Our zero-shot evaluation results reveal that training data composition dominates architectural sophistication: models trained on the Open Materials 2024 (OMat24) dataset, which emphasizes non-equilibrium configurations, achieve mean absolute percentage errors below 6% and correctly identify the thermodynamically most stable surface terminations in 87% of cases, without any explicit surface energy training. In contrast, architecturally identical models trained on equilibrium-only datasets show five-fold higher errors, while models trained on surface-adsorbate data fail catastrophically with a 17-fold degradation. Remarkably, simpler architectures trained on appropriate data achieve comparable accuracy to complex transformers while offering 10–100× computational speedup. These findings fundamentally reframe MLIP development priorities: highlighting that strategic training-data generation with appropriate non-equilibrium sampling deserves equal or greater attention than architectural complexity.</description><author>AI for Science - latest papers</author><pubDate>Fri, 07 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae1408</guid></item><item><title>[Joule] Li–Si compound anodes enabling high-performance all-solid-state Li-ion batteries</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00376-9?rss=yes</link><description>Li–Si compound anodes, exemplified by Li2.33Si, overcome the degradation issues of conventional Si anodes by combining high ionic and electronic conductivity, favorable mechanical properties, and a negligible-volume-change Li-storage mechanism (Li2.33 + αSi, 0 &lt; α &lt; 0.92), thereby enabling high areal capacity, long cycle life, and fast rate capability in all-solid-state Li-ion batteries.</description><author>Joule</author><pubDate>Fri, 07 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00376-9?rss=yes</guid></item><item><title>[ACS Physical Chemistry Au: Latest Articles (ACS Publications)] [ASAP] Machine Learning as a Method for Retrieving Pressure Values by Analyzing Spectral Line Parameters: The Hydrochloric Acid Case</title><link>http://dx.doi.org/10.1021/acsphyschemau.5c00097</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsphyschemau.5c00097/asset/images/medium/pg5c00097_0010.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;ACS Physical Chemistry Au&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acsphyschemau.5c00097&lt;/div&gt;</description><author>ACS Physical Chemistry Au: Latest Articles (ACS Publications)</author><pubDate>Tue, 04 Nov 2025 19:09:10 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsphyschemau.5c00097</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Near-Infrared-II Aggregation-Induced Emission Photosensitizers with Mitochondrial Respiration Perturbation Activity Amplifies Ferroptosis, Necroptosis, and Apoptosis for Cancer Immunotherapy</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505952?af=R</link><description>&lt;p&gt;&lt;img src="https://www.chinesechemsoc.org/cms/asset/f7af22ce-ffaa-4e3f-8d56-8d413c6d2d18/keyimage.jpg" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;CCS Chemistry, Ahead of Print.&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI:10.31635/ccschem.025.202505952&lt;/div&gt;Activation of programmed cell death (PCD) networks in cancer cells represents an emerging paradigm in precision oncology. However, conventional antitumor agents remain constrained by insufficient cellular targeting and limited capacity to engage multiple ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Tue, 04 Nov 2025 04:36:34 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505952?af=R</guid></item><item><title>[Applied Physics Reviews Current Issue] Dynamic landscape of chemiresistive breathomic nanosensors based on fifth-generation chips for complex disease diagnosis and healthcare monitoring</title><link>https://pubs.aip.org/aip/apr/article/12/4/041311/3371043/Dynamic-landscape-of-chemiresistive-breathomic</link><description>&lt;span class="paragraphSection"&gt;The growing global population is overwhelming the existing medical infrastructure, demanding a pressing need for the advancement of early-stage and point-of-care disease diagnostics. Conventional techniques are mostly invasive, time-consuming, expensive, sophisticated, and centered at urban facilities. Moreover, they are unable to address the biological complexities related to critical diseases, disorders, and pandemics, resulting in associated high morbidity and mortality. To address this gap, miniaturized fifth-generation sensing chips provide alternatives in terms of accessibility, affordability, and adaptability, being point-of-care and minimally invasive diagnostics. In this context, Breathomic chips based on nanoscale semiconductors have shown their potential for noninvasive, personalized, and on-site operation, offering the capability to identify volatile organic compounds/gases as disease biomarkers from exhaled breath and enabling early disease detection. However, the practical implementation of these sensors in real-time medical contexts remains challenging due to factors including the lack of clinical trials, dedicated data analysis, understanding of the complexities, public awareness, scalability, and accessibility. This comprehensive review critically summarizes the landscape of breath biomarkers detecting fifth-generation chemiresistive chips for human disease diagnosis, methodically outlining associated challenges, alternative strategies, and prospects for clinical implementations and commercial advancement. It details the biological origins of biomarkers, the diverse sensing modalities, and the underlying mechanisms pertaining to breathomic biomarker diagnosis. Furthermore, it highlights the integration of digital-age technologies, including nanotechnology, artificial intelligence, bioinformatics, and machine learning, for high-performance breathomic chips. These next-generation smart sensory chips have the potential to revolutionize medical healthcare facilities, improving patient outcomes, understanding prognosis, and aiding the UN's sustainable development goals.&lt;/span&gt;</description><author>Applied Physics Reviews Current Issue</author><pubDate>Tue, 04 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/12/4/041311/3371043/Dynamic-landscape-of-chemiresistive-breathomic</guid></item><item><title>[RSC - Digital Discovery latest articles] Machine learning of polyurethane prepolymer viscosity: a comparison of chemical and physicochemical approaches</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00287G</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00287G" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2025, &lt;b&gt;4&lt;/b&gt;,3652-3661&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00287G, Paper&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;Joseph A. Pugar, Calvin Gang, Isabelle Millan, Karl Haider, Newell R. Washburn&lt;br /&gt;A dual machine learning framework predicts polyurethane prepolymer viscosity using either chemical composition or physicochemical descriptors, balancing accuracy for known chemistries with generalizability to new formulations.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Tue, 04 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00287G</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Biodegradable Cesium Nanosalts Activating Antitumor Immunity via Inducing Cellular Pyroptosis and Interfering with Metabolism</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202506187?af=R</link><description>&lt;p&gt;&lt;img src="https://www.chinesechemsoc.org/cms/asset/8dc60c7d-045c-4cad-8445-d4353b034d07/keyimage.png" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;CCS Chemistry, Ahead of Print.&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI:10.31635/ccschem.025.202506187&lt;/div&gt;Nanosalts deserve to be taken seriously as an important kind of superior antitumor agent. However, nanosalts for tumor therapy are still a little-touched “Blue Ocean.” Broadening the material library of nanosalts is particularly important to extend their ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Mon, 03 Nov 2025 01:13:27 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202506187?af=R</guid></item><item><title>[tandf: Materials Research Letters: Table of Contents] Machine learning-assisted design of strong and ductile BCC high-entropy alloys</title><link>https://www.tandfonline.com/doi/full/10.1080/21663831.2025.2577751?af=R</link><description>Volume 13, Issue 12, December 2025, Page 1260-1268&lt;br /&gt;. &lt;br /&gt;</description><author>tandf: Materials Research Letters: Table of Contents</author><pubDate>Thu, 30 Oct 2025 12:22:23 GMT</pubDate><guid isPermaLink="true">/doi/full/10.1080/21663831.2025.2577751?af=R</guid></item><item><title>[RSC - Digital Discovery latest articles] FFLAME: a fragment-to-framework learning approach for MOF potentials</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00321K</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00321K" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2025, &lt;b&gt;4&lt;/b&gt;,3466-3477&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00321K, Paper&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/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.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Xiaoqi Zhang, Yutao Li, Xin Jin, Berend Smit&lt;br /&gt;FFLAME, a fragment-centric strategy for training transferable MOF machine learning potentials, learns from building blocks, lowers data needs, and achieves near-target accuracy with minimal fine-tuning even for unseen MOFs.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Thu, 30 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00321K</guid></item><item><title>[RSC - Digital Discovery latest articles] Machine learning generalised DFT+U projectors in a numerical atom-centred orbital framework</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00292C</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00292C" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2025, &lt;b&gt;4&lt;/b&gt;,3701-3727&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00292C, Paper&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/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.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Amit Chaudhari, Kushagra Agrawal, Andrew J. Logsdail&lt;br /&gt;We present machine learning-based workflows using symbolic regression and support vector machines to simultaneously optimise Hubbard &lt;em&gt;U&lt;/em&gt; values and projectors, enabling accurate and efficient simulations of defects and polarons in complex metal oxides.