diff --git a/filtered_feed.xml b/filtered_feed.xml
new file mode 100644
index 0000000..69ce774
--- /dev/null
+++ b/filtered_feed.xml
@@ -0,0 +1,24 @@
+<?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 02:34:39 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[ScienceDirect Publication: Journal of 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 02:34:38 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 02:34:38 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 02:34:38 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725006852</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] First-principles design of YGaTe&lt;sub&gt;3&lt;/sub&gt; for bidirectional catalysis and shuttle suppression in lithium–sulfur batteries</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725007043?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): Qingyi Feng, Caizheng Wang, Bo Li, Sizhao Huang, Sean Li, Zhen Wang, Xia Xiang, Yangfang Li, Qiuquan Guo, Peipei Jia, Jun Yang&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Tue, 23 Dec 2025 02:34:38 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725007043</guid></item><item><title>[ScienceDirect Publication: Acta Materialia] Revealing the three-dimensional morphology and formation process of Fe-contained intermetallic compounds in aluminum alloys: A combined first-principles, phase field, and FIB-SEM tomography study</title><link>https://www.sciencedirect.com/science/article/pii/S135964542501122X?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 17 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Acta Materialia&lt;/p&gt;&lt;p&gt;Author(s): Jiale Ma, Yanli Zhang, Qing Peng, Qingyan Xu, Haidong Zhao, Zhiqiang Han&lt;/p&gt;</description><author>ScienceDirect Publication: Acta Materialia</author><pubDate>Tue, 23 Dec 2025 02:34:38 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S135964542501122X</guid></item><item><title>[ScienceDirect Publication: Acta Materialia] Effect of Ag on the precipitation stability in Al-Mg-Si-Ag alloy: First-principles calculations, Calphad modeling and experimental validation</title><link>https://www.sciencedirect.com/science/article/pii/S1359645425010419?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): Wei Shao, Witold Chrominski, Mark Fedorov, Michał Kanios, Chenying Shi, Javier LLorca, Jan S. Wróbel&lt;/p&gt;</description><author>ScienceDirect Publication: Acta Materialia</author><pubDate>Tue, 23 Dec 2025 02:34:38 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1359645425010419</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. Kekung, Ryan T. Ott, Sougata Roy, Prashant Singh&lt;/p&gt;</description><author>ScienceDirect Publication: Acta Materialia</author><pubDate>Tue, 23 Dec 2025 02:34:38 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S135964542501050X</guid></item><item><title>[ScienceDirect Publication: Acta Materialia] Modeling the equilibrium vacancy concentration in multi-principal element alloys from first-principles</title><link>https://www.sciencedirect.com/science/article/pii/S1359645425010390?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): Damien K.J. Lee, Yann L. Müller, Anirudh Raju Natarajan&lt;/p&gt;</description><author>ScienceDirect Publication: Acta Materialia</author><pubDate>Tue, 23 Dec 2025 02:34:38 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1359645425010390</guid></item><item><title>[ScienceDirect Publication: Acta Materialia] Experimental and first-principles insights into Ti-mediated Cu–Si&lt;sub&gt;3&lt;/sub&gt;N&lt;sub&gt;4&lt;/sub&gt; interfaces for high-reliability electronic substrates</title><link>https://www.sciencedirect.com/science/article/pii/S1359645425011000?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): Hiroaki Tatsumi, Shunya Nitta, Atsushi M. Ito, Arimichi Takayama, Makoto Takahashi, Seongjae Moon, Eiki Tsushima, Hiroshi Nishikawa&lt;/p&gt;</description><author>ScienceDirect Publication: Acta Materialia</author><pubDate>Tue, 23 Dec 2025 02:34:38 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1359645425011000</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 02:34:38 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 02:34:26 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 02:34:26 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 02:34:26 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352847825000565</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 02:34:25 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 02:34:25 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 02:34:25 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 02:34:25 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 02:34:25 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25046298</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] First-principles study of Y&lt;sub&gt;2&lt;/sub&gt;CO&lt;sub&gt;2&lt;/sub&gt;, an O-functionalized two-dimensional electrode material for metal-ion batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25046079?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): Guangmin Yang, Mai Xiao, Xinlin Yang, Sen Xu, Jianyan Lin&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 23 Dec 2025 02:34:25 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25046079</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 02:34:25 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 02:34:24 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 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0167273825003224</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Bayesian prior construction for uncertainty quantification in first-principles statistical mechanics</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625006731?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): Derick E. Ober, Sesha Sai Behara, Anton Van der Ven&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625006731</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 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007025</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] First-principles study of efficient photocatalytic water splitting in XMoSiN&lt;sub&gt;2&lt;/sub&gt;/XWSiN&lt;sub&gt;2&lt;/sub&gt; (X = Se; Te) heterojunctions</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625006391?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): Bikang Lin, Gangyuan Yu, Runhong Zou, Luyu Zhou, Sili Huang, Guolin Qian, Quan Xie&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625006391</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 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007037</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] First-principles calculations on the adhesion strength, interfacial bonding and electronic structure of Fe(111)/NiAl(111) interface</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007074?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): Xiaohui Wang, Zhenbao Liu, Yonghua Duan, Xinquan Zhang, Wen Han, Chen Li, Zhiyong Yang, Jianxiong Liang, Zhengjie Duan, Ancang Yang&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007074</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] First-principles insights into the optical response of the MOF-5 metal–organic framework for gas sensing applications</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625006949?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): Pedro H. Souza, Walter Orellana&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625006949</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 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625006883</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] First-principles calculation on directly bonded aluminum/graphene interface: Atomic matching, interface binding energy, and electron transfer</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625006937?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): Fangzheng Shi, Qingyu Shi, Yijun Liu, Gong Zhang, Mengran Zhou, Gaoqiang Chen&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625006937</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 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007207</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Clarification of the effects of carbon and nitrogen addition on the stacking fault energy in Co-X binary alloys (X = Cr, W, and Ni) by first-principles calculations</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007177?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): Tomoki Wada, Kai Hiyama, Ryoji Sahara, Kyosuke Ueda, Takayuki Narushima&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007177</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] First-principles investigation on neutral oxygen vacancies and hydrogen passivation mechanism in amorphous silica</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007189?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): Yuqi Wang, Zhongcun Chen, Yaolin Zhao, Chenxi Yu, Jun Zhang&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007189</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Studying the effects of vacancy structures on helium trapping and desorption in TiC by first-principles methods</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007281?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): ShuLong Wen, ZhaoHong Luo, Gang Wang, Yan Yang, HuiQiu Deng&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007281</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 02:34:24 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 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007384</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Structural and electronic transformations of lithium selenide during delithiation: A density functional theory study</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007359?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): Maeng-Eun Lee, Kwang-Hwi Cho&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007359</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. Fellman, J. Byggmästar, F. Granberg, F. Djurabekova, K. Nordlund&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007414</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Application of artificial neural networks for predicting viscoelastic properties of short-fiber reinforced thermoplastics</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007529?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): Sally Rüschendorf, Alexander Kriwet, Fabian Urban, Kai-Uwe Schröder&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007529</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] First-principles study of hydrogen bridge defects at amorphous-SiO&lt;sub&gt;2&lt;/sub&gt;/Si (100) interface: Structural dynamics and non-radiative carrier capture</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007347?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): Di Qi, Hong Luo, Zhaoqing Ye, Jiangtao Cao, Hang Zhou, Yang Liu, Xu Zuo&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007347</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Structure-driven prediction and mechanism insights into piezoelectric performance of potassium sodium niobate via interpretable machine learning</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007530?