bluish/paper-feed
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Files
f12234025264790c68d6420e9dc75e3301fcdf86
paper-feed/filtered_feed.xml
github-actions[bot] f122340252 Auto-update RSS feed
2026-01-02 12:40:48 +00:00

45 lines
311 KiB
XML
Raw Blame History

This file contains invisible Unicode characters

This file contains invisible Unicode characters that are indistinguishable to humans but may be processed differently by a computer. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.

<?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</title><link>https://github.com/your_username/your_repo</link><description>Aggregated research papers</description><language>en-US</language><lastBuildDate>Fri, 02 Jan 2026 12:40:48 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[Wiley: ENERGY &amp; ENVIRONMENTAL MATERIALS: Table of Contents] Correlating the Interfacial Chemistries With Ion Conduction and Lithium Deactivation in Hybrid Solid Electrolytes</title><link>https://onlinelibrary.wiley.com/doi/10.1002/eem2.70196?af=R</link><description>ENERGY &amp;amp;ENVIRONMENTAL MATERIALS, EarlyView.</description><author>Wiley: ENERGY &amp; ENVIRONMENTAL MATERIALS: Table of Contents</author><pubDate>Fri, 02 Jan 2026 06:03:30 GMT</pubDate><guid isPermaLink="true">10.1002/eem2.70196</guid></item><item><title>[ChemRxiv] Complete Computational Exploration of Eight-Carbon Hydrocarbon Chemical Space</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-qjr5r?rft_dat=source%3Ddrss</link><description>Hydrocarbons are the most fundamental class of chemical species, but even the chemical space of those with eight carbon atoms or less has not been explored exhaustively. Here we report a full enumeration and computational exploration of this space. Density functional theory-based geometry optimisation and energy calculations have identified all stable molecules within this space, forming a new database called CHX8. A universal strain value has been proposed and assigned to each of these molecules, acting as a proxy for synthesisability and providing a clear guideline of how synthetically plausible these molecules could be. This paper explores the limits of chemical space with CHX8, with a focus on trans-fused, unsaturated and anti-Bredt ring systems. We show that, contrary to prevailing wisdom, most of these unconventional structures should be synthetically accessible, with relative strain energies less than that of cubane. It is expected that this dataset will inspire the synthesis of many new molecules with applications in various areas of chemistry, biology and materials science. The resulting dataset also provides a valuable resource for the development of general and robust machine learning models.</description><author>ChemRxiv</author><pubDate>Fri, 02 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-qjr5r?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] A round-robin exercise for the precise prediction of aqueous solubility of organic chemicals using chemometric, machine learning, and stacking ensemble of deep learning models</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-6xb6k?rft_dat=source%3Ddrss</link><description>Aqueous solubility is an important property for assessing the druggability and ecotoxicological effects of molecules. Successful drug candidates should have optimal aqueous solubility to improve bioavailability to target tissues. To effectively screen molecules in a short period of time, reliable predictive models are highly useful. In the present study, we conducted a round-robin exercise using a large, curated dataset of over 6000 compounds to predict aqueous solubility quantitatively. The six participating groups used an array of Machine Learning and Deep Learning algorithms to develop models with strong robustness and external predictive performance. All the models underwent rigorous Leave-One-Out and 10-fold cross-validation. The diversity of training sets and descriptor types used by different groups paved the way for exploring the mechanistic basis for the efficient identification of contributing features. The best-performing model was selected using the statistical Sum of Ranking Differences (SRD) approach, considering the performances on training, cross-validation, and test, as well as the performance difference between the training and test sets. Additionally, a curated, true external set was screened by the six different models. Here, the best-performing model was selected using a consensus ranking strategy based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R_Ext^2. In both approaches, i.e., the inherent model performance in terms of training, test, and cross-validation statistics, and the ability of the model to efficiently predict true external data, the Stacking Ensemble of Deep q-RASPR model emerged as the winner. This model showed comparable predictive performance to the previously reported model, which apparently lacked a proper data curation workflow and contained a significant number of duplicates and mixtures in its dataset, which can inflate model statistics. The insights from the different feature contributions from the different groups identified the useful structural and physicochemical aspects, which can help synthetic chemists to optimize molecules.