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Thu, 30 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00292C</guid></item><item><title>[RSC - Digital Discovery latest articles] Leveraging large language models for enzymatic reaction prediction and characterization</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00187K</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00187K" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2025, &lt;b&gt;4&lt;/b&gt;,3588-3609&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00187K, Paper&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/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.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Lorenzo Di Fruscia, Jana M. Weber&lt;br /&gt;We present a systematic study of multitask Large Language Models, fine-tuned to predict enzyme commission numbers and to perform forward and retrosynthesis of enzymatic reactions.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Thu, 30 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00187K</guid></item><item><title>[RSC - Digital Discovery latest articles] Cross-laboratory validation of machine learning models for copper nanocluster synthesis using cloud-based automated platforms</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00335K</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00335K" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2025, &lt;b&gt;4&lt;/b&gt;,3683-3692&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00335K, Paper&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/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.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Ricardo Montoya-Gonzalez, Rosa de Guadalupe González-Huerta, Martha Leticia Hernández-Pichardo, Subha R. Das&lt;br /&gt;Cloud laboratory robotic synthesis of copper nanoclusters combined with ML enables predictive models from just 40 experiments&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Thu, 30 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00335K</guid></item><item><title>[Wiley: InfoMat: Table of Contents] Delicate design of lithium‐ion bridges in hybrid solid electrolyte for wide‐temperature adaptive solid‐state lithium metal batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/inf2.70095?af=R</link><description>InfoMat, EarlyView.</description><author>Wiley: InfoMat: Table of Contents</author><pubDate>Wed, 29 Oct 2025 00:36:10 GMT</pubDate><guid isPermaLink="true">10.1002/inf2.70095</guid></item><item><title>[APL Machine Learning Current Issue] Building an affordable self-driving lab: Practical machine learning experiments for physics education using Internet-of-Things</title><link>https://pubs.aip.org/aip/aml/article/3/4/046105/3369966/Building-an-affordable-self-driving-lab-Practical</link><description>&lt;span class="paragraphSection"&gt;Machine learning (ML) is transforming modern physics research, but practical, hands-on experience with ML techniques remains limited due to cost and complexity barriers. To address this gap, we introduce an affordable, autonomous, Internet-of-Things (IoT)-enabled experimental platform designed specifically for applied physics education. Utilizing an Arduino microcontroller, a customizable multi-wavelength light emitting diode array, and photosensors, our setup generates diverse, real-time optical datasets ideal for training and evaluating foundational ML algorithms, including traversal methods, Bayesian inference, and deep learning. The platform facilitates a closed-loop, self-driving experimental workflow, encompassing automated data collection, preprocessing, model training, and validation. Through systematic performance comparisons, we demonstrate the superior ability of deep learning to capture complex nonlinear relationships compared to traversal and Bayesian methods. At ∼$60, this open-source IoT platform provides an accessible, practical pathway for students to master advanced ML concepts, promoting deeper conceptual insights and essential technical skills required for the next generation of physicists and engineers.&lt;/span&gt;</description><author>APL Machine Learning Current Issue</author><pubDate>Wed, 29 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/046105/3369966/Building-an-affordable-self-driving-lab-Practical</guid></item><item><title>[APL Machine Learning Current Issue] Data integration and data fusion approaches in self-driving labs: A perspective</title><link>https://pubs.aip.org/aip/aml/article/3/4/040901/3369947/Data-integration-and-data-fusion-approaches-in</link><description>&lt;span class="paragraphSection"&gt;Self-driving laboratories (SDLs) are transforming materials discovery by combining automation, machine learning, and real-time feedback. Yet, their success depends on robust data integration and fusion methods capable of handling materials data that are heterogeneous, sparse, and multi-scale. Such data span theoretical models, simulations, and experimental techniques across diverse spatial and temporal scales, creating significant challenges for interoperability and analysis. This perspective reviews the state-of-the-art techniques, including knowledge graphs, structured pipelines, multimodal machine learning, and physics-informed models, that are enabling materials science and SDLs to unify and learn from disparate data sources, identify critical challenges, and propose forward-looking directions to enhance data readiness, interoperability, and predictive power in SDLs. We also highlight emerging methods such as transformer architectures, zero-shot learning, and real-time stream processing, and discuss the critical need for more scalable, interpretable, and adaptive solutions to fully realize autonomous materials innovation. By mapping out both the current landscape and future opportunities, we argue that next-generation data integration and fusion are not just enablers but essential pillars for achieving fully autonomous, adaptive, and intelligent SDL systems capable of addressing the complexities of hierarchical and multifunctional materials.&lt;/span&gt;</description><author>APL Machine Learning Current Issue</author><pubDate>Wed, 29 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/040901/3369947/Data-integration-and-data-fusion-approaches-in</guid></item><item><title>[RSC - Digital Discovery latest articles] Machine learning anomaly detection of automated HPLC experiments in the cloud laboratory</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00253B</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00253B" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2025, &lt;b&gt;4&lt;/b&gt;,3445-3454&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00253B, Paper&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/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.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Filipp Gusev, Benjamin C. Kline, Ryan Quinn, Anqin Xu, Ben Smith, Brian Frezza, Olexandr Isayev&lt;br /&gt;Autonomous experiments are vulnerable to unforeseen adverse events. We developed a transferable ML framework that flags affected HPLC runs in real time and provides expert-level quality control without human oversight.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Wed, 29 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00253B</guid></item><item><title>[Applied Physics Reviews Current Issue] Strain engineering of van Hove singularity and coupled itinerant ferromagnetism in quasi-2D oxide superlattices</title><link>https://pubs.aip.org/aip/apr/article/12/4/041406/3369932/Strain-engineering-of-van-Hove-singularity-and</link><description>&lt;span class="paragraphSection"&gt;Engineering van Hove singularities (vHss) near the Fermi level, if feasible, offers a powerful route to control exotic quantum phases in electronic and magnetic behaviors. However, conventional approaches rely primarily on chemical and electrical doping and focus mainly on local electrical or optical measurements, limiting their applicability to coupled functionalities. In this study, a vHs-induced insulator-metal transition coupled with a ferromagnetic phase transition was empirically achieved in atomically designed quasi-2D SrRuO&lt;sub&gt;3&lt;/sub&gt; (SRO) superlattices via epitaxial strain engineering, which has not been observed in conventional 3D SRO systems. Theoretical calculations revealed that epitaxial strain effectively modulates the strength and energy positions of vHs of specific Ru orbitals, driving correlated phase transitions in the electronic and magnetic ground states. X-ray absorption spectroscopy confirmed the anisotropic electronic structure of quasi-2D SRO modulated by epitaxial strain. Magneto-optic Kerr effect and electrical transport measurements demonstrated modulated magnetic and electronic phases. Furthermore, magneto-electrical measurements detected significant anomalous Hall effect signals and ferromagnetic magnetoresistance, indicating the presence of magnetically coupled charge carriers in the 2D metallic regime. This study establishes strain engineering as a promising platform for tuning vHss and resultant itinerant ferromagnetism of low-dimensional correlated quantum systems.&lt;/span&gt;</description><author>Applied Physics Reviews Current Issue</author><pubDate>Tue, 28 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/12/4/041406/3369932/Strain-engineering-of-van-Hove-singularity-and</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Machine Learning‐Enhanced Random Matrix Theory Design for Human Immunodeficiency Virus Vaccine Development</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500124?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Mon, 27 Oct 2025 03:21:44 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500124</guid></item><item><title>[RSC - Digital Discovery latest articles] An improved machine learning strategy using structural features to predict the glass transition temperature of oxide glasses</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00326A</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00326A" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2025, &lt;b&gt;4&lt;/b&gt;,3764-3773&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00326A, Paper&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/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.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Satwinder Singh Danewalia, Kulvir Singh&lt;br /&gt;We present a physics-informed machine learning approach to predict the glass transition temperature (&lt;em&gt;T&lt;/em&gt;&lt;small&gt;&lt;sub&gt;&lt;em&gt;g&lt;/em&gt;&lt;/sub&gt;&lt;/small&gt;) of sodium borosilicate glasses.