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 263&lt;/p&gt;&lt;p&gt;Author(s): Hui Li, WenKe Lu, Chunlei Li, Jinyi Liu, Zihui Feng, Jiaming Liu, Lan Yang&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007530</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Band structure modulation of germanium under arbitrary strain directions: A combined approach of strain matrix theory, first-principles calculation and machine learning</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007694?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 263&lt;/p&gt;&lt;p&gt;Author(s): Hai Wang, Wenqi Huang, Mengjiang Jia&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007694</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] High-pressure behavior of energetic metal-organic frameworks: A first-principles study</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007839?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 263&lt;/p&gt;&lt;p&gt;Author(s): Mustafa Erkartal&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007839</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Revealing the dynamic evolution of the electronic structure at the Ti&lt;sub&gt;2&lt;/sub&gt;AlC/TiAl interface during tensile deformation by the first-principles calculations</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007876?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 263&lt;/p&gt;&lt;p&gt;Author(s): Hongwu Gao, Hangyu Yue, Yinwen Mao, Rengeng Li&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007876</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Machine learning-driven prognostication of multifunctional properties of (K,Na)NbO&lt;sub&gt;3&lt;/sub&gt;-based lead-free ceramics for optimized materials design</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007803?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 263&lt;/p&gt;&lt;p&gt;Author(s): Manisha Kumari, Alok Shukla&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007803</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Interfacial bonding mechanisms in TiB&lt;sub&gt;2&lt;/sub&gt;/Ti systems fabricated by molten salt electrophoretic deposition: Experimental and first-principles calculation</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007888?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 263&lt;/p&gt;&lt;p&gt;Author(s): Peipei Liu, Chunchun Dong, Xiping Chen, Xuemin Liang&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007888</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Machine learning for domain transfer between simulated and experimental 2D X-ray diffraction patterns using generative adversarial networks</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007724?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 263&lt;/p&gt;&lt;p&gt;Author(s): Samantha J. Brozak, David Montes de Oca Zapiain, Brendan Donohoe, Tommy Ao, Nathan P. Brown, Marcus D. Knudson, Carianne Martinez, J. Matthew D. Lane&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007724</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Machine learning interatomic potentials for monolayer hexagonal boron nitride: Thermal transport and defect effects via deep potential molecular dynamics</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007955?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 263&lt;/p&gt;&lt;p&gt;Author(s): Guanghao Zhang, Zicheng Wang, Caihua Shi, Ye Han&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007955</guid></item><item><title>[ScienceDirect Publication: Artificial Intelligence Chemistry] Machine Learning (ML)-driven quantitative structure-pharmacokinetic relationship (QSPKR) modeling of the tissue-to-plasma partition coefficient (K&lt;sub&gt;p&lt;/sub&gt;) of drugs across different tissues</title><link>https://www.sciencedirect.com/science/article/pii/S2949747725000107?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; Artificial Intelligence Chemistry, Volume 3, Issue 2&lt;/p&gt;&lt;p&gt;Author(s): Souvik Pore, Kunal Roy&lt;/p&gt;</description><author>ScienceDirect Publication: Artificial Intelligence Chemistry</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2949747725000107</guid></item><item><title>[ScienceDirect Publication: Artificial Intelligence Chemistry] Machine learning prediction of pKa of organic acids</title><link>https://www.sciencedirect.com/science/article/pii/S2949747725000090?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; Artificial Intelligence Chemistry, Volume 3, Issue 2&lt;/p&gt;&lt;p&gt;Author(s): Juda Baikété, Alhadji Malloum, Jeanet Conradie&lt;/p&gt;</description><author>ScienceDirect Publication: Artificial Intelligence Chemistry</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2949747725000090</guid></item><item><title>[ScienceDirect Publication: Artificial Intelligence Chemistry] GraphSLA: Graph machine learning for predicting small molecule - lncRNA associations</title><link>https://www.sciencedirect.com/science/article/pii/S2949747725000119?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; Artificial Intelligence Chemistry, Volume 3, Issue 2&lt;/p&gt;&lt;p&gt;Author(s): Ashish Panghalia, Parth Kumar, Vikram Singh&lt;/p&gt;</description><author>ScienceDirect Publication: Artificial Intelligence Chemistry</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2949747725000119</guid></item><item><title>[ScienceDirect Publication: Artificial Intelligence Chemistry] Machine learning-guided synthesis of prospective organic molecular materials: An algorithm with latent variables for understanding and predicting experimentally unobservable reactions</title><link>https://www.sciencedirect.com/science/article/pii/S2949747725000132?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; Artificial Intelligence Chemistry, Volume 3, Issue 2&lt;/p&gt;&lt;p&gt;Author(s): Kazuhiro Takeda, Naoya Ohtsuka, Toshiyasu Suzuki, Norie Momiyama&lt;/p&gt;</description><author>ScienceDirect Publication: Artificial Intelligence Chemistry</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2949747725000132</guid></item><item><title>[ScienceDirect Publication: Artificial Intelligence Chemistry] A method for predicting molecular point group based on graph neural networks</title><link>https://www.sciencedirect.com/science/article/pii/S2949747725000144?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; Artificial Intelligence Chemistry, Volume 3, Issue 2&lt;/p&gt;&lt;p&gt;Author(s): Siyuan Zeng, Kuanping Gong, Yongquan Jiang, Yan Yang&lt;/p&gt;</description><author>ScienceDirect Publication: Artificial Intelligence Chemistry</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2949747725000144</guid></item><item><title>[ScienceDirect Publication: Artificial Intelligence Chemistry] Neural networks for neurocomputing circuits: A computational study of tolerance to noise and activation function non-uniformity when machine learning materials properties</title><link>https://www.sciencedirect.com/science/article/pii/S2949747725000168?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; Artificial Intelligence Chemistry, Volume 3, Issue 2&lt;/p&gt;&lt;p&gt;Author(s): Ye min Thant, Methawee Nukunudompanich, Chu-Chen Chueh, Manabu Ihara, Sergei Manzhos&lt;/p&gt;</description><author>ScienceDirect Publication: Artificial Intelligence Chemistry</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2949747725000168</guid></item><item><title>[ScienceDirect Publication: Artificial Intelligence Chemistry] Electrostatic map for the hydration-induced frequency shifts of the NH stretching mode of peptide constructed using some machine learning models</title><link>https://www.sciencedirect.com/science/article/pii/S2949747725000156?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; Artificial Intelligence Chemistry, Volume 3, Issue 2&lt;/p&gt;&lt;p&gt;Author(s): Hajime Torii, Mikito Tsujimoto, Yukichi Kitamura&lt;/p&gt;</description><author>ScienceDirect Publication: Artificial Intelligence Chemistry</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2949747725000156</guid></item><item><title>[ScienceDirect Publication: Artificial Intelligence Chemistry] Comparative study of machine learning methods for accurate prediction of logP and pKb</title><link>https://www.sciencedirect.com/science/article/pii/S2949747725000181?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: June 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Artificial Intelligence Chemistry, Volume 4, Issue 1&lt;/p&gt;&lt;p&gt;Author(s): Juda Baikété, Alhadji Malloum, Jeanet Conradie&lt;/p&gt;</description><author>ScienceDirect Publication: Artificial Intelligence Chemistry</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2949747725000181</guid></item><item><title>[ScienceDirect Publication: Artificial Intelligence Chemistry] Statistical comparison and uncertainty analysis of graph neural networks and machine learning models for molecular property prediction in drug discovery</title><link>https://www.sciencedirect.com/science/article/pii/S294974772500020X?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: June 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Artificial Intelligence Chemistry, Volume 4, Issue 1&lt;/p&gt;&lt;p&gt;Author(s): Sarmad Waleed, Shams ul Islam, Muhammad Saleem, Ali Ahmed&lt;/p&gt;</description><author>ScienceDirect Publication: Artificial Intelligence Chemistry</author><pubDate>Tue, 23 Dec 2025 02:34:24 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S294974772500020X</guid></item><item><title>[ScienceDirect Publication: eScience] &lt;em&gt;In situ&lt;/em&gt; engineered organic–inorganic bilayer solid electrolyte interphase enabling lithium metal batteries over 15,000-hour lifespan</title><link>https://www.sciencedirect.com/science/article/pii/S2667141725001090?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 27 September 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; eScience&lt;/p&gt;&lt;p&gt;Author(s): Bin Zhang, Ke Yue, Ke Wang, Guohao Zhao, Wenbin Huang, Tao Li, Jianmin Luo, Ying Xu, Xinyong Tao&lt;/p&gt;</description><author>ScienceDirect Publication: eScience</author><pubDate>Tue, 23 Dec 2025 02:34:22 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2667141725001090</guid></item><item><title>[ScienceDirect Publication: eScience] Machine learning-driven discovery of high-performance Co/Sr-free air electrodes for protonic ceramic electrolysis cells</title><link>https://www.sciencedirect.com/science/article/pii/S2667141725001168?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 24 October 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; eScience&lt;/p&gt;&lt;p&gt;Author(s): Ning Wang, Qingwen Su, Huanxin Xiang, Biyao Zhou, Jiaxin Huang, Fangyuan Zheng, Ruijie Zhu, Yoshitaka Aoki, Ling Meng, Feng Jiao, Baoyin Yuan, Chunmei Tang, Siyu Ye&lt;/p&gt;</description><author>ScienceDirect Publication: eScience</author><pubDate>Tue, 23 Dec 2025 02:34:22 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2667141725001168</guid></item><item><title>[ScienceDirect Publication: Science Bulletin] Machine learning-guided design of high-performance Mg-based thermoelectrics: insights into thermal expansion effects</title><link>https://www.sciencedirect.com/science/article/pii/S2095927325008163?