</description><author>ChemRxiv</author><pubDate>Fri, 02 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-6xb6k?rft_dat=source%3Ddrss</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Accelerating the search for superconductors using machine learning</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007967?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): Suhas Adiga, Umesh V. Waghmare&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Thu, 01 Jan 2026 18:29:38 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007967</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>Thu, 01 Jan 2026 12:22:12 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>Thu, 01 Jan 2026 12:22:12 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725006852</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] Protonation dynamics of confined ethanol–water mixtures in H-ZSM-5 from machine learning-driven metadynamics</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725007249?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Catalysis, Volume 454&lt;/p&gt;&lt;p&gt;Author(s): Princy Jarngal, Benjamin A. Jackson, Simuck F. Yuk, Difan Zhang, Mal-Soon Lee, Maria Cristina Menziani, Vassiliki-Alexandra Glezakou, Roger Rousseau, GiovanniMaria Piccini&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Thu, 01 Jan 2026 12:22:12 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725007249</guid></item><item><title>[ScienceDirect Publication: Acta Materialia] Inverse Design of High-Performance Glasses Through an Encoder-Decoder Machine Learning Approach Toward Materials Discovery: Application to Oxynitride Glasses</title><link>https://www.sciencedirect.com/science/article/pii/S1359645425011693?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 29 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): Alexis Duval, Eric Robin, Patrick Houizot, Tanguy Rouxel&lt;/p&gt;</description><author>ScienceDirect Publication: Acta Materialia</author><pubDate>Thu, 01 Jan 2026 12:22:12 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1359645425011693</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>Thu, 01 Jan 2026 12:22:12 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S135964542501050X</guid></item><item><title>[ScienceDirect Publication: Acta Materialia] On-the-fly machine learning of interatomic potentials for elastic property modeling in Al–Mg–Zr solid solutions</title><link>https://www.sciencedirect.com/science/article/pii/S1359645425011310?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 15 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Acta Materialia, Volume 305&lt;/p&gt;&lt;p&gt;Author(s): Lukas Volkmer, Leonardo Medrano Sandonas, Philip Grimm, Julia Kristin Hufenbach, Gianaurelio Cuniberti&lt;/p&gt;</description><author>ScienceDirect Publication: Acta Materialia</author><pubDate>Thu, 01 Jan 2026 12:22:12 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>Thu, 01 Jan 2026 12:21:56 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>Thu, 01 Jan 2026 12:21:56 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>Thu, 01 Jan 2026 12:21:56 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352847825000565</guid></item><item><title>[ScienceDirect Publication: Journal of Materiomics] PTCDA/CuS cathode enabling stable sulfide-based all-solid-state batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352847825000814?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Materiomics, Volume 12, Issue 1&lt;/p&gt;&lt;p&gt;Author(s): Zhixing Wan, Shuo Wang, Yahao Mu, Ruihua Zhou, Hang Liu, Tingwu Jin, Di Wu, Jianlong Xia, Ce-Wen Nan&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Materiomics</author><pubDate>Thu, 01 Jan 2026 12:21:56 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352847825000814</guid></item><item><title>[ScienceDirect Publication: Current Opinion in Solid State and Materials Science] Solid electrolyte-driven suppression of H2–H3 phase transition in Ni-rich cathodes for stable high-voltage cycling</title><link>https://www.sciencedirect.com/science/article/pii/S1359028625000324?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Current Opinion in Solid State and Materials Science, Volume 39&lt;/p&gt;&lt;p&gt;Author(s): Hao Chen, Hsiao-Hsuan Wu, Chia-Chen Li&lt;/p&gt;</description><author>ScienceDirect Publication: Current Opinion in Solid State and Materials Science</author><pubDate>Thu, 01 Jan 2026 12:21:56 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>Thu, 01 Jan 2026 12:21:56 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1359028625000300</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Machine learning-aided prediction and optimization of specific capacitance in functionalized GO-based Mn/Fe co-doped Bi&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt; nanocomposites</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25048285?