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Thu, 23 Oct 2025 23:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00326A</guid></item><item><title>[Applied Physics Reviews Current Issue] 3D-printed lithium-metal batteries: Multiscale architectures, hybrid technologies, and monolithic integration for next-generation energy storage</title><link>https://pubs.aip.org/aip/apr/article/12/4/041310/3369005/3D-printed-lithium-metal-batteries-Multiscale</link><description>&lt;span class="paragraphSection"&gt;Lithium-metal anodes, with their unmatched theoretical capacity (3860 mAh g&lt;sup&gt;−1&lt;/sup&gt;) and ultra-low electrochemical potential (−3.04 V vs standard hydrogen electrode), are pivotal for next-generation high-energy-density batteries. However, their practical deployment is hindered by persistent challenges—dendritic growth, unstable solid electrolyte interphases (SEIs), and severe volumetric expansion. Emerging as a transformative solution, three-dimensional (3D) printing enables the rational design of multiscale architectures (e.g., micro-lattice anodes and gradient-porous cathodes) and hybrid solid-state electrolytes to address these limitations. This review presents a pioneering synthesis of 3D printing's role in lithium-metal battery engineering, focusing on its capacity to regulate lithium-ion flux, stabilize SEIs, and suppress dendrite proliferation through hierarchical structural control. We systematically analyze four key additive manufacturing technologies (inkjet printing, direct ink writing, fused deposition modeling, and stereolithography), delineating their unique advantages in tailoring ion transport pathways and mechanical robustness. Furthermore, we propose multi-material co-printing strategies to resolve interfacial incompatibilities in monolithic lithium-metal batteries, a critical barrier in current research. By bridging additive manufacturing with electrochemical fundamentals, this work outlines a roadmap to harness 3D printing's full potential, addressing scalability challenges and advancing applications in aerospace, wearables, and biomedical devices where energy density and safety are paramount.&lt;/span&gt;</description><author>Applied Physics Reviews Current Issue</author><pubDate>Thu, 23 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/12/4/041310/3369005/3D-printed-lithium-metal-batteries-Multiscale</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Harnessing Large Language Models to Advance Microbiome Research: From Sequence Analysis to Clinical Applications</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500038?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Tue, 21 Oct 2025 05:48:44 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500038</guid></item><item><title>[RSC - Digital Discovery latest articles] GoFlow: efficient transition state geometry prediction with flow matching and E(3)-equivariant neural networks</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00283D</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00283D" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2025, &lt;b&gt;4&lt;/b&gt;,3492-3501&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00283D, Paper&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/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.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Leonard Galustian, Konstantin Mark, Johannes Karwounopoulos, Maximilian P.-P. Kovar, Esther Heid&lt;br /&gt;We introduce GoFlow, a package to generate 3D transition state geometries using flow matching.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Mon, 20 Oct 2025 23:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00283D</guid></item><item><title>[AI for Science - latest papers] AInstein: numerical Einstein metrics via machine learning</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae1117</link><description>A new semi-supervised machine learning package is introduced which successfully solves the Euclidean vacuum Einstein equations with a cosmological constant, without any symmetry assumptions. The model architecture contains subnetworks for each patch in the manifold-defining atlas. Each subnetwork predicts the components of a metric in its associated patch, with the relevant Einstein conditions of the form being used as independent loss components (here , where n is the dimension of the Riemannian manifold, and the Einstein constant ). To ensure the consistency of the global structure of the manifold, another loss component is introduced across the patch subnetworks which enforces the coordinate transformation between the patches, , for an appropriate analytically known Jacobian J. We test our method for the case of spheres represented by a pair of patches in dimensions 2, 3, 4, and 5. In dimensions 2 and 3, the geometries have been fully classified. However, it is unknown whether a Ricci-flat metric can exist on spheres in dimensions 4 and 5. This work hints against the existence of such a metric.</description><author>AI for Science - latest papers</author><pubDate>Thu, 16 Oct 2025 23:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae1117</guid></item><item><title>[Matter] SpectroGen: A physically informed generative artificial intelligence for accelerated cross-modality spectroscopic materials characterization</title><link>https://www.cell.com/matter/fulltext/S2590-2385(25)00477-1?rss=yes</link><description>SpectroGen seamlessly couples physics-driven distribution models with a variable autoencoder to generate synthetic spectra indistinguishable from real data. By speeding up high-throughput screening, it closes the gap between AI-based materials discovery and experimental confirmation. Its flexible architecture accommodates diverse spectroscopic techniques, extending its utility across multiple scientific domains. The synergy of rapid AI-driven design and swift AI-enabled characterization expedites validation of innovative materials, bridging lab-based discovery and industry-ready applications to address urgent societal needs.</description><author>Matter</author><pubDate>Tue, 14 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/matter/fulltext/S2590-2385(25)00477-1?rss=yes</guid></item><item><title>[Chem] Precisely modulating Li2CO3 coverage on Ni-rich cathode boosts sulfide solid-state lithium battery performance</title><link>https://www.cell.com/chem/fulltext/S2451-9294(25)00366-3?rss=yes</link><description>It is of great importance to address the issues of stability and charge transfer at the cathode/electrolyte interface in all-solid-state lithium batteries (ASSLBs). We proposed a CO2 atmosphere treatment to precisely modulate Li2CO3 coverage on Ni-rich layered oxide cathodes (NRLOs) with minimal damage, effectively utilizing the advantageous effect while avoiding the harmful effect of surficial Li2CO3 on the interface. This fundamental mechanism offers insights for optimizing the NRLO/sulfide solid electrolyte interface and advancing high-energy-density ASSLBs.</description><author>Chem</author><pubDate>Tue, 14 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/chem/fulltext/S2451-9294(25)00366-3?rss=yes</guid></item><item><title>[RSC - Digital Discovery latest articles] Do Llamas understand the periodic table?</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00374A</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00374A" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2025, &lt;b&gt;4&lt;/b&gt;,3455-3465&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00374A, Paper&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/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.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Ge Lei, Samuel J. Cooper&lt;br /&gt;We observe a 3D spiral structure in the hidden states of LLMs that aligns with the conceptual structure of the periodic table.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Mon, 13 Oct 2025 23:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00374A</guid></item><item><title>[Applied Physics Reviews Current Issue] Carbon-based memristors for neuromorphic computing</title><link>https://pubs.aip.org/aip/apr/article/12/4/041307/3367658/Carbon-based-memristors-for-neuromorphic-computing</link><description>&lt;span class="paragraphSection"&gt;Driven by the rapid advancement of the Internet of Things and artificial intelligence, computational power demands have experienced an exponential surge, thereby accentuating the inherent limitations of the conventional von Neumann architecture. Neuromorphic computing memristors are emerging as a promising solution to overcome this bottleneck. Among various material-based memristors, carbon-based memristors (CBMs) are particularly attractive due to their biocompatibility, flexibility, and stability, which make them well suited for next-generation neuromorphic applications. This review summarizes the recent advancements in CBMs and proposes potential application scenarios in neuromorphic computing. Representative CBMs and preparation methods of carbon-based materials in different dimensions (0D, 1D, 2D, and 3D) are presented, followed by structural, storage, and synaptic plasticity testing and switching mechanisms. The neural network architecture built by CBMs is summarized for image processing, wearable electronics, and three-dimensional integration. Finally, the future challenges and application prospects of CBMs are reviewed and summarized.&lt;/span&gt;</description><author>Applied Physics Reviews Current Issue</author><pubDate>Mon, 13 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/12/4/041307/3367658/Carbon-based-memristors-for-neuromorphic-computing</guid></item><item><title>[Matter] Dynamic pressure mapping of infant cervical spines using a wearable magnetoelastic patch</title><link>https://www.cell.com/matter/fulltext/S2590-2385(25)00529-6?rss=yes</link><description>To enable continuous monitoring of infant cervical motion, we present a kirigami-inspired soft magnetoelastic patch that conforms intimately to the cervical position without compromising comfort. By leveraging passive magnetic sensing and flexible structural design, the patch captures subtle biomechanical changes during cervical movements. Integrated with machine learning classification, it enables intelligent recognition of stress patterns, providing a non-invasive and adaptive solution for early assessment of infant cervical motion.</description><author>Matter</author><pubDate>Fri, 10 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/matter/fulltext/S2590-2385(25)00529-6?rss=yes</guid></item><item><title>[Matter] Machine learning-driven ligand engineering decodes and controls structural distortions in 2D perovskites</title><link>https://www.cell.com/matter/fulltext/S2590-2385(25)00525-9?rss=yes</link><description>This work deciphers how ligand molecular descriptors (i.e., nitrogen content, hydrogen bonding, and π-conjugation) govern structural distortions and optoelectronic properties in 2D perovskites. The authors demonstrate that machine learning can quantitatively correlate these descriptors with octahedral distortions (92.6% prediction accuracy) and enable the targeted synthesis of six new perovskites with tunable band gaps (1.91–2.39 eV). The established structure-property relationships and machine learning-driven design paradigm represent a transformative approach for accelerating the discovery of functional perovskites, bridging computational prediction with experimental validation for optoelectronic applications.