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 30 November 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Science Bulletin, Volume 70, Issue 22&lt;/p&gt;&lt;p&gt;Author(s): Da Wan, Shulin Bai, Sirui Fan, Bixuan Li, Xiaoya Huang, Hongyuan Zhao, Ziqi Wang, Zhen Li, Yu Liu, Peng Kang, Lei Zheng, Li-Dong Zhao, Huibin Xu&lt;/p&gt;</description><author>ScienceDirect Publication: Science Bulletin</author><pubDate>Tue, 23 Dec 2025 02:34:22 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2095927325008163</guid></item><item><title>[ScienceDirect Publication: Science Bulletin] Reconfigurable bipolar and nonlinear photoresponse in 2D VSe&lt;sub&gt;2&lt;/sub&gt;/WSe&lt;sub&gt;2&lt;/sub&gt; photodetectors for high-performance optical neural networks</title><link>https://www.sciencedirect.com/science/article/pii/S2095927325012575?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 15 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Science Bulletin&lt;/p&gt;&lt;p&gt;Author(s): Zhengui Zhao, Yuan Cheng, Jiacheng Sun, Pengyu Zhang, Tonglu Wang, Jianshi Tang, Yuyan Wang, Junying Zhang&lt;/p&gt;</description><author>ScienceDirect Publication: Science Bulletin</author><pubDate>Tue, 23 Dec 2025 02:34:22 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2095927325012575</guid></item><item><title>[ScienceDirect Publication: Progress in Materials Science] The role of protein content in body fluids in magnesium alloy bioimplant degradation: A machine learning approach</title><link>https://www.sciencedirect.com/science/article/pii/S0079642525002166?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: April 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Progress in Materials Science, Volume 158&lt;/p&gt;&lt;p&gt;Author(s): M.N. Bharath, R.K. Singh Raman, Alankar Alankar&lt;/p&gt;</description><author>ScienceDirect Publication: Progress in Materials Science</author><pubDate>Tue, 23 Dec 2025 02:34:20 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0079642525002166</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Deformation mechanisms of multiphase AlCoCuFeNiTi high-entropy alloys revealed by machine learning-assisted molecular dynamics</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325002561?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): Yu-Sheng Lu, Kenta Yamanaka, Hiroya Ishii, Thi-Xuyen Bui, Yi-Ju Hsieh, Te-Hua Fang&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 02:34:18 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325002561</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Modeling strategies for hydrogen reduction of high-purity metals: From DFT to ReaxFF and machine learning</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325002597?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): Zhimeng Shao, Bowen Gao, Zhifang Hu, Honglin Jiang, Qidong Zhang, Zhihe Dou, Yanxi Yin&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 02:34:18 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325002597</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Pressure-induced superconductivity in quaternary &lt;em&gt;MX&lt;/em&gt;&lt;sub&gt;3&lt;/sub&gt;B&lt;sub&gt;4&lt;/sub&gt;H&lt;sub&gt;20&lt;/sub&gt; compounds: A first-principles study</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325002779?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): Guo-Yong Shi, Tian-Tian Liu, Xin Li, Rui-Tong Xiao, Hong Jiang, Yue-Hua Su, Song-you Wang, Wan-Sheng Su, Chao Zhang&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 02:34:18 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325002779</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Physics-informed neural networks with hard-encoded angle-dependent boundary conditions for phonon Boltzmann transport equation</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325002780?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): Jiahang Zhou, Ruiyang Li, Wenjie Shang, Yi Liu, J.P. 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 02:34:18 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 02:34:18 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 02:34:18 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 02:34:18 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 02:34:18 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 02:34:18 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 02:34:18 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S254252932500327X</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Density functional theory calculations of the bandstructure of cubic boron arsenide</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325003189?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): A. King, R. Gillen, G. Burwell, B.A. Niyikiza, F.J. Pan, Z.F. Ren, L. Li, K. Kalna&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 02:34:18 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325003189</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 02:34:18 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325003359</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Comprehensive insights into intrinsic thermal conduction mechanism of Sn-Bi-In alloys toward high-performance thermal interface materials: experimental and first-principles calculations</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325003347?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): Xuechi Wang, Xiaoliang Ji, Yishu Wang, Hao Yuan, Dan Li, Fu Guo&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 02:34:18 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325003347</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 02:34:18 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 02:34:18 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 02:34:14 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 02:34:14 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009292</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 02:34:14 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 02:34:14 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009620</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 02:34:14 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 02:34:14 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 02:34:14 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 02:34:14 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 02:34:14 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2590238525004114</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 02:34:13 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125003563</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 02:34:13 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125004143</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>[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>[Recent Articles in Phys. Rev. B] Giant magnetostriction by design: A first-principles screening of Co-based Heusler alloys</title><link>http://link.aps.org/doi/10.1103/ngfz-qh5k</link><description>Author(s): Pengju Wu, Jie Du, Liang Yao, Hang Li, Xiaodong Zhou, Tao Zhu, and Wenhong Wang&lt;br /&gt;&lt;p&gt;The pursuit of high-performance, rare-earth-free magnetostrictive materials is crucial for advancing technologies in sensing, actuation, and microelectromechanical systems. Heusler alloys represent a promising, yet underexplored, class of materials for this purpose. In this work, we perform a system…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 112, 214446] Published Mon Dec 22, 2025</description><author>Recent Articles in Phys. Rev. B</author><pubDate>Mon, 22 Dec 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/ngfz-qh5k</guid></item><item><title>[cond-mat updates on arXiv.org] Superconductivity in YRu3B2 and LuRu3B2</title><link>https://arxiv.org/abs/2512.16945</link><description>arXiv:2512.16945v1 Announce Type: new 
+Abstract: We report the experimental discovery of bulk superconductivity in two kagome lattice compounds, YRu$_3$B$_2$ and LuRu$_3$B$_2$, which were predicted through machine learning-accelerated high-throughput screening combined with first principles calculations. These materials crystallize in the hexagonal CeCo$_3$B$_2$-type structure with planar kagome networks formed by Ru atoms. We observe superconducting critical temperatures of $T_{c} = 0.81$~K for YRu$_3$B$_2$ and $T_{c} = 0.95$~K for LuRu$_3$B$_2$, confirmed through magnetization and specific heat measurements. Both compounds exhibit nearly 100\% superconducting volume fractions, demonstrating bulk superconductivity. Compared with LaRu$_3$Si$_2$, YRu$_3$B$_2$ and LuRu$_3$B$_2$ show a more dispersive Ru local $d_{x^2-y^2}$ quasi-flat band (and thus a reduced DOS at $E_F$) together with an overall hardening of the phonon spectrum, both of which lower the electron-phonon coupling (EPC) constant $\lambda$. Meanwhile, the dominant real-space EPC between Ru local $d_{x^2-y^2}$ states and the low-frequency Ru in-plane local $x$ branch remains nearly unchanged, indicating that the reduction of $\lambda$ originates from the $d_{x^2-y^2}$ DOS reduction and the overall phonon hardening. Superfluid weight calculations show that conventional contributions dominate over quantum geometric effects due to the dispersive nature of bands near the Fermi level. This work demonstrates the effectiveness of integrating machine learning screening, first principles theory, and experimental synthesis for accelerating the discovery of new superconducting materials.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 22 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.16945v1</guid></item><item><title>[cond-mat updates on arXiv.org] Single-$q$ Cycloid and Double-$q$ Vortex Lattices in Layered Magnetic Semimetal EuAg$_4$Sb$_2$</title><link>https://arxiv.org/abs/2512.16990</link><description>arXiv:2512.16990v1 Announce Type: new 
+Abstract: Recently, a host of exotic magnetic textures such as topologically protected skyrmion lattices has been discovered in several bulk metallic lanthanide compounds. In addition to hosting skyrmion phases, a hallmark of this class of materials is the appearance of numerous spin textures characterized by a superposition of multi-$q$ magnetic modulations: spin moir\'{e} superlattices. The nuanced energy landscape thus motivates detailed studies to understand the underlying interactions. Here, we comprehensively characterize and model the three zero-field magnetic textures present in one such material, EuAg$_4$Sb$_2$. Systematic symmetry breaking experiments using magnetic field and strain determine that the ground state incommensurate magnetic phase (ICM1) is single-$q$. In contrast, ICM2 and ICM3 are both double-$q$, \textit{i.e.}, spin moir\'{e} superlattices. Further, through application of polarized small angle neutron scattering and spherical neutron polarimetry, we demonstrate that ICM1 is a single-$q$ cycloid and ICM2 and ICM3 are double-$q$ vortex lattices, with Eu moments lying in the $ab$-plane in zero field and with a ferromagnetic component at finite field. Despite the quasi-2D nature of EuAg$_4$Sb$_2$, the modulations propagate out of the \textit{ab}-plane, leading to a shift of the spin texture between triangle lattice planes. Further, the ICM3 to ICM2 transition includes an unusual 45$^\circ$ rotation of the magnetic vortex lattice. Motivated by the coexistence of such drastically different phases in this compound, we conclude by developing a phenomenological model to understand the stability of these states. Our experimental probes and theoretical modeling definitively characterize three different and tunable phases in one material, and provide insight for the design of new topological spin-texture materials.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 22 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.16990v1</guid></item><item><title>[cond-mat updates on arXiv.org] Real-space Atomic Dynamics in Liquid Gallium Studied by Inelastic Neutron Scattering</title><link>https://arxiv.org/abs/2512.17018</link><description>arXiv:2512.17018v1 Announce Type: new 