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 10 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 146&lt;/p&gt;&lt;p&gt;Author(s): Vijay A. Mane, Kartik M. Chavan, Sushant S. Munde, Dnyaneshwar V. Dake, Nita D. Raskar, Ramprasad B. Sonpir, Pravin V. Dhole, Ketan P. Gattu, Sandeep B. Somvanshi, Pavan R. Kayande, Jagruti S. Pawar, Babasaheb N. Dole&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Thu, 01 Jan 2026 12:21:56 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25048285</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Time-resolved impedance spectroscopy analysis of stable lithium iron phosphate cathode with enhanced electronic/ionic conductivity and ion diffusion characteristics</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25049035?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 10 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 146&lt;/p&gt;&lt;p&gt;Author(s): Jiguo Tu, Yan Li, Libo Chen, Dongbai Sun&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Thu, 01 Jan 2026 12:21:56 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25049035</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Hollow nanofiber ion conductor protective layer on Zn metal anode for long-term stable zinc battery</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25049953?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 10 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 146&lt;/p&gt;&lt;p&gt;Author(s): Mengfei Sun, Zumin Zhang, Yang Su, Wensheng Yu, Xiangting Dong, Dongtao Liu, Xinlu Wang, Gaopeng Li, Jinxian Wang&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Thu, 01 Jan 2026 12:21:56 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25049953</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Alkaline-compatible polyaniline/graphene negative electrode for ultrahigh-energy all-solid-state asymmetric supercapacitors</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25048844?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 10 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 146&lt;/p&gt;&lt;p&gt;Author(s): Aizhen Xu, Li Yin, Shaoqing Zhang, Zhiyi Zhao, Wenna Lv, Yuanyu Zhu, Yujun Qin&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Thu, 01 Jan 2026 12:21:56 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25048844</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>Thu, 01 Jan 2026 12:21:54 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>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0167273825003224</guid></item><item><title>[ScienceDirect Publication: Solid State Ionics] Anion engineering enhances the electrochemical performance of Li&lt;sub&gt;7&lt;/sub&gt;P&lt;sub&gt;3&lt;/sub&gt;S&lt;sub&gt;11&lt;/sub&gt; solid electrolyte</title><link>https://www.sciencedirect.com/science/article/pii/S0167273825003236?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 15 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Solid State Ionics, Volume 435&lt;/p&gt;&lt;p&gt;Author(s): D. Narsimulu, Ramkumar Balasubramaniam, Kwang-Sun Ryu&lt;/p&gt;</description><author>ScienceDirect Publication: Solid State Ionics</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0167273825003236</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>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625006883</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] PyVUMAT: A package to develop and deploy machine learning material models in finite element analysis simulations</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007207?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 30 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 262&lt;/p&gt;&lt;p&gt;Author(s): Joshua C. Crone&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007207</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Predicting hydrogen storage capacity of metal hydrides using novel imputation techniques and tree-based machine learning models</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007335?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 30 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 262&lt;/p&gt;&lt;p&gt;Author(s): Zaid Allal, Hassan N. Noura, Flavien Vernier, Ola Salman, Khaled Chahine&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Thu, 01 Jan 2026 12:21:54 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>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007384</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Effects of training machine-learning potentials for radiation damage simulations using different pseudopotentials</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007414?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 30 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 262&lt;/p&gt;&lt;p&gt;Author(s): A. Fellman, J. Byggmästar, F. Granberg, F. Djurabekova, K. Nordlund&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007414</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>Thu, 01 Jan 2026 12:21:54 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>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007694</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>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007803</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>Thu, 01 Jan 2026 12:21:54 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>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007955</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] An enhanced machine learning and computational screening framework for synthesizable single-phase high-entropy spinel oxides</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625008110?