</description><author>Matter</author><pubDate>Fri, 10 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/matter/fulltext/S2590-2385(25)00525-9?rss=yes</guid></item><item><title>[Applied Physics Reviews Current Issue] The enduring legacy of scanning spreading resistance microscopy: Overview, advancements, and future directions</title><link>https://pubs.aip.org/aip/apr/article/12/4/041305/3366934/The-enduring-legacy-of-scanning-spreading</link><description>&lt;span class="paragraphSection"&gt;Scanning spreading resistance microscopy (SSRM) has recently celebrated 30 years of existence when counting from the original patent of 1994. In this time, the technique has experienced an incredible journey with substantial evolutions that transformed SSRM from a small-scale experiment into a staple for chip manufacturing laboratories for physical analysis of materials, failure analysis, and process development of integrated circuits. As the nanoelectronics industry is ready for a new inflection point, with the introduction of nanosheet field-effect transistor to replace FinFETs and cell track scaling architectures such as the complementary field-effect transistors, SSRM is once again at a turning point. This review aims to highlight the state-of-the-art while discussing the emerging challenges introduced by the ever-increasing complexity in complementary metal–oxide–semiconductor (CMOS) manufacturing. We start by illustrating the unique capability of the SSRM technique, its origin, and its evolution. Next, we continue by showing the considerable research effort that enabled SSRM to transition to a tomographic sensing method in support of FinFET transistors. Here, the high aspect ratio fin geometry and the complex contacts technology have imposed important modifications to the original method. Later, we elaborate on some of the key challenges introduced by the upcoming device transition from three-sided channel FinFETs into nanosheet FETs, i.e., offering a four-sided electrostatic control of the channel. Finally, we present the use of machine learning for automation in carrier calibration with increased accuracy. We close by introducing some of the concepts that we consider promising for further extension of SSRM to obtain sub-nm structural information and doping profiles in the area of advanced FinFETs and nanosheet FET technologies, including (a) correlative analysis flow, (b) liquid-assisted probing, and (c) top–down and bottom–up multi-probe sensing schemes to merge low- and high-pressure SSRM scans.&lt;/span&gt;</description><author>Applied Physics Reviews Current Issue</author><pubDate>Wed, 08 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/12/4/041305/3366934/The-enduring-legacy-of-scanning-spreading</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Oxygen Vacancy-Modulated Molybdenum Oxide/Montmorillonite Heterointerface Nanoarchitectures for Plasmon-Enhanced Solar-Osmotic Energy Harvesting</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202506132?af=R</link><description>&lt;p&gt;&lt;img src="https://www.chinesechemsoc.org/cms/asset/762823f9-fd0a-4aff-acde-289220f569a8/keyimage.png" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;CCS Chemistry, Ahead of Print.&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI:10.31635/ccschem.025.202506132&lt;/div&gt;Two-dimensional (2D) nanofluidic membranes with precise ion transport kinetics hold transformative potential for next-generation ion-selective membranes, yet their practical implementation in osmotic energy conversion remains constrained by inherent ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Mon, 06 Oct 2025 03:22:16 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202506132?af=R</guid></item><item><title>[RSC - Digital Discovery latest articles] Constructing and explaining machine learning models for the exploration and design of boron-based Lewis acids</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00212E</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00212E" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2025, &lt;b&gt;4&lt;/b&gt;,3623-3634&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00212E, Paper&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;Juliette Fenogli, Laurence Grimaud, Rodolphe Vuilleumier&lt;br /&gt;Bridging ML and chemical intuition, interpretable models predict Lewis acidity with high accuracy and reveal design rules for tailoring boron-based Lewis acids.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Sun, 05 Oct 2025 23:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00212E</guid></item><item><title>[APL Machine Learning Current Issue] Deep learning model of myofilament cooperative activation and cross-bridge cycling in cardiac muscle</title><link>https://pubs.aip.org/aip/aml/article/3/4/046101/3365811/Deep-learning-model-of-myofilament-cooperative</link><description>&lt;span class="paragraphSection"&gt;Cardiac muscle contraction is driven by the cross-bridge cycle, where myosin heads generate force by cyclically attaching to and pulling on actin filaments using energy from ATP. Modeling this process is central to understanding cardiac sarcomere mechanics. In this study, we developed supervised machine learning (ML) models using artificial neural networks (ANNs) to simulate cross-bridge cycling and muscle behavior under isosarcometric, isometric, and isotonic conditions. Trained on synthetic data, the ANN captured nonlinear dependencies among calcium concentration, stiffness, sarcomere length, temperature, and force output. Error analysis through histograms and unity-line scatterplots validated prediction accuracy and identified underfitting and overfitting patterns. Comparisons across ANN architectures showed how hidden layer complexity affects model generalization. The present deep learning models accurately reproduced key physiological behaviors, including steady-state force–Ca&lt;sup&gt;2+&lt;/sup&gt; relations, sarcomere length changes, and force–velocity relations, and matched theoretical results. This work demonstrates the potential of ML tools to enhance cardiac muscle modeling and exploit existing experimental datasets for improved prediction of cardiac muscle diseases.&lt;/span&gt;</description><author>APL Machine Learning Current Issue</author><pubDate>Fri, 03 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/046101/3365811/Deep-learning-model-of-myofilament-cooperative</guid></item><item><title>[RSC - Digital Discovery latest articles] ReactPyR: a python workflow for ReactIR allows for quantification of the stability of sensitive compounds in air</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00305A</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00305A" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2025, &lt;b&gt;4&lt;/b&gt;,3533-3539&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00305A, Paper&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/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.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Nicola L. Bell, Emanuele Berardi, Marina Gladkikh, Richard Drummond Turnbull, Freya Turton&lt;br /&gt;ReactPyR enables automated ReactIR workflows to quantify air-sensitivity in organometallic reagents, delivering reproducible kinetic insights and guiding stabilisation strategies for safer, more efficient handling of highly reactive species.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Thu, 02 Oct 2025 23:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00305A</guid></item><item><title>[Applied Physics Reviews Current Issue] Extracellular vesicles as the drug delivery vehicle for gene-based therapy</title><link>https://pubs.aip.org/aip/apr/article/12/4/041301/3365609/Extracellular-vesicles-as-the-drug-delivery</link><description>&lt;span class="paragraphSection"&gt;Extracellular vesicles (EVs) are membrane-bound nanoparticles naturally secreted by cells, playing a vital role in intercellular communication and holding significant promise as therapeutic agents. These natural carriers deliver various molecules into cells, including proteins and nucleic acids. There are numerous methods to load and modify EVs, encompassing physical, chemical, and biological approaches. EVs demonstrate the capacity to target specific cells within organs, even requiring blood–tissue transition. The protein corona significantly influences EV availability and cargo delivery, with biomolecules residing both within and conjugated to the EV membrane. Furthermore, embedding EVs within biomaterials such as hydrogels, scaffolds, and nanofibers can enhance their stability, targeting specificity, and therapeutic potential. By addressing cargo loading and cell/tissue-specific targeting, EVs offer a novel therapeutic strategy for various diseases, including cancer, autoimmune disorders, and neurodegenerative diseases. Furthermore, EVs show promise as vaccination tools, delivering messenger RNA and proteins of various pathogens. Advances in EV biology and engineering would provide improved strategies for vesicle targeting, enhanced cargo loading, and safe and effective delivery. The convergence of technological advancements, interdisciplinary collaboration, and an enhanced understanding of EVs promises to revolutionize therapeutic approaches to a wide range of diseases, establishing EV-based treatments as a cornerstone of future medicine.&lt;/span&gt;</description><author>Applied Physics Reviews Current Issue</author><pubDate>Wed, 01 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/12/4/041301/3365609/Extracellular-vesicles-as-the-drug-delivery</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Discovery of Novel Materials with Giant Dielectric Constants via First‐Principles Phonon Calculations and Machine Learning</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500141?