+Abstract: Gallium is a prototypical liquid metal and has gained renewed attention due to its unique properties. Characterizing and elucidating its atomic dynamics remains elusive despite numerous studies, primarily due to the challenges of quantifying atomic-scale dynamics in liquids. Recent developments in inelastic neutron scattering enable us to measure the Van Hove correlation function that describes the real-space motion of liquid atoms. In this work, we use this approach to reveal the dynamics in gallium liquids and find the co-existence of two dynamical medium-range orders (MROs), which have a dynamical behavior distinct from that of the short-range order (SRO). We propose that these MROs are driven by global forces in the form of two density waves, as a direct consequence of the underlying competition between ionic core repulsion and valence electron cohesion. We suggest that the density wave approach is not only applicable to other metallic liquids exhibiting similar structural anomalies, but also offers a promising direction for elucidating the dynamics of complex liquids and glasses by linking electronic-state fluctuations to atomic dynamics.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 22 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.17018v1</guid></item><item><title>[cond-mat updates on arXiv.org] Tunable Electronic Transport in Pd$_3$O$_2$Cl$_2$ Kagome Bilayers: Interplay of Stacking Configuration and Strain</title><link>https://arxiv.org/abs/2512.17069</link><description>arXiv:2512.17069v1 Announce Type: new 
+Abstract: Kagome lattice bilayers offer unique opportunities for engineering electronic properties through interlayer stacking and strain. We report a comprehensive first-principles study of Pd$_3$O$_2$Cl$_2$ kagome bilayers, examining four stacking configurations (AA, AA$'$, AB, AB$'$). Our calculations reveal dramatic stacking-dependent band gap modulation from 0.08 to 0.76~eV, with the AB$'$ configuration being the most thermodynamically stable. All stackings exhibit robust mechanical stability with Young's moduli of 54.82-61.97~N/m and ductile behavior suitable for flexible electronics. Carrier effective masses show significant stacking dependence, ranging from 2.39-6.35~$m_0$ for electrons and 0.67-1.55~$m_0$ for holes. Strain engineering of the AB$'$ bilayer demonstrates non-monotonic band gap tuning and asymmetric modulation of carrier masses, with hole effective masses showing stronger strain sensitivity. These results establish Pd$_3$O$_2$Cl$_2$ bilayers as a promising platform for strain-engineered kagome-based quantum devices, where stacking order and mechanical deformation provide complementary control over electronic transport.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 22 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.17069v1</guid></item><item><title>[cond-mat updates on arXiv.org] A Three-Dimensional Dodecaphenylyne-Derived Carbon Allotrope with Anisotropic and Auxetic-Like Mechanical Behavior</title><link>https://arxiv.org/abs/2512.17105</link><description>arXiv:2512.17105v1 Announce Type: new 
+Abstract: We introduce 3D-DPhyne, a novel three-dimensional (3D) carbon allotrope derived from the dodecaphenylyne framework, and investigate its structural, electronic, optical, and mechanical properties using first-principles calculations. The proposed structure forms a tetragonal, topologically complex network of four-, six-, and twelve-membered carbon rings with mixed sp/sp^2 hybridization and a formation energy of -7.87 eV/atom, comparable to other stable carbon allotropes. Phonon dispersion calculations show no imaginary modes, and ab initio molecular dynamics simulations at 1000~K confirm robust thermal stability without bond breaking. Electronic structure analysis reveals metallic character, with multiple bands crossing the Fermi level and dominant contributions from carbon p orbitals, consistent with a fully delocalized 3D $\pi$-conjugated network. The optical response is anisotropic, exhibiting strong absorption in the visible and ultraviolet regions and low reflectivity across a broad range of photon energies. Mechanical analysis reveals pronounced elastic anisotropy, with Young's modulus varying from approximately 40 to 490 GPa depending on direction. Poisson's ratio displays unconventional directional behavior, including auxetic-like responses.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 22 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.17105v1</guid></item><item><title>[cond-mat updates on arXiv.org] Machine Learning Assisted Parameter Tuning on Wavelet Transform Amorphous Radial Distribution Function</title><link>https://arxiv.org/abs/2512.17245</link><description>arXiv:2512.17245v1 Announce Type: new 
+Abstract: Understanding atomic structures is crucial, yet amorphous materials remain challenging due to their irregular and non-periodic nature. The wavelet-transform radial distribution function (WT-RDF) offers a physics-based framework for analyzing amorphous structures, reliably predicting the first and second RDF peaks and overall curve trends in both binary Ge 0.25 Se 0.75 and ternary Ag x(Ge 0.25 Se 0.75)100-x (x=5,10,15,20,25) systems. Despite these strengths, WT-RDF shows limitations in amplitude accuracy, which affects quantitative analyses such as coordination numbers. This study addresses the issue by optimizing WT-RDF parameters using a machine learning approach, producing the enhanced WT-RDF+ framework. WT-RDF+ improves the precision of peak predictions and outperforms benchmark ML models, including RBF and LSTM, even when trained on only 25 percent of the binary dataset. These results demonstrate that WT-RDF+ is a robust and reliable model for structural characterization of amorphous materials, particularly Ge-Se systems, and support the efficient design and development of phase-change thin films for next-generation electronic devices and components.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 22 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.17245v1</guid></item><item><title>[cond-mat updates on arXiv.org] Vector Spin Chirality Switching in Noncollinear Antiferromagnets</title><link>https://arxiv.org/abs/2512.17248</link><description>arXiv:2512.17248v1 Announce Type: new 
+Abstract: Spin chirality provides a powerful route to control magnetic and topological phases in materials, enabling next-generation spintronic and quantum technologies. Coplanar noncollinear antiferromagnets with Kagome lattice spin geometries host vector spin chirality (VSC), the handedness of spin arrangement, and offer an excellent platform for chirality-driven phase control. However, the microscopic mechanisms governing VSC switching and its coupling to magnetic order, electronic structure, and quantum geometry remain elusive, with experimental evidence still lacking. Here, we present conclusive experimental evidence of temperature-driven VSC switching in an archetypal noncollinear antiferromagnetic manganese chromium nitride (Mn3CrN) epitaxial thin films. The VSC switching induces a concomitant quantum-geometric and Lifshitz transition, manifested through a pronounced peak in anomalous Hall conductivity remanence, a metal-insulator-like crossover in longitudinal resistivity, and a distinct evolution of x-ray magnetic circular dichroic signal. The reversal of VSC reconstructs the spin configuration, Fermi surface topology and Berry curvature, marking a unified magnetic-electronic-quantum geometric transition. This emergent behaviour, captured through magneto-transport and magneto-optic measurements, and supported by first-principles theory establish VSC as an active control knob for chirality-driven phase engineering and the design of multifunctional quantum devices.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 22 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.17248v1</guid></item><item><title>[cond-mat updates on arXiv.org] Switchable Giant Spin Injection Current in Janus Altermagnet Fe$_2$SSeO</title><link>https://arxiv.org/abs/2512.17315</link><description>arXiv:2512.17315v1 Announce Type: new 
+Abstract: Generating and controlling spin current in miniaturized magnetic quantum devices remains a central objective of spintronics, due to its potential to enable future energy-efficient information technologies. Among the existing magnetic phases, altermagnetism have recently emerged as a highly promising platform for spin current generation and control, going beyond ferromagnetism and antiferromagnetism. Here, we propose a symmetry-allowed spin photovoltaic effect in two-dimensional (2D) altermagnetic semiconductors that enables predictable control of giant spin injection currents. Distinct from parity-time ($\mathcal{PT}$)-antiferromagnets, Janus altermagnetic semiconductors generate not only shift current but also a unique injection current with spin momentum locked in a specific direction under linearly polarized light -- a mechanism absent in $\mathcal{PT}$-antiferromagnets. Through symmetry analysis and first-principles calculations, we identify Janus Fe$_2$SSeO as a promising candidate. Specifically, the monolayer Fe$_2$SSeO exhibits a polarization-dependent injection conductivity reaching $\sim$1,200~$\mu$A/V$^{2}\!\cdot\!\hbar/2e$, and the giant spin injection current can be effectively switched by rotating the magnetization direction and engineering strains. These findings underscore the potential of 2D altermagnets in spin photovoltaics and open avenues for innovative quantum devices.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 22 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.17315v1</guid></item><item><title>[cond-mat updates on arXiv.org] Direct demonstration of time-reversal-symmetry-breaking spin injection from a compensated magnet</title><link>https://arxiv.org/abs/2512.17427</link><description>arXiv:2512.17427v1 Announce Type: new 