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 263&lt;/p&gt;&lt;p&gt;Author(s): Mahalaxmi Chandramohan, Hridhya Vinod, Meenal Deo&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625008110</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>Thu, 01 Jan 2026 12:21:54 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>Thu, 01 Jan 2026 12:21:54 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>Thu, 01 Jan 2026 12:21:54 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>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2949747725000132</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>Thu, 01 Jan 2026 12:21:54 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>Thu, 01 Jan 2026 12:21:54 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>Thu, 01 Jan 2026 12:21:54 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>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S294974772500020X</guid></item><item><title>[ScienceDirect Publication: Artificial Intelligence Chemistry] Integrating machine learning with electrochemical sensors for intelligent food safety monitoring</title><link>https://www.sciencedirect.com/science/article/pii/S2949747725000223?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): Aaryashree, Arti Devi&lt;/p&gt;</description><author>ScienceDirect Publication: Artificial Intelligence Chemistry</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2949747725000223</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>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2667141725001168</guid></item><item><title>[ScienceDirect Publication: eScience] Oxygen-induced lithium inter-cage conduction for enhanced performance in all-solid-state batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2667141725001326?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 18 November 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; eScience&lt;/p&gt;&lt;p&gt;Author(s): Yongsun Park, Myeongcho Jang, Jiyu Kim, Joon Ha Chang, Hae-Yong Kim, Seung-Deok Seo, Gwangseok Oh, Min Jae You, Sunho Ko, Jung Ho Kim, Kyung-Wan Nam, Seungho Yu, Sang-Cheol Nam, Ohmin Kwon&lt;/p&gt;</description><author>ScienceDirect Publication: eScience</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2667141725001326</guid></item><item><title>[ScienceDirect Publication: eScience] Achieving high-voltage polymer-based all-solid-state batteries based on thermodynamic and kinetic degradation insights</title><link>https://www.sciencedirect.com/science/article/pii/S2667141725000631?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; eScience, Volume 6, Issue 1&lt;/p&gt;&lt;p&gt;Author(s): Xiaoyan Yu, Yun Su, Hang Su, Ruizhi Liu, Jingyi Qiu, Xiayu Zhu, Rui Wen, Hao Zhang, Xiaohui Rong, Yong-Sheng Hu, Gaoping Cao&lt;/p&gt;</description><author>ScienceDirect Publication: eScience</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2667141725000631</guid></item><item><title>[ScienceDirect Publication: Science Bulletin] Welding surface epitaxial layer enables high-loading sulfide all-solid-state batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2095927325010965?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 15 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Science Bulletin, Volume 70, Issue 23&lt;/p&gt;&lt;p&gt;Author(s): Zhuomin Qiang, Yanbin Ning, Wei Zhao, Yajie Song, Chaoqun Zhang, Shengwei Dong, Yifei Zhao, Yan Zhang, Yinyong Sun, Biao Deng, Xudong Li, Geping Yin, Shuaifeng Lou&lt;/p&gt;</description><author>ScienceDirect Publication: Science Bulletin</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2095927325010965</guid></item><item><title>[ScienceDirect Publication: Science Bulletin] Machine learning for spectral precision: a new horizon in radiative cooling material design</title><link>https://www.sciencedirect.com/science/article/pii/S2095927325011235?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 30 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Science Bulletin, Volume 70, Issue 24&lt;/p&gt;&lt;p&gt;Author(s): Xinpeng Hu, Mingxiang Liu, Xuemei Fu, Guangming Tao, Xiang Lu, Jinping Qu&lt;/p&gt;</description><author>ScienceDirect Publication: Science Bulletin</author><pubDate>Thu, 01 Jan 2026 12:21:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2095927325011235</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>Thu, 01 Jan 2026 12:21:51 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0079642525002166</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>Thu, 01 Jan 2026 12:21:49 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>Thu, 01 Jan 2026 12:21:49 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325002962</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>Thu, 01 Jan 2026 12:21:49 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>Thu, 01 Jan 2026 12:21:49 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>Thu, 01 Jan 2026 12:21:49 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S254252932500327X</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Revisiting thermoelectric transport in 122 Zintl phases: Anharmonic phonon renormalization and phonon localization effects</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325003359?