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Tue, 30 Sep 2025 06:30:24 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500141</guid></item><item><title>[AI for Science - latest papers] FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae0808</link><description>We introduce a rapid, accurate framework for computing atomic migration barriers in crystals by combining universal machine‐learning force fields (MLFFs) with 3D potential‐energy‐surface sampling and interpolation. Our method suppresses periodic self‐interactions via supercell expansion, builds a continuous potential energy surface (PES) from MLFF energies on a spatial grid, and extracts minimum‐energy pathways without predefined nudged elastic band (NEB) images. For a benchmark set of twelve electrode and electrolyte materials, including LiCoO2, LiFePO4, and Li10GeP2S12, our MLFF‐derived barriers lie within tens of meV of density functional theory (DFT) and experiment values, while achieving a ∼100-fold speedup over standard DFT‐NEB calculations. We benchmark GPTFF, CHGNet, and MACE, showing that fine‐tuning on PBE/PBE + U data further enhances accuracy. Ultimately, we introduce an open‐source package for high‐throughput materials screening and interactive PES visualization.</description><author>AI for Science - latest papers</author><pubDate>Mon, 29 Sep 2025 23:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae0808</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500060?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Wed, 24 Sep 2025 13:21:08 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500060</guid></item><item><title>[AI for Science - latest papers] 4D-MISR: a unified model for low-dose super-resolution imaging via feature fusion</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae05b6</link><description>While an electron microscope offers crucial atomic-resolution insights into structure–property relationships, radiation damage severely limits its use on beam-sensitive materials like proteins and 2D materials. To overcome this challenge, we push beyond the electron dose limits of the conventional method by adapting principles from multi-image super-resolution that had been widely used in remote sensing. Our method fuses multiple low-resolution, sub-pixel-shifted views and enhances this reconstruction with a convolutional neural network that integrates features from synthetic, multi-angle observations. We developed a dual-path, attention-guided network for four-dimensional scanning transmission electron microscopy (4D-STEM) that achieves atomic-scale super-resolution from ultra-low-dose data. This provides robust atomic-scale visualization across amorphous, semi-crystalline, and crystalline beam-sensitive specimens. Systematic evaluations on representative materials demonstrate the comparable spatial resolution to conventional ptychography under ultra low-dose conditions. Our work one-step expands the capabilities of 4D-STEM, offering a new and generalizable method for the structural analysis of any radiation-vulnerable material.</description><author>AI for Science - latest papers</author><pubDate>Wed, 17 Sep 2025 23:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae05b6</guid></item><item><title>[Recent Articles in PRX Energy] Thermodynamic Modeling of Complex Solid Solutions in the $\mathrm{Lu}$-$\mathrm{H}$-$\mathrm{N}$ System via Graph Neural Network Accelerated Monte Carlo Simulations</title><link>http://link.aps.org/doi/10.1103/bsxd-qtph</link><description>Author(s): Pin-Wen Guan, Catalin D. Spataru, Vitalie Stavila, Reese Jones, Peter A. Sharma, and Matthew D. Witman&lt;br /&gt;&lt;p&gt;A thermodynamic modeling framework captures interstitial lattice disorder in complex metal hydrides, yielding pressure- and temperature-dependent phase diagrams that align with experiments and show how nitrogen doping can lower dehydrogenation temperatures for optimized hydrogen-storage alloys.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/bsxd-qtph.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 033013] Published Tue Sep 02, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Tue, 02 Sep 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/bsxd-qtph</guid></item><item><title>[Recent Articles in PRX Energy] Reconstructions and Dynamics of $β$-Lithium Thiophosphate Surfaces</title><link>http://link.aps.org/doi/10.1103/5hf9-hlj6</link><description>Author(s): Hanna Türk, Davide Tisi, and Michele Ceriotti&lt;br /&gt;&lt;p&gt;Machine-learning-based molecular dynamics simulations of the solid electrolyte &lt;math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"&gt;&lt;mi&gt;β&lt;/mi&gt;&lt;/math&gt;-Li&lt;math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;/msub&gt;&lt;/math&gt;PS&lt;math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mn&gt;4&lt;/mn&gt;&lt;/msub&gt;&lt;/math&gt; under realistic conditions reveal dynamic surface structure and reactivity.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/5hf9-hlj6.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 033010] Published Tue Aug 26, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Tue, 26 Aug 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/5hf9-hlj6</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Application of Neural Networks for Advanced Ir Spectroscopy Characterization of Ceria Catalysts Surfaces</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500046?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Mon, 25 Aug 2025 07:01:05 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500046</guid></item><item><title>[Chem] Photocatalytic hydrogenation of alkenes using ammonia-borane</title><link>https://www.cell.com/chem/fulltext/S2451-9294(25)00302-X?rss=yes</link><description>Hydrogenation of olefins is a key reaction in chemistry; however, it typically requires the use of flammable hydrogen gas with transition metal catalysis. This work presents a novel approach employing ammonia-borane, a stable and non-toxic reagent, as a hydrogen surrogate material under photocatalytic conditions, which efficiently circumvents the use of hydrogen and metal catalysis.</description><author>Chem</author><pubDate>Mon, 25 Aug 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/chem/fulltext/S2451-9294(25)00302-X?rss=yes</guid></item><item><title>[Chem] Stereoselective C(sp3)-Si/Ge bond formation via nickel-catalyzed decarboxylative couplings</title><link>https://www.cell.com/chem/fulltext/S2451-9294(25)00304-3?rss=yes</link><description>A long-standing gap in the catalytic synthesis of Si- and Ge-glycosides—particularly Ge analogs—has been filled by a stereoselective Ni-catalyzed decarboxylative coupling. This strategy enables selective C(sp³)–Si/Ge bond formation from redox-active esters and (silyl/germyl) zinc reagents, operating through a distinct Ni(0)/Ni(I)/Ni(II) cycle involving SET-mediated N–O bond cleavage. Ge- and Si-glycosides demonstrated promising bioactivity for the first time. These findings expand the synthetic repertoire for bioactive glycomimetics and provide new mechanistic insights into NHPI ester activation in cross-coupling chemistry.</description><author>Chem</author><pubDate>Thu, 21 Aug 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/chem/fulltext/S2451-9294(25)00304-3?rss=yes</guid></item><item><title>[Matter] CGformer: Transformer-enhanced crystal graph network with global attention for material property prediction</title><link>https://www.cell.com/matter/fulltext/S2590-2385(25)00423-0?rss=yes</link><description>Designing new materials for better batteries is a major challenge, especially for complex “high-entropy” materials with countless atomic combinations. We developed a novel AI model, CGformer, that looks at the entire crystal structure to accurately predict material properties. By screening nearly 150,000 candidates, our AI identified promising new sodium-ion solid electrolytes. We successfully synthesized these materials, and they showed excellent performance, validating our AI-driven discovery pipeline. This work provides a powerful tool to accelerate the design of next-generation energy materials.</description><author>Matter</author><pubDate>Wed, 20 Aug 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/matter/fulltext/S2590-2385(25)00423-0?rss=yes</guid></item><item><title>[Chem] Organosilicon precursors for efficient aromatic copper-mediated radiocyanation</title><link>https://www.cell.com/chem/fulltext/S2451-9294(25)00298-0?rss=yes</link><description>We present a copper-mediated labeling of aryl silanes with carbon-11, a radioactive isotope used in positron emission tomography imaging. The approach enables late-stage and efficient tagging of organic molecules, helping to accelerate the development of new imaging agents for biomedical imaging.</description><author>Chem</author><pubDate>Wed, 20 Aug 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/chem/fulltext/S2451-9294(25)00298-0?rss=yes</guid></item><item><title>[Chem] Manganese low-energy photocatalysis for remodeling nitrogenation of alkenes</title><link>https://www.cell.com/chem/fulltext/S2451-9294(25)00293-1?rss=yes</link><description>A manganese low-energy photoredox catalytic platform via the in situ assembly of a manganese(II) salt, a bidentate N ligand, a nucleophilic azide reagent, and an alcohol is established. This catalytic platform enables the oxidative remodeling nitrogenation of alkenes, efficiently synthesizing ketonitriles, ketones, or nitriles with excellent functional group tolerance. Additionally, the feasibility for late-stage functionalization of drug molecule derivatives and streamlined synthesis of anabasine demonstrates the potential applications of this protocol in synthetic organic chemistry and biomedicine.</description><author>Chem</author><pubDate>Tue, 19 Aug 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/chem/fulltext/S2451-9294(25)00293-1?rss=yes</guid></item><item><title>[Recent Articles in PRX Energy] Large-Scale Simulation Unveiled Superior Potassium-Based Solid Electrolyte with High Ionic Conductivity and Excellent Electrochemical Stability in ${M}_{5}{\mathrm{YSi}}_{4}{\mathrm{O}}_{12}$ ($M=\mathrm{Li},\mathrm{K}$)</title><link>http://link.aps.org/doi/10.1103/8wkh-238p</link><description>Author(s): Zhao Li, Jiaxiang Li, Congwei Xie, Keith Butler, Fei Du, and Yu Xie&lt;br /&gt;&lt;p&gt;Advanced computational modeling predicts the ionic conductivity and electrochemical stability of a promising potassium-based solid electrolyte. The approach highlights the importance of longer length and time scales during simulations, achievable with machine learning potentials.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/8wkh-238p.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 033007] Published Thu Aug 14, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Thu, 14 Aug 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/8wkh-238p</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Interpretable Machine Learning for Solvent‐Dependent Carrier Mobility in Solution‐Processed Organic Thin Films</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500078?