+Abstract: The injection, propagation and detection of spin currents are essential physical processes in spintronics. So far, the separation of charge and spin currents was facilitated by the electrical spin injection from a ferromagnet (FM) or the injection by a relativistic spin Hall effect. The devices employed are lateral spin valves comprising spatially separated injection and detection electrodes, connected by a spin-propagation channel. The time-reversal symmetry (TRS) breaking FM spin injection is realized in a geometry with an electrical bias applied between the injection electrode and the channel and is modelled by a conserved spin-polarized drift current. In contrast, the spin injection by the T-symmetric relativistic spin Hall mechanism is driven by an electrical bias applied across the injection electrode alone, and is modelled by a non-conserved spin current transverse to the applied bias. In this work, we use a lateral spin valve with a Mn5Si3 injection electrode to directly demonstrate a TRS-breaking spin injection from a compensated magnet with a vanishing net magnetization. Specifically, the TRS-breaking is demonstrated by the fact that switching between time-reversed states of the compensated magnet changes the detected spin signal. Moreover, the TRS-breaking nature of the spin injection is observed in both experimental geometries with the different electrical biasing, while using the same detection electrode. We show that this unconventional spin-injection is consistent with different magnitudes and propagation angles of electrical currents in the spin-up and spin-down channel in a d-wave altermagnet. Here our symmetry analysis and first-principles calculations are based on the compensated collinear altermagnetic order which has provided a comprehensive microscopic interpretation of earlier structural, magnetic, and anomalous Hall and Nernst measurements in Mn5Si3 thin films.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 22 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.17427v1</guid></item><item><title>[cond-mat updates on arXiv.org] Emergence of a hidden-order phase well below the charge density wave transition in a topological Weyl semimetal (TaSe$_4$)$_2$I</title><link>https://arxiv.org/abs/2512.17433</link><description>arXiv:2512.17433v1 Announce Type: new 
+Abstract: The emergence of a charge density wave (CDW) in a Weyl semimetal -- a correlated topological phase, is exceptionally rare in condensed matter systems. In this context, the quasi-one-dimensional type-III Weyl semimetal (TaSe$_4$)$_2$I undergoes a CDW transition at $T_{\mathrm{CDW}} \approx 263$~K, providing an exceptional platform to investigate correlated topological CDW states. Here, we uncover an additional hidden-order phase transition at $T^* \sim 100$ K, well below the CDW onset, using low-frequency resistance noise spectroscopy, electrical transport, and thermoelectric measurements. This transition is characterized by a sharp enhancement in the noise exponent ($\alpha$) and variance of resistance fluctuations. Analysis of higher-order statistics of resistance fluctuations reveals the correlated dynamics underlying the transition. A pronounced anomaly in the Seebeck coefficient near $T^*$ further suggests a Fermi surface reconstruction. First-principles calculations reveal a structural distortion from the high-symmetry $I422$ phase to a low-symmetry $C2$ phase, via an intermediate $I4$ symmetry. This leads to renormalization of the electronic structure near the Fermi level and opening of a bandgap in the hidden-order phase. These findings demonstrate a previously unidentified correlated phase transition in the topological CDW-Weyl semimetal (TaSe$_4$)$_2$I, enriching the phase diagram of this material and establishing it as an ideal platform for studying intertwined electronic and structural orders.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 22 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.17433v1</guid></item><item><title>[cond-mat updates on arXiv.org] Multipoles as quantitative order parameters for altermagnetic spin splitting</title><link>https://arxiv.org/abs/2512.17587</link><description>arXiv:2512.17587v1 Announce Type: new 
+Abstract: We establish a quantitative relation between the altermagnetic spin-splitting and different higher order multipoles of the charge and magnetization density around the magnetic atoms. Magnetic multipoles such as octupoles or triakontadipoles have been suggested as potential ferroic order parameters for d- and g-wave altermagnetism, respectively, based mainly on qualitative symmetry arguments. We use first-principles-based electronic structure calculations to establish a clear quantitative relation between the strength of the altermagnetic spin splitting and the magnitude of certain local multipoles. We vary the magnitude of these multipoles either by applying an appropriate constraint on the charge density or by varying a corresponding structural distortion mode, using two simple perovskite materials, SrCrO3 and LaVO3, as model systems. Our analysis indicates that in general the altermagnetic spin splitting is not exclusively determined by the lowest order nonzero magnetic multipole, but results from a superposition of contributions from different multipoles with comparable strength, suggesting the need for a multi-component order parameter to describe altermagnetism. We also discuss different measures to quantify the overall spin-splitting of a material, without relying on features that might be specific to only individual bands.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 22 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.17587v1</guid></item><item><title>[cond-mat updates on arXiv.org] Revisiting the Broken Symmetry Phase of Solid Hydrogen: A Neural Network Variational Monte Carlo Study</title><link>https://arxiv.org/abs/2512.17703</link><description>arXiv:2512.17703v1 Announce Type: new 
+Abstract: The crystal structure of high-pressure solid hydrogen remains a fundamental open problem. Although the research frontier has mostly shifted toward ultra-high pressure phases above 400 GPa, we show that even the broken symmetry phase observed around 130~GPa requires revisiting due to its intricate coupling of electronic and nuclear degrees of freedom. Here, we develop a first principle quantum Monte Carlo framework based on a deep neural network wave function that treats both electrons and nuclei quantum mechanically within the constant pressure ensemble. Our calculations reveal an unreported ground-state structure candidate for the broken symmetry phase with $Cmcm$ space group symmetry, and we test its stability up to 96 atoms. The predicted structure quantitatively matches the experimental equation of state and X-ray diffraction patterns. Furthermore, our group-theoretical analysis shows that the $Cmcm$ structure is compatible with existing Raman and infrared spectroscopic data. Crucially, static density functional theory calculation reveals the $Cmcm$ structure as a dynamically unstable saddle point on the Born-Oppenheimer potential energy surface, demonstrating that a full quantum many-body treatment of the problem is necessary. These results shed new light on the phase diagram of high-pressure hydrogen and call for further experimental verifications.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 22 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.17703v1</guid></item><item><title>[cond-mat updates on arXiv.org] Mechanistic Origin of Charge Separation and Enhanced Photocatalytic Activity in D-$\pi$-A-Functionalized UiO-66-NH$_2$ MOFs</title><link>https://arxiv.org/abs/2512.17778</link><description>arXiv:2512.17778v1 Announce Type: new 
+Abstract: Donor-$\pi$-acceptor (D-$\pi$-A) functionalization of MOF linkers can enhance visible-light photocatalytic activity, yet the mechanisms responsible for these effects remain unclear. Here we combine EPR spectroscopy, transient photoluminescence, and first-principles calculations to examine how diazo-coupled anisole, diphenylamine (DPA), and N,N-dimethylaniline (NNDMA) groups modify the photophysics of UiO-66-NH$_2$. All donor units introduce new occupied states near the valence-band edge, enabling charge separation through dye-to-framework electron transfer. Among them, the anisole-modified material stands out for facilitating efficient intersystem crossing into a triplet charge-transfer configuration that suppresses fast recombination and yields long-lived charge carriers detectable by photo-EPR. Meanwhile, bulkier donors such as DPA and NNDMA - despite their stronger electron-donating character - also tend to introduce defect-associated trap states. These results underscore the interplay between donor-induced electronic-structure changes, triplet pathways, and defect-mediated recombination, offering a mechanistic basis for tuning photocatalytic response in D-$\pi$-A-modified MOFs.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 22 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.17778v1</guid></item><item><title>[cond-mat updates on arXiv.org] Interplay of Defects and the Charge Density Wave State in Hf-Doped ZrTe$_{3}$</title><link>https://arxiv.org/abs/2512.17867</link><description>arXiv:2512.17867v1 Announce Type: new 
+Abstract: We carry out temperature-dependent scanning tunneling microscopy (STM) studies of the charge density wave (CDW) compound ZrTe$_3$ which is intentionally doped with Hf. Previous bulk studies tie Hf doping to an enhancement of the CDW transition temperature (T$_{CDW}$). In our work, by combining STM measurements with density functional theory (DFT) calculations, we observe and identify multiple defects in Zr$_{0.95}$Hf$_{0.05}$Te$_3$. Surprisingly, instead of finding clear structural or electronic signatures associated with Hf dopants, we determine the origin of the observed defects are consistent with Te and Zr vacancies. Further, our temperature dependent STM measurements allow us to examine CDW pinning to both types of observed defects below and above T$_{CDW}$.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 22 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.17867v1</guid></item><item><title>[cond-mat updates on arXiv.org] Modelling financial time series with $\phi^{4}$ quantum field theory</title><link>https://arxiv.org/abs/2512.17225</link><description>arXiv:2512.17225v1 Announce Type: cross 
+Abstract: We use a $\phi^{4}$ quantum field theory with inhomogeneous couplings and explicit symmetry-breaking to model an ensemble of financial time series from the S$\&amp;$P 500 index. The continuum nature of the $\phi^4$ theory avoids the inaccuracies that occur in Ising-based models which require a discretization of the time series. We demonstrate this using the example of the 2008 global financial crisis. The $\phi^{4}$ quantum field theory is expressive enough to reproduce the higher-order statistics such as the market kurtosis, which can serve as an indicator of possible market shocks. Accurate reproduction of high kurtosis is absent in binarized models. Therefore Ising models, despite being widely employed in econophysics, are incapable of fully representing empirical financial data, a limitation not present in the generalization of the $\phi^{4}$ scalar field theory. We then investigate the scaling properties of the $\phi^{4}$ machine learning algorithm and extract exponents which govern the behavior of the learned couplings (or weights and biases in ML language) in relation to the number of stocks in the model. Finally, we use our model to forecast the price changes of the AAPL, MSFT, and NVDA stocks. We conclude by discussing how the $\phi^{4}$ scalar field theory could be used to build investment strategies and the possible intuitions that the QFT operations of dimensional compactification and renormalization can provide for financial modelling.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 22 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.17225v1</guid></item><item><title>[cond-mat updates on arXiv.org] Generative Multi-Objective Bayesian Optimization with Scalable Batch Evaluations for Sample-Efficient De Novo Molecular Design</title><link>https://arxiv.org/abs/2512.17659</link><description>arXiv:2512.17659v1 Announce Type: cross 
+Abstract: Designing molecules that must satisfy multiple, often conflicting objectives is a central challenge in molecular discovery. The enormous size of chemical space and the cost of high-fidelity simulations have driven the development of machine learning-guided strategies for accelerating design with limited data. Among these, Bayesian optimization (BO) offers a principled framework for sample-efficient search, while generative models provide a mechanism to propose novel, diverse candidates beyond fixed libraries. However, existing methods that couple the two often rely on continuous latent spaces, which introduces both architectural entanglement and scalability challenges. This work introduces an alternative, modular "generate-then-optimize" framework for de novo multi-objective molecular design/discovery. At each iteration, a generative model is used to construct a large, diverse pool of candidate molecules, after which a novel acquisition function, qPMHI (multi-point Probability of Maximum Hypervolume Improvement), is used to optimally select a batch of candidates most likely to induce the largest Pareto front expansion. The key insight is that qPMHI decomposes additively, enabling exact, scalable batch selection via only simple ranking of probabilities that can be easily estimated with Monte Carlo sampling. We benchmark the framework against state-of-the-art latent-space and discrete molecular optimization methods, demonstrating significant improvements across synthetic benchmarks and application-driven tasks. Specifically, in a case study related to sustainable energy storage, we show that our approach quickly uncovers novel, diverse, and high-performing organic (quinone-based) cathode materials for aqueous redox flow battery applications.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 22 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.17659v1</guid></item><item><title>[cond-mat updates on arXiv.org] Hidden ferromagnetism of centrosymmetric antiferromagnets</title><link>https://arxiv.org/abs/2509.00369</link><description>arXiv:2509.00369v2 Announce Type: replace 
+Abstract: The time-reversal symmetry ($\mathcal{T}$) breaking is a signature of ferromagnetism, giving rise to such phenomena as the anomalous Hall effect (AHE) and orbital magnetism. Nevertheless, $\mathcal{T}$ can be also broken in certain classes of antiferromagnets, such as weak ferromagnets or altermagnets, which remain invariant under the spatial inversion. In the light of this similarity with the ferromagnetism, it is tempting to ask whether such unconventional antiferromagnetic (AFM) state can be presented as a simplest ferromagnetic one, i.e. within the unit cell containing only one magnetic site. We show that such presentation is possible due to special form of the spin-orbit (SO) interaction in an antiferroelectrically distorted lattice hosting this AFM state. The inversion symmetry constrains the form of the SO interaction, which becomes invariant under the symmetry operation $\{ \mathcal{S}| {\bf t} \}$, combining the $180^{\circ}$ rotation of spins ($\mathcal{S}$) with the lattice shift ${\bf t}$, connecting two antiferromagnetically coupled sublattices. This is the fundamental symmetry property of centrosymmetric antiferromagnets, which justifies the use of the generalized Bloch theorem and transformation to the local coordinate frame with one magnetic site per cell. It naturally explains the emergence of AHE and net orbital magnetization, and provide transparent expressions for these properties in terms of the electron hoppings and SO interaction operating between AFM sublattices, as well as the orthorhombic strain, controlling the piezomagnetic response. The idea is illustrated on a number of examples including two-dimensional square lattice, monoclinic VF$_4$ and CuF$_2$, and RuO$_2$-type materials with the rutile structure, using for these purposes realistic models derived from first-principles calculations.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 22 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2509.00369v2</guid></item><item><title>[cond-mat updates on arXiv.org] Percolation Diagrams Derived from First-Principles Investigation of Chemical Short-Range Order in Binary Alloys</title><link>https://arxiv.org/abs/2509.08253</link><description>arXiv:2509.08253v2 Announce Type: replace 
+Abstract: Recent developments in the percolation theory of passivation have shown that chemical short-range order (SRO) affects the aqueous passivation behavior of alloys. However, there has been no systematic exploration to quantify these SRO effects on percolation in practical alloys and the related passivation behavior. In this study, we quantify the effects of SRO on percolation in a binary size-mismatched Cu-Rh alloy and study the related passivation behavior. We develop a mixed-space cluster expansion model trained on the mixing energy calculated using density functional theory. We use the cluster expansion model to sample the configuration space via variance-constrained semi-grand canonical Monte Carlo simulations and develop SRO diagrams over a range of compositions and temperatures. Building on this with the percolation crossover model, specifically the variation of percolation threshold with SRO in the FCC lattice, we construct the first nearest-neighbor chemical percolation diagram. These diagrams can inform the design of the next generation of corrosion-resistant metallic alloys.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 22 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2509.08253v2</guid></item><item><title>[cond-mat updates on arXiv.org] Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Dataset Generation for Training Machine-Learned Interatomic Potentials</title><link>https://arxiv.org/abs/2509.21647</link><description>arXiv:2509.21647v2 Announce Type: replace 
+Abstract: Machine learning interatomic potentials (MLIPs) have become powerful tools to extend molecular simulations beyond the limits of quantum methods, offering near-quantum accuracy at much lower computational cost. Yet, developing reliable MLIPs remains difficult because it requires generating high-quality datasets, preprocessing atomic structures, and carefully training and validating models. In this work, we introduce an Automated Machine Learning Pipeline (AMLP) that unifies the entire workflow from dataset creation to model validation. AMLP employs large-language-model agents to assist with electronic-structure code selection, input preparation, and output conversion, while its analysis suite (AMLP-Analysis), based on ASE supports a range of molecular simulations. The pipeline is built on the MACE architecture and validated on acridine polymorphs, where, with a straightforward fine-tuning of a foundation model, mean absolute errors of ~1.7 meV/atom in energies and ~7.0 meV/{\AA} in forces are achieved. The fitted MLIP reproduces DFT geometries with sub-{\AA} accuracy and demonstrates stability during molecular dynamics simulations in the microcanonical and canonical ensembles.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 22 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2509.21647v2</guid></item><item><title>[cond-mat updates on arXiv.org] Local Order Average-Atom Interatomic Potentials</title><link>https://arxiv.org/abs/2510.06459</link><description>arXiv:2510.06459v3 Announce Type: replace 
+Abstract: This article describes an extension to the effective Average Atom (AA) method for random alloys to account for local ordering (short-range order) effects by utilizing information from partial radial distribution functions. The new Local-Order Average Atom (LOAA) method is rigorously derived based on statistical mechanics arguments and validated for non-stoichiometric binary 2D hexagonal crystals and 3D FeNiCr and NiAl alloys whose ground state is obtained through Monte Carlo sampling. Material properties for these alloys, and phase transformations for the NiAl system, computed from static and dynamic atomistic simulations using standard interatomic potentials (IPs) exhibit a strong dependence on local ordering that is captured by simulations with effective LOAA IPs, but not the original AA method. The advantage of LOAA is that it requires smaller system sizes to achieve statistically converged results and therefore enables the simulation of complex materials, such as high-entropy alloys, at a fraction of the computational cost of standard IPs.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 22 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2510.06459v3</guid></item><item><title>[cond-mat updates on arXiv.org] Deep Learning-Based Quantum Transport Simulations in Two-Dimensional Materials</title><link>https://arxiv.org/abs/2512.11291</link><description>arXiv:2512.11291v3 Announce Type: replace 
+Abstract: Two-dimensional (2D) materials exhibit a wide range of electronic properties that make them promising candidates for next-generation nanoelectronic devices. Accurate prediction of their quantum transport behavior is therefore of both fundamental and technological importance. While density functional theory (DFT) combined with the non-equilibrium Green$'$s function (NEGF) formalism provides reliable insights, its high computational cost limits applications to large-scale or high-throughput studies. Here we present DeePTB-NEGF, a framework that combines a deep learning-based tight-binding Hamiltonians derived learned directly from first-principles calculations (DeePTB) with efficient quantum transport simulations implemented in the DPNEGF package. To validate the method, we apply it to three prototypical 2D materials: graphene, hexagonal boron nitride (h-BN), and MoS$_2$. The resulting band structures and transmission spectra show excellent agreement with conventional DFT-NEGF results, while achieving orders-of-magnitude improvement in efficiency. These results highlight the capability of DeePTB-NEGF to enable accurate and efficient quantum transport simulations, thereby opening avenues for large-scale exploration and device design in 2D materials.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 22 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.11291v3</guid></item><item><title>[cond-mat updates on arXiv.org] Instability of explicit time integration for strongly quenched dynamics with neural quantum states</title><link>https://arxiv.org/abs/2507.17421</link><description>arXiv:2507.17421v2 Announce Type: replace-cross 