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Fri, 08 Aug 2025 09:54:45 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500078</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Macrophage Phenotype Detection Methodology on Textured Surfaces via Nuclear Morphology Using Machine Learning</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500055?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Fri, 01 Aug 2025 08:40:28 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500055</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500063?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Fri, 25 Jul 2025 08:24:33 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500063</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500079?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Thu, 24 Jul 2025 10:45:19 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500079</guid></item><item><title>[Recent Articles in PRX Energy] Origin of Intrinsically Low Thermal Conductivity in a Garnet-Type Solid Electrolyte: Linking Lattice and Ionic Dynamics with Thermal Transport</title><link>http://link.aps.org/doi/10.1103/6wj2-kzhh</link><description>Author(s): Yitian Wang, Yaokun Su, Jesús Carrete, Huanyu Zhang, Nan Wu, Yutao Li, Hongze Li, Jiaming He, Youming Xu, Shucheng Guo, Qingan Cai, Douglas L. Abernathy, Travis Williams, Kostiantyn V. Kravchyk, Maksym V. Kovalenko, Georg K.H. Madsen, Chen Li, and Xi Chen&lt;br /&gt;&lt;p&gt;Using a comprehensive experimental and computational approach, this work analyzes the intrinsically low thermal conductivity of solid ionic conductor Li&lt;math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;6&lt;/mn&gt;&lt;mo lspace="0" rspace="0"&gt;.&lt;/mo&gt;&lt;mn&gt;5&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;La&lt;math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;/msub&gt;&lt;/math&gt;Zr&lt;math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;mo lspace="0" rspace="0"&gt;.&lt;/mo&gt;&lt;mn&gt;5&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;Ta&lt;math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo lspace="0" rspace="0"&gt;.&lt;/mo&gt;&lt;mn&gt;5&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;O&lt;math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mn&gt;12&lt;/mn&gt;&lt;/msub&gt;&lt;/math&gt;, a promising electrolyte for all-solid-state batteries.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/6wj2-kzhh.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 033004] Published Thu Jul 17, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Thu, 17 Jul 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/6wj2-kzhh</guid></item><item><title>[Recent Articles in PRX Energy] A Comparative Study of Solid Electrolyte Interphase Evolution in Ether and Ester-Based Electrolytes for $\mathrm{Na}$-ion Batteries</title><link>http://link.aps.org/doi/10.1103/jfvb-wp5w</link><description>Author(s): Liang Zhao, Sara I.R. Costa, Yue Chen, Jack R. Fitzpatrick, Andrew J. Naylor, Oleg Kolosov, and Nuria Tapia-Ruiz&lt;br /&gt;&lt;p&gt;Diglyme-based electrolytes promote a thin, uniform, and stable solid electrolyte interphase that can extend the lifespan of sodium-ion batteries, as shown using advanced spectroscopic and electrochemical techniques.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/jfvb-wp5w.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 033002] Published Tue Jul 15, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Tue, 15 Jul 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/jfvb-wp5w</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Metalloligand Enabling Cobalt-Catalyzed anti-Markovnikov Hydrosilylation of Alkynes with Tertiary Silanes</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505983?af=R</link><description>&lt;p&gt;&lt;img src="https://www.chinesechemsoc.org/cms/asset/3b740e13-4c4b-4bf4-b136-f902e373730e/keyimage.png" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;CCS Chemistry, Ahead of Print.&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI:10.31635/ccschem.025.202505983&lt;/div&gt;Metal-catalyzed hydrosilylation of alkynes with hydrosilanes is a useful method for the preparation of vinylsilanes that are useful reagents in organic synthesis and the silicone industry. 3d metal catalysts affecting the hydrosilylation reactions of ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Tue, 15 Jul 2025 04:05:27 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505983?af=R</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500042?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Mon, 14 Jul 2025 15:08:07 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500042</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Inorganic Iodide Catalyzed Alkylation of Amines with Primary Alcohols</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505864?af=R</link><description>&lt;p&gt;&lt;img src="https://www.chinesechemsoc.org/cms/asset/873df89d-eae9-4fc3-9bd9-7c7642e36008/keyimage.png" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;CCS Chemistry, Ahead of Print.&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI:10.31635/ccschem.025.202505864&lt;/div&gt;Alkylation of amines with readily accessible reagents is one of the most efficient strategies for synthesizing substituted amines. Primary alcohols, being widely abundant, are excellent electrophiles that can undergo dehydration to react with various ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Fri, 11 Jul 2025 03:57:49 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505864?af=R</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Autonomous Machine Learning‐Based Classification and Arrangement of Submillimeter Objects Using a Capillary Force Gripper</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500068?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Wed, 09 Jul 2025 08:01:30 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500068</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500031?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Wed, 09 Jul 2025 07:56:18 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500031</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500074?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Fri, 27 Jun 2025 08:27:19 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500074</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500022?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Fri, 27 Jun 2025 08:15:35 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500022</guid></item><item><title>[ACS Materials Au: Latest Articles (ACS Publications)] [ASAP] Historical Data Mining Deep Dive into Machine Learning-Aided 2D Materials Research in Electrochemical Applications</title><link>http://dx.doi.org/10.1021/acsmaterialsau.5c00030</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsmaterialsau.5c00030/asset/images/medium/mg5c00030_0020.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;ACS Materials Au&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acsmaterialsau.5c00030&lt;/div&gt;</description><author>ACS Materials Au: Latest Articles (ACS Publications)</author><pubDate>Mon, 23 Jun 2025 15:22:16 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsmaterialsau.5c00030</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Machine Learning‐Assisted Infectious Disease Detection in Low‐Income Areas: Toward Rapid Triage of Dengue and Zika Virus Using Open‐Source Hardware</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500049?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Mon, 23 Jun 2025 08:20:28 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500049</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500033?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Fri, 20 Jun 2025 08:36:19 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500033</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Predicting High‐Resolution Spatial and Spectral Features in Mass Spectrometry Imaging with Machine Learning and Multimodal Data Fusion</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500021?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Wed, 18 Jun 2025 08:10:58 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500021</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] A Machine Learning Perspective on the Brønsted–Evans–Polanyi Relation in Water‐Gas Shift Catalysis on MXenes</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500045?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Wed, 18 Jun 2025 08:09:26 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500045</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Efficient Pure Organic Near-Infrared Room-Temperature Phosphorescence Based on n/π Orbital Decoupling</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202405319?af=R</link><description>&lt;p&gt;&lt;img src="https://www.chinesechemsoc.org/cms/asset/91960c98-a47a-4293-846c-f1e9b670d740/keyimage.jpg" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;CCS Chemistry, Ahead of Print.&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI:10.31635/ccschem.025.202405319&lt;/div&gt;Developing pure organic room-temperature phosphorescence (RTP) materials remains an enormous challenge, especially for efficient near-infrared (NIR) RTP materials. Herein, a functional unit combination strategy is employed to design a series of pure ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Sat, 14 Jun 2025 05:08:51 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202405319?af=R</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Identification of Cu-N2 Sites for Zn-Air Batteries in Harsh Electrolytes: Computer Virtual Screening, Machine Learning, and Practical Application</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505577?af=R</link><description>&lt;p&gt;&lt;img src="https://www.chinesechemsoc.org/cms/asset/48f68dbe-4a5c-4ae6-a581-ca6b1854a4fe/keyimage.jpg" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;CCS Chemistry, Ahead of Print.&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI:10.31635/ccschem.025.202505577&lt;/div&gt;Nonprecious-metal catalysts possess great potential to replace noble metals in catalysis; however, selecting efficient candidates through experiments is time-consuming and costly. Herein, we employed a data-driven virtual screening (VS) method to discover ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Sat, 14 Jun 2025 04:39:17 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505577?af=R</guid></item><item><title>[Recent Articles in PRX Energy] Correlating Local Morphology and Charge Dynamics via Kelvin Probe Force Microscopy to Explain Photoelectrode Performance</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.023010</link><description>Author(s): Maryam Pourmahdavi, Mauricio Schieda, Ragle Raudsepp, Steffen Fengler, Jiri Kollmann, Yvonne Pieper, Thomas Dittrich, Thomas Klassen, and Francesca M. Toma&lt;br /&gt;&lt;p&gt;Charge transport in photoelectrodes for photoelectrochemical cells is influenced by microstructural variations; here, the authors use Kelvin Probe Force Microscopy to correlate local morphology with optoelectronic properties toward optimizing materials toward material optimization.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.023010.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 023010] Published Mon Jun 09, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Mon, 09 Jun 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.023010</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Radical Amidoalkynylation of Electron-Rich Alkenes with Bifunctional Alkynylsulfonamide Reagents</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505686?af=R</link><description>&lt;p&gt;&lt;img src="https://www.chinesechemsoc.org/cms/asset/177265a3-baa5-4afd-b212-2041fd6f4032/keyimage.png" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;CCS Chemistry, Ahead of Print.&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI:10.31635/ccschem.025.202505686&lt;/div&gt;Radical amidoalkynylations of olefins offer a powerful platform for the rapid construction of both C–N and C–O bonds, generating vicinal aminoalkynes which are frequently found in biologically active molecules. Herein, we developed a practical two-...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Mon, 09 Jun 2025 02:05:34 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505686?af=R</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Development of Nanomolar Affinity Miniprotein Inhibitors Targeting α-Synuclein Aggregation as Promising Therapeutic Agents for Parkinson’s Disease</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505587?af=R</link><description>&lt;p&gt;&lt;img src="https://www.chinesechemsoc.org/cms/asset/85e9d696-a460-4ab8-9429-f4171d774f4c/keyimage.jpg" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;CCS Chemistry, Ahead of Print.&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI:10.31635/ccschem.025.202505587&lt;/div&gt;Parkinson’s disease (PD) is a debilitating neurodegenerative disorder characterized by the accumulation of α-synuclein (α-syn) aggregates in the brain. Developing effective therapies targeting α-syn has been challenging due to its intrinsically disordered ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Tue, 03 Jun 2025 05:02:30 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505587?af=R</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Harnessing Photoredox Cascade to Enhance Photodynamic Oncotherapy by Nanoformulated Macrocyclic Photosensitizer</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505567?af=R</link><description>&lt;p&gt;&lt;img src="https://www.chinesechemsoc.org/cms/asset/9d2289f8-faf8-4215-abd9-689d080163de/keyimage.jpg" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;CCS Chemistry, Ahead of Print.&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI:10.31635/ccschem.025.202505567&lt;/div&gt;Optically active nanoagents hold a dominant position in advanced phototheranostics, but there remain challenges in structural optimization and performance maximization for biomedical applications. Herein, a π-extended triphenylamine-containing ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Tue, 03 Jun 2025 04:56:28 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505567?af=R</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Minimal Sulfur-Grafted Graphite Anode with Accelerated Interfacial Kinetics for Fast-Charging Lithium-Ion Batteries</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505705?af=R</link><description>&lt;p&gt;&lt;img src="https://www.chinesechemsoc.org/cms/asset/3b36947a-a194-4971-8441-76ab0db62773/keyimage.png" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;CCS Chemistry, Ahead of Print.&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI:10.31635/ccschem.025.202505705&lt;/div&gt;The significant capacity decay and undesired metallic Li plating of graphite anode resulting from sluggish Li-ion diffusion kinetics at the graphite/electrolyte interface have largely hindered the fast-charging capability of lithium-ion batteries (LIBs). ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Wed, 28 May 2025 08:32:07 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505705?af=R</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Enantioconvergent Negishi Cross-Coupling of Racemic sec-Alkylzinc Reagent with Aryl Halides Enabled by Bulky N-Heterocyclic Carbene-Pd Catalyst</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505591?af=R</link><description>&lt;p&gt;&lt;img src="https://www.chinesechemsoc.org/cms/asset/e0a02a81-dbdc-4b77-98ea-862388e9596a/keyimage.png" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;CCS Chemistry, Ahead of Print.&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI:10.31635/ccschem.025.202505591&lt;/div&gt;Transition-metal-catalyzed cross-coupling reactions have revolutionized synthetic approaches for forging C–C bonds. However, catalytic enantioconvergent couplings of racemic secondary organometallics with aryl electrophiles remain a significant challenge. ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Mon, 19 May 2025 04:21:11 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505591?af=R</guid></item><item><title>[Recent Articles in PRX Energy] Resolving the Solvation Structure and Transport Properties of Aqueous Zinc Electrolytes from Salt-in-Water to Water-in-Salt Using Neural Network Potential</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.023004</link><description>Author(s): Chuntian Cao, Arun Kingan, Ryan C. Hill, Jason Kuang, Lei Wang, Chunyi Zhang, Matthew R. Carbone, Hubertus van Dam, Shinjae Yoo, Shan Yan, Esther S. Takeuchi, Kenneth J. Takeuchi, Xifan Wu, AM Milinda Abeykoon, Amy C. Marschilok, and Deyu Lu&lt;br /&gt;&lt;p&gt;A neural network potential model is developed for ZnCl&lt;math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/msub&gt;&lt;/math&gt; electrolytes that provides atomic scale insights into the solvation structure and ionic conductivity. The results agree well with experiment and shed light on the performance of aqueous zinc-ion batteries across a wide concentration range of ZnCl&lt;math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/msub&gt;&lt;/math&gt; electrolytes.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.023004.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 023004] Published Fri Apr 11, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Fri, 11 Apr 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.023004</guid></item><item><title>[Recent Articles in PRX Energy] Crystal Structure Prediction and Phase Stability in Highly Anharmonic Silver-Based Chalcohalide Antiperovskites</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.023002</link><description>Author(s): Pol Benítez, Cibrán López, Cong Liu, Ivan Caño, Josep-Lluís Tamarit, Edgardo Saucedo, and Claudio Cazorla&lt;br /&gt;&lt;p&gt;Using theoretical first-principles methods, stable and metastable phases of silver-based chalcohalide anti-perovskites are predicted, offering insight into their stability for potential energy and optoelectronic applications.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.023002.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 023002] Published Thu Apr 03, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Thu, 03 Apr 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.023002</guid></item><item><title>[Recent Articles in PRX Energy] Unraveling Temperature-Induced Vacancy Clustering in Tungsten: From Direct Microscopy to Atomistic Insights via Data-Driven Bayesian Sampling</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.013008</link><description>Author(s): Anruo Zhong, Clovis Lapointe, Alexandra M. Goryaeva, Kazuto Arakawa, Manuel Athènes, and Mihai-Cosmin Marinica&lt;br /&gt;&lt;p&gt;This study reveals how anharmonic, entropy-driven stabilization of di-vacancies at elevated temperatures reconcile theoretical predictions with experimental observations of vacancy clustering in tungsten, a prime candidate material for fusion reactors.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.013008.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 013008] Published Tue Feb 25, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Tue, 25 Feb 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.013008</guid></item><item><title>[Recent Articles in PRX Energy] Constant-Current Nonequilibrium Molecular Dynamics Approach for Accelerated Computation of Ionic Conductivity Including Ion-Ion Correlation</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.013005</link><description>Author(s): Ryoma Sasaki, Yoshitaka Tateyama, and Debra J. Searles&lt;br /&gt;&lt;p&gt;A new “constant-current” nonequilibrium molecular dynamics simulation method accelerates ionic conductivity calculations by up to two orders of magnitude while accurately capturing ion-ion correlations, enabling more efficient screening of solid electrolytes and revealing important low-temperature conduction behaviors.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.013005.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 013005] Published Wed Feb 19, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Wed, 19 Feb 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.013005</guid></item><item><title>[Recent Articles in PRX Energy] Detecting Attacks and Estimating States of Power Grids from Partial Observations with Machine Learning</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.013003</link><description>Author(s): Zheng-Meng Zhai, Mohammadamin Moradi, and Ying-Cheng Lai&lt;br /&gt;&lt;p&gt;Using data-driven machine learning models, this work demonstrates a method to map the dynamical state of a full power grid from limited observations, enabling the user to locate disruptions with information only from other areas of the grid.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.013003.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 013003] Published Tue Feb 04, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Tue, 04 Feb 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.013003</guid></item><item><title>[Recent Articles in PRX Energy] 3D Reconstruction of a Nuclear Reactor by Muon Tomography: Structure Validation and Anomaly Detection</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.013002</link><description>Author(s): Baptiste Lefevre, Julien Vogel, Héctor Gomez, David Attié, Laurent Gallego, Philippe Gonzales, Bertrand Lesage, Philippe Mas, and Daniel Pomarède&lt;br /&gt;&lt;p&gt;Muon tomography, a non-invasive technique that can be used to image large, inaccessible structures, is combined with machine learning to create a 3D reconstruction of a historical nuclear reactor that reveals material density variations, including potential anomalies in the graphite core.