+Abstract: Neural quantum states have recently demonstrated significant potential for simulating quantum dynamics beyond the capabilities of existing variational ans\"{a}tze. However, studying strongly driven quantum dynamics with neural networks has proven challenging so far. Here, we focus on assessing several sources of numerical instabilities that can appear in the simulation of quantum dynamics based on the time-dependent variational principle (TDVP) with the computationally efficient explicit time integration scheme. Focusing on the restricted Boltzmann machine architecture, we compare solutions obtained by TDVP with analytical solutions and implicit methods as a function of the quench strength. Interestingly, we uncover a quenching strength that leads to a numerical breakdown in the absence of Monte Carlo noise, despite the fact that physical observables don't exhibit irregularities. This breakdown phenomenon appears consistently across several different TDVP formulations, even those that eliminate small eigenvalues of the Fisher matrix or use geometric properties to recast the equation of motion. We provide evidence that the nature of the instability stems from stiffness of the dynamics of the variational parameters, despite the absence of stiffness in the exact quantum dynamics. We conclude that alternative methods need to be developed to leverage the computational efficiency of explicit time integration of the TDVP equations for simulating strongly nonequilibrium quantum dynamics with neural-network quantum states.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 22 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2507.17421v2</guid></item><item><title>[Nature Communications] Structural insights into toxicant export mediated by ABCC2 in &lt;i&gt;Arabidopsis thaliana&lt;/i&gt;</title><link>https://www.nature.com/articles/s41467-025-67713-5</link><description>&lt;p&gt;Nature Communications, Published online: 22 December 2025; &lt;a href="https://www.nature.com/articles/s41467-025-67713-5"&gt;doi:10.1038/s41467-025-67713-5&lt;/a&gt;&lt;/p&gt;Here, the authors present the structures of Arabidopsis ABCC2, a detoxification transporter mediating the export of toxicants. A dimeric architecture is revealed and multiple transport states are captured, shedding lights on its working mechanism.</description><author>Nature Communications</author><pubDate>Mon, 22 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41467-025-67713-5</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>[ChemRxiv] Light-driven hydrogen evolution reactivity of molecular thio-oxomolybdate catalysts</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-n2fps?rft_dat=source%3Ddrss</link><description>Molybdenum sulfides are widely used noble metal-free hydrogen evolution reaction (HER) catalysts. Their molecular analogues, so-called thiomolybdates, have been developed as viable minimal models to study reactivity of Mo-S HER catalysts. Here, we explore the light-driven HER reactivity and stability of the mixed thio-oxo-molybdate prototype [Mo2O2S6]2- in homogeneous solution using experimental and theoretical methods. In combination with the photosensitizer [Ru(bpy)3]2+, [Mo2O2S6]2- shows promising HER performance (TON &gt; 500), as well as strong reactivity dependence on the solvent mixture used (here: methanol-water mixtures). Mechanistic studies show that increasing water concentrations in the reaction solution led to a reduction of HER reactivity. Time-dependent Raman spectroscopy show, that the system undergoes exchange of the terminal disulfide ligands for solvent ligands, leading to complex, coupled speciation equilibria in solution. Analysis of turnover-frequency (TOF) time-profiles indicate initial formation of a more active species followed by catalyst deactivation. Density functional theory (DFT) calculations provide complementary information into the speciation and show that ligand-exchanged species [Mo2O2S4(L)2]0 (L = MeOH/H2O), feature favorable free-energy landscapes for proton-coupled electron transfer than the native catalyst species. In sum, combined experiment and theory provide unique molecular-level insights into the reactivity of thio-oxo-molybdate HER catalysts and shed light on the complex speciation and changes of reactivity upon ligand exchange at these species. These structure–reactivity insights outline design rules for more robust, solvent-tolerant Mo–S HER catalysts.</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-n2fps?rft_dat=source%3Ddrss</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] Electric fields enhance Diels–Alderase catalysis in Abyssomicin C biosynthesis</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-l22j6-v2?rft_dat=source%3Ddrss</link><description>Natural Diels–Alderases catalyse [4+2] cycloadditions by preorganizing substrates into reactive conformations. However, the role of other catalytic factors, such as electrostatic effects, remain elusive. Here, we combine conceptual Density Functional Theory (CDFT) descriptors and electric field analysis to unravel the electrostatic basis of activity in the Diels-Alderase AbyU. Previously, four different enzyme-substrate poses were identified, of which two showed catalytically favorable free energy barriers based on quantum mechanical/molecular mechanical (QM/MM) reaction simulations. Here, we show that atom-condensed Fukui functions can predict the reactivity from reactant conformations alone, focusing on the diene carbons involved in bond formation. The importance of the enzyme-diene interaction is supported by electric field analysis, which shows how reactivity of enzyme-substrate poses correlates with alignment of the enzyme field along the diene moiety. Our findings establish a basis for predicting and engineering Diels–Alderase activity based on electrostatic and electronic reactivity features.</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-l22j6-v2?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] Chlorine Substitution in Disordered Rock Salt Li-rich Transition Metal Sulfides</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-rn6fx?rft_dat=source%3Ddrss</link><description>Li-rich sulfides are promising alternatives to Li-rich oxides as intercalation materials, the latter suffering from limited cycling reversibility and copious voltage fade, all associated with the redox activity of oxygen ligands upon cycling. Although moving from oxygen to sulfur ligands alleviates some of these drawbacks, sulfides suffer from their lower redox potential which limits the energy density. Here, we partially replace divalent sulfur ligands with monovalent chlorine and synthesize novel transition metal sulfochlorides Li2M1-xMnxS2Cl (with M = Ti4+ and Nb5+) crystallizing in a cation-disordered rock salt (DRX) structure. Owing to the greater electronegativity of chlorine compared to sulfur, we demonstrate an increase in average redox potential for DRX sulfochlorides. Combining ex situ X-ray absorption spectroscopy measurements at various edges and density functional theory calculations, we demonstrate that chlorine ligands preferentially form Mn-Cl and Li-Cl bonds, while sulfur ligands preferentially coordinate the high valence d0 metals. While sulfur ligands are redox active throughout the charge (and discharge), Mn2+ redox activity depends on the chemical composition, with Li2Ti0.5Mn0.5S2Cl showing a cationic redox activity only at the end of charge and beginning of discharge. More dramatic, our experimental and computational results demonstrate that the S-S bond formation induces sizeable changes in local coordination and the release of Cl ligands into the electrolyte, triggering cell corrosion as early as the second cycle. Through an electrolyte engineering approach, combining a Cl-scavenger molecule with a cathode electrolyte interphase former, we demonstrate that corrosion can be suppressed, and intrinsic cycling properties of these novel sulfochloride DRX materials investigated. Our work extends the chemical space for designing better intercalation materials, showing the unique opportunities brought by mixed anion compounds.</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-rn6fx?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] Electrochemical Potential Pulses Steer the CO2 Reduction Reaction Selectivity by Surface Reconstruction on Single-Atom Centers of Ti3C2Tx MXene</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-0t3h2?rft_dat=source%3Ddrss</link><description>The electrocatalytic CO2 reduction reaction (CO2RR) is an attractive process to remediate greenhouse gas emissions while simultaneously producing value-added chemicals. However, it suffers from a selectivity problem, as several carbon products can be formed, and the competing hydrogen evolution reaction (HER) occurs under cathodic polarization. While experimental work has shown that CO2RR selectivity can be manipulated by pulsing the electrochemical potential, in the present work we describe the effects of alternating electrochemical anodic and cathodic potential pulses on the Ti3C2Tx MXene using density functional theory (DFT) calculations in combination with a descriptor-based analysis. Under anodic polarization, water-mediated surface reconstruction leads to the formation of single-atom centers with interfacial oxygen sites that control the CO2RR selectivity by suppressing HER. The combination of the anodic pulse with a subsequent cathodic pulse stimulates a second surface reconstruction, leading to a structure with the highest activity and selectivity toward HCOOH. However, excessively high cathodic potentials trigger the formation of a surface with low stability and high HER selectivity. Our results provide insights into the structure-performance relations of reconstructed Ti3C2Tx phases under cathodic polarization for the CO2RR and reveal how CO2RR selectivity can be manipulated by precise control of potential pulse protocols.</description><author>ChemRxiv</author><pubDate>Mon, 22 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-0t3h2?rft_dat=source%3Ddrss</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>[Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)] [ASAP] Accelerating First-Principles Molecular-Dynamics Thermal Conductivity Calculations for Complex Systems</title><link>http://dx.doi.org/10.1021/acs.jctc.5c01525</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jctc.5c01525/asset/images/medium/ct5c01525_0011.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.5c01525&lt;/div&gt;</description><author>Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)</author><pubDate>Fri, 19 Dec 2025 16:16:13 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jctc.5c01525</guid></item><item><title>[Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)] [ASAP] Linearized Pair-Density Functional Theory with Spin–Orbit Coupling</title><link>http://dx.doi.org/10.1021/acs.jctc.5c01633</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jctc.5c01633/asset/images/medium/ct5c01633_0008.