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.013002.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 013002] Published Tue Jan 28, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Tue, 28 Jan 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.013002</guid></item><item><title>[Recent Articles in PRX Energy] Determining Parameters of Metal-Halide Perovskites Using Photoluminescence with Bayesian Inference</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.013001</link><description>Author(s): Manuel Kober-Czerny, Akash Dasgupta, Seongrok Seo, Florine M. Rombach, David P. McMeekin, Heon Jin, and Henry J. Snaith&lt;br /&gt;&lt;p&gt;Experiment and machine learning are combined to extract key material parameters and insight into charge carrier transport in metal halide perovskites for solar cell applications.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.013001.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 013001] Published Tue Jan 14, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Tue, 14 Jan 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.013001</guid></item><item><title>[Recent Articles in PRX Energy] Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.3.023006</link><description>Author(s): Hengrui Zhang (张恒睿), Tianxing Lai (来天行), Jie Chen, Arumugam Manthiram, James M. Rondinelli, and Wei Chen&lt;br /&gt;&lt;p&gt;MolSets, a machine learning model that integrates graph neural networks with permutation invariant architecture, addresses multilevel complexity for effective prediction of molecular mixture properties, thus accelerating lithium battery electrolyte design.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.3.023006.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 3, 023006] Published Wed Jun 12, 2024</description><author>Recent Articles in PRX Energy</author><pubDate>Wed, 12 Jun 2024 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.3.023006</guid></item><item><title>[Recent Articles in PRX Energy] Temperature Impact on Lithium Metal Morphology in Lithium Reservoir-Free Solid-State Batteries</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.3.023003</link><description>Author(s): Min-Gi Jeong, Kelsey B. Hatzell, Sourim Banerjee, Bairav S. Vishnugopi, and Partha P. Mukherjee&lt;br /&gt;&lt;p&gt;Toward enhancing performance in reservoir-free solid-state batteries, confocal imaging experiments are combined with meso-scale modeling to unveil vertical and horizontal growth mechanisms at varying temperatures of lithium metal at an agyrodite solid electrolyte|stainless steel interface.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.3.023003.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 3, 023003] Published Fri May 17, 2024</description><author>Recent Articles in PRX Energy</author><pubDate>Fri, 17 May 2024 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.3.023003</guid></item><item><title>[Recent Articles in Rev. Mod. Phys.] &lt;i&gt;Colloquium&lt;/i&gt;: Advances in automation of quantum dot devices control</title><link>http://link.aps.org/doi/10.1103/RevModPhys.95.011006</link><description>Author(s): Justyna P. Zwolak and Jacob M. Taylor&lt;br /&gt;&lt;p&gt;A promising platform for quantum computing consists of arrays of quantum dots. However, operating these devices presents a challenging control problem, since the location of the dots and the charges they contain must be reliably and reproducibly matched with the gate voltages. This Colloquium explains how automated control protocols that make use of machine learning techniques can succeed in systems where heuristic control is not feasible.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/RMP/key_images/10.1103/RevModPhys.95.011006.png" width="200" /&gt;&lt;br /&gt;[Rev. Mod. Phys. 95, 011006] Published Fri Feb 17, 2023</description><author>Recent Articles in Rev. Mod. Phys.</author><pubDate>Fri, 17 Feb 2023 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/RevModPhys.95.011006</guid></item><item><title>[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Hydrogen as promoter and inhibitor of superionicity: A case study on Li-N-H systems</title><link>http://link.aps.org/doi/10.1103/PhysRevB.82.024304</link><description>Author(s): Andreas Blomqvist, C. Moysés Araújo, Ralph H. Scheicher, Pornjuk Srepusharawoot, Wen Li, Ping Chen, and Rajeev Ahuja&lt;br /&gt;&lt;p&gt;Materials which possess a high lithium ion conductivity are very attractive for battery and fuel cell applications. Hydrogenation of the fast-ion conductor lithium nitride $({\text{Li}}_{3}\text{N})$ leads to the formation of lithium imide $({\text{Li}}_{2}\text{NH})$ and subsequently of lithium ami…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 82, 024304] Published Mon Jul 26, 2010</description><author>PRB: Dynamics, dynamical systems, lattice effects, quantum solids</author><pubDate>Mon, 26 Jul 2010 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PhysRevB.82.024304</guid></item><item><title>[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Nonadiabatic effects of rattling phonons and $4f$ excitations in $\text{Pr}{({\text{Os}}_{1−x}{\text{Ru}}_{x})}_{4}{\text{Sb}}_{12}$</title><link>http://link.aps.org/doi/10.1103/PhysRevB.81.224305</link><description>Author(s): Peter Thalmeier&lt;br /&gt;&lt;p&gt;In the skutterudite compounds the anharmonic “rattling” oscillations of $4f$-guest ions in the surrounding ${\text{Sb}}_{12}$ host cages are found to have significant influence on the low-temperature properties. Recently specific-heat analysis of $\text{Pr}{({\text{Os}}_{1−x}{\text{Ru}}_{x})}_{4}{\t…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 81, 224305] Published Fri Jun 18, 2010</description><author>PRB: Dynamics, dynamical systems, lattice effects, quantum solids</author><pubDate>Fri, 18 Jun 2010 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PhysRevB.81.224305</guid></item><item><title>[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Ionic conductivity of nanocrystalline yttria-stabilized zirconia: Grain boundary and size effects</title><link>http://link.aps.org/doi/10.1103/PhysRevB.81.184301</link><description>Author(s): O. J. Durá, M. A. López de la Torre, L. Vázquez, J. Chaboy, R. Boada, A. Rivera-Calzada, J. Santamaria, and C. Leon&lt;br /&gt;&lt;p&gt;We report on the effect of grain size on the ionic conductivity of yttria-stabilized zirconia samples synthesized by ball milling. Complex impedance measurements, as a function of temperature and frequency are performed on $10\text{ }\text{mol}\text{ }\mathrm{%}$ yttria-stabilized zirconia nanocryst…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 81, 184301] Published Mon May 10, 2010</description><author>PRB: Dynamics, dynamical systems, lattice effects, quantum solids</author><pubDate>Mon, 10 May 2010 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PhysRevB.81.184301</guid></item><item><title>[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Calculating the anharmonic free energy from first principles</title><link>http://link.aps.org/doi/10.1103/PhysRevB.81.172301</link><description>Author(s): Zhongqing Wu&lt;br /&gt;&lt;p&gt;We developed a method to calculate the anharmonic free energy without requiring any adjustable parameter. The requisite computations are first-principles quasiharmonic calculations plus an additional Canonical (NVT) ensemble first-principles molecular-dynamics simulation and, therefore, are affordab…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 81, 172301] Published Fri May 07, 2010</description><author>PRB: Dynamics, dynamical systems, lattice effects, quantum solids</author><pubDate>Fri, 07 May 2010 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PhysRevB.81.172301</guid></item><item><title>[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Phason dynamics in one-dimensional lattices</title><link>http://link.aps.org/doi/10.1103/PhysRevB.81.064302</link><description>Author(s): Hansjörg Lipp, Michael Engel, Steffen Sonntag, and Hans-Rainer Trebin&lt;br /&gt;&lt;p&gt;In quasicrystals, the phason degree of freedom and the inherent anharmonic potentials lead to complex dynamics, which cannot be described by the usual phonon modes of motion. We have constructed simple one-dimensional model systems, the dynamic Fibonacci chain, and approximants thereof. They allow u…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 81, 064302] Published Thu Feb 25, 2010</description><author>PRB: Dynamics, dynamical systems, lattice effects, quantum solids</author><pubDate>Thu, 25 Feb 2010 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PhysRevB.81.064302</guid></item><item><title>[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] &lt;i&gt;Ab initio&lt;/i&gt; construction of interatomic potentials for uranium dioxide across all interatomic distances</title><link>http://link.aps.org/doi/10.1103/PhysRevB.80.174302</link><description>Author(s): P. Tiwary, A. van de Walle, and N. Grønbech-Jensen&lt;br /&gt;&lt;p&gt;We provide a methodology for generating interatomic potentials for use in classical molecular-dynamics simulations of atomistic phenomena occurring at energy scales ranging from lattice vibrations to crystal defects to high-energy collisions. A rigorous method to objectively determine the shape of a…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 80, 174302] Published Wed Nov 25, 2009</description><author>PRB: Dynamics, dynamical systems, lattice effects, quantum solids</author><pubDate>Wed, 25 Nov 2009 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PhysRevB.80.174302</guid></item><item><title>[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] One-dimensional nanostructure-guided chain reactions: Harmonic and anharmonic interactions</title><link>http://link.aps.org/doi/10.1103/PhysRevB.80.174301</link><description>Author(s): Nitish Nair and Michael S. Strano&lt;br /&gt;&lt;p&gt;We have performed a parametric study of self-propagating chain reactions along a one-dimensional bead-spring array. The coupling between beads is modeled using harmonic and anharmonic Fermi-Pasta-Ulam (FPU)-$β$ and ${φ}^{4}$ potentials. The parameters that define the system are the activation energy…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 80, 174301] Published Fri Nov 13, 2009</description><author>PRB: Dynamics, dynamical systems, lattice effects, quantum solids</author><pubDate>Fri, 13 Nov 2009 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PhysRevB.80.174301</guid></item></channel></rss>
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