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.5c01633&lt;/div&gt;</description><author>Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)</author><pubDate>Fri, 19 Dec 2025 16:09:05 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jctc.5c01633</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] Structure, magnetic, and ferroelectric properties of multiferroic ${\mathrm{GdCrO}}_{3}$</title><link>http://link.aps.org/doi/10.1103/tq5w-clgg</link><description>Author(s): Jun Ding, Liwei Wen, Qianhui Mao, and Ying Zhang&lt;br /&gt;&lt;p&gt;We investigate the structure, magnetic, and ferroelectric properties of orthochromite ${\mathrm{GdCrO}}_{3}$ using density functional theory simulations. Structure relaxation with the experimental lattice found centrosymmetric &lt;i&gt;Pbnm&lt;/i&gt; and $P{2}_{1}/c$ phases together with noncentrosymmetric $Pna{2}_{1}…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 112, 214442] 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/tq5w-clgg</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: 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>[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>[Applied Physics Letters Current Issue] Strain engineering of intrinsic multiferroic coupling in bilayer ScI 2</title><link>https://pubs.aip.org/aip/apl/article/127/24/242406/3375192/Strain-engineering-of-intrinsic-multiferroic</link><description>&lt;span class="paragraphSection"&gt;Two-dimensional (2D) sliding ferroelectrics have emerged as promising candidates for next-generation nonvolatile memory technologies. However, integrating magnetic, ferroelectric, and ferrovalley properties within a single material system remains a significant challenge. Here, we propose a strategy combining interlayer sliding and strain engineering to synergistically control magnetism, ferroelectric polarization, magnetic anisotropy energy (MAE), and valley polarization in bilayer ScI&lt;sub&gt;2&lt;/sub&gt; through first-principles calculations. By altering the stacking order from AA to AB/BA configurations, the magnetic ground state transitions from antiferromagnetic (AFM) to ferromagnetic (FM) ordering, accompanied by the modulation of ferroelectric polarization and valley splitting. External strain further enables precise tuning of these properties: A compressive strain of −2% induces an AFM–FM transition in AB stacked ScI&lt;sub&gt;2&lt;/sub&gt;, while a −6% strain enhances MAE beyond 1 meV. Notably, a tensile strain of 5.71% triggers a semiconductor-to-semimetal transition, transforming the ferrovalley state into a half-valley metal. These findings establish bilayer ScI&lt;sub&gt;2&lt;/sub&gt; as a versatile platform for the multifunctional device design, offering promising pathways to integrate charge, spin, and valley degrees of freedom in 2D multiferroics.&lt;/span&gt;</description><author>Applied Physics Letters Current Issue</author><pubDate>Thu, 18 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apl/article/127/24/242406/3375192/Strain-engineering-of-intrinsic-multiferroic</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>[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>[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] The Unique Polaron Behavior at Anatase TiO2 (101) Surfaces under Water Modification: A Constrained Density Functional Theory Study</title><link>http://dx.doi.org/10.1021/acs.jpcc.5c07918</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpcc.5c07918/asset/images/medium/jp5c07918_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.5c07918&lt;/div&gt;</description><author>The Journal of Physical Chemistry C: Latest Articles (ACS Publications)</author><pubDate>Wed, 17 Dec 2025 15:49:45 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpcc.5c07918</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>[Recent Articles in Phys. Rev. Lett.] Intrinsic High Chern Numbers in Two-Dimensional ${\mathrm{M}}_{2}{\mathrm{X}}_{2}$ Materials</title><link>http://link.aps.org/doi/10.1103/ktgw-2wx2</link><description>Author(s): Zujian Dai, Xudong Zhu, and Lixin He&lt;br /&gt;&lt;p&gt;Despite sharing a common lattice structure, monolayer ${\mathrm{M}}_{2}{\mathrm{X}}_{2}$ compounds realize quantum anomalous Hall phases with distinct Chern numbers, a striking phenomenon that has not been fully explored. Combining first-principles calculations with symmetry analysis and tight-bindi…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. Lett. 135, 256401] Published Wed Dec 17, 2025</description><author>Recent Articles in Phys. Rev. Lett.</author><pubDate>Wed, 17 Dec 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/ktgw-2wx2</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>[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Nanostructure and Reactivity of Copper–Cerium Bimetallic Oxide Catalysts under Operando Conditions: Insights from First-Principles Simulations</title><link>http://dx.doi.org/10.1021/acs.jpcc.5c06605</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpcc.5c06605/asset/images/medium/jp5c06605_0009.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.5c06605&lt;/div&gt;</description><author>The Journal of Physical Chemistry C: Latest Articles (ACS Publications)</author><pubDate>Tue, 16 Dec 2025 19:31:54 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpcc.5c06605</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>[RSC - Chem. Sci. latest articles] Soft crystalline properties of 2D frameworks constructed from lithium ion and dinitriles</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC06222E</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/D5SC06222E, 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;Taichi Nishiguchi, Kotoha Kageyama, Takuya Kurihara, Nanae Shimanaka, Shun Tokuda, Shuto Tsuda, Nattapol Ma, Satoshi Horike&lt;br /&gt;We constructed two-dimensional (2D) molecular frameworks composed of lithium ion (Li&lt;small&gt;&lt;sup&gt;+&lt;/sup&gt;&lt;/small&gt;) and dinitrile aliphatic ligands to explore their mechanical and thermal properties. Calorimetry, X-ray diffraction, density functional theory calculations, alternating...&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, 16 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC06222E</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>[Applied Physics Letters Current Issue] Improving efficiency and water–oxygen barrier of perovskite solar cells through phenylalanine additives</title><link>https://pubs.aip.org/aip/apl/article/127/24/243901/3374856/Improving-efficiency-and-water-oxygen-barrier-of</link><description>&lt;span class="paragraphSection"&gt;Perovskite solar cells (PSCs) show strong potential in the photovoltaic field, but their limited material stability hinders industrial-scale application. Major challenges include degradation of the perovskite structure caused by water and oxygen, as well as uncontrolled crystallization that leads to defects and non-radiative recombination, reducing the device's power conversion efficiency (PCE). This work introduces phenylalanine (PHE) into the precursor solution. The large-sized benzene ring of PHE enhances the film's resistance to water and oxygen. In addition, the water–oxygen adsorption energy of perovskite materials is calculated based on density functional theory simulation, and the steric effects of PHE is quantified. Meanwhile, the Lewis basicity of PHE promotes directional crystallization and defect passivation of the perovskite layer. The optimized PSCs achieve a PCE of 21.49%. The T90 lifetime reaches 1008 h at room temperature and humidity (25 °C, 40% RH), and exceeds 1150 h in a desiccator (25 °C, 10% RH).&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/127/24/243901/3374856/Improving-efficiency-and-water-oxygen-barrier-of</guid></item><item><title>[Applied Physics Letters Current Issue] Realization and regulation of negative magnetoresistance behavior in Dy-doped SnS 2  with high Curie temperature</title><link>https://pubs.aip.org/aip/apl/article/127/24/242403/3374800/Realization-and-regulation-of-negative</link><description>&lt;span class="paragraphSection"&gt;The magnetic two-dimensional transition metal dichalcogenides with high Curie temperature play a pivotal role in spintronic devices and exhibit promising application potentials. In this paper, rare earth Dy-doped SnS&lt;sub&gt;2&lt;/sub&gt; wafers are synthesized through gas–liquid phase deposition and high-temperature, high-pressure processes. The material exhibits comprehensive properties such as ferromagnetism, high Curie temperature (628 K), and large negative magnetoresistance at low magnetic fields over a wide temperature range (55%–4%, 50–350 K). The results of first-principles calculations indicate that it exhibits the half-metallic behavior of electrons with a single spin direction passing through the Fermi level, with a large spin bandgap of 1.7 eV, and a flatband exists near the Fermi level. Therefore, the substitution of Sn with Dy induces a global structural reorganization and disrupts the system's symmetry, resulting in the formation of a flatband near the Fermi level through the occupation of 4f orbital electrons, providing a stable local magnetic moment. Through the orbital hybridization between Dy and S, the ferromagnetic exchange interaction is formed, achieving the ferromagnetism of Dy&lt;sub&gt;x&lt;/sub&gt;Sn&lt;sub&gt;1−x&lt;/sub&gt;S&lt;sub&gt;2&lt;/sub&gt;. This laid the foundation for the application of magnetoresistive sensors, electromagnetic shielding, and spin field-effect transistors.&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/127/24/242403/3374800/Realization-and-regulation-of-negative</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] Revealing phonon signature of dislocations in silicon carbide using machine-learning interatomic potential</title><link>https://pubs.aip.org/aip/apl/article/127/24/242102/3374786/Revealing-phonon-signature-of-dislocations-in</link><description>&lt;span class="paragraphSection"&gt;Defects are the main performance killer in silicon carbide (SiC) power devices. Among various defect types, dislocations are particularly important, as they affect device reliability. However, first-principles modeling of dislocations is computationally challenging due to their complex, extended structure and topological nature. To overcome this difficulty, we develop a neuroevolution potential (NEP) to enable accurate and large-scale lattice dynamics simulations for defect-containing SiC. To circumvent the difficulty of direct dislocation calculation, the NEP is trained on a first-principles dataset generated by iteratively incorporating various point defects, line defects, and surface structures that are computationally tractable. The resulting NEP reproduces phonon spectra in crystalline and dislocation-containing SiC, indicating its transferability. With this potential, we analyze the phonon characteristics around dislocations in 4H-SiC. Our results reveal localized vibrational modes around dislocation cores, and phonon frequency shifts away from the cores due to dislocation-induced strain fields. This work may facilitate the identification of dislocation phonon signatures and delivers a machine-learning potential that overcomes the computational limitations for large-scale SiC defect simulations.&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/127/24/242102/3374786/Revealing-phonon-signature-of-dislocations-in</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>[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Ultrafast Near-Edge X-ray Absorption Fine Structure Calculations with the Exact Integral Simplified Time-Dependent Density Functional Theory (XsTD-DFT) for Large Systems</title><link>http://dx.doi.org/10.1021/acs.jpclett.5c03411</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpclett.5c03411/asset/images/medium/jz5c03411_0006.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;The Journal of Physical Chemistry Letters&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acs.jpclett.5c03411&lt;/div&gt;</description><author>The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)</author><pubDate>Sat, 13 Dec 2025 15:21:31 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpclett.5c03411</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></channel></rss>
\ No newline at end of file