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics, Volume 60&lt;/p&gt;&lt;p&gt;Author(s): Zhenguo Wang, Yinchang Zhao, Jun Ni, Zhenhong Dai&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Thu, 01 Jan 2026 12:21:49 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325003359</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Anomalous temperature evolution of lattice anharmonicity and thermal transport in orthorhombic SnSe</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325003608?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 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): Tianxiang Jiang, Wujie Qiu, Haijuan Zhang, Jifen Wang, Kunpeng Zhao, Huaqing Xie&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Thu, 01 Jan 2026 12:21:49 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325003608</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Machine learning aided bandgap and defect engineering of mixed halide perovskites for photovoltaic applications</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325003591?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 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): Ayush Kumar Pandey, Vivek Pandey, Abhishek Tewari&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Thu, 01 Jan 2026 12:21:49 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325003591</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>Thu, 01 Jan 2026 12:21:49 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>Thu, 01 Jan 2026 12:21:49 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>Thu, 01 Jan 2026 12:21:46 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>Thu, 01 Jan 2026 12:21:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009292</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Calendar aging of sulfide all-solid-state batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009358?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Yujing Wu, Ziqi Zhang, Dengxu Wu, Fuqiang Xu, Mu Zhou, Hong Li, Liquan Chen, Fan Wu&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Thu, 01 Jan 2026 12:21:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009358</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Energy-efficient, high-accuracy sensing in loose-fitting textile sensor matrix for LLM-enabled human-robot collaboration</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009425?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Pengfei Deng, Yang Meng, Qilong Cheng, Yuanqiu Tan, Zhihong Chen, Tian Li&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Thu, 01 Jan 2026 12:21:46 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>Thu, 01 Jan 2026 12:21:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009620</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Advancing high-safety and low-cost all-solid-state batteries with polyanion cathodes: Challenges and recent progress</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009978?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 148&lt;/p&gt;&lt;p&gt;Author(s): Ali Yaghtin, Atiyeh Nekahi, Jeremy I.G. Dawkins, Xia Li, Karim Zaghib, Sixu Deng&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Thu, 01 Jan 2026 12:21:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009978</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Enabling high-accuracy lithium-ion battery status prediction via machine learning-integrated perovskite sensors</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009851?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 148&lt;/p&gt;&lt;p&gt;Author(s): Jiaxin Sun, Xianjie Xu, Zhefu Mu, Zijun Huang, Guo Chen, Xinkai Qi, Hongwei Liu, Lei Zhu, Xiuquan Gu, Xinjian He, Sheng Huang&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Thu, 01 Jan 2026 12:21:46 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>Thu, 01 Jan 2026 12:21:46 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>Thu, 01 Jan 2026 12:21:46 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>Thu, 01 Jan 2026 12:21:46 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>Thu, 01 Jan 2026 12:21:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2590238525004114</guid></item><item><title>[ScienceDirect Publication: Matter] Rapid scalable plasma processing of thin-film Li–La–Zr–O solid-state electrolytes</title><link>https://www.sciencedirect.com/science/article/pii/S2590238525005119?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 5 November 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Matter, Volume 8, Issue 11&lt;/p&gt;&lt;p&gt;Author(s): Gabriel Badillo Crane, Thomas W. Colburn, Sarah E. Holmes, Justus Just, Yi Cui, Reinhold H. Dauskardt&lt;/p&gt;</description><author>ScienceDirect Publication: Matter</author><pubDate>Thu, 01 Jan 2026 12:21:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2590238525005119</guid></item><item><title>[ScienceDirect Publication: Joule] Impact of metallic interlayers at the lithium-Li&lt;sub&gt;6&lt;/sub&gt;PS&lt;sub&gt;5&lt;/sub&gt;Cl solid electrolyte interface</title><link>https://www.sciencedirect.com/science/article/pii/S2542435125003563?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 19 November 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Joule, Volume 9, Issue 11&lt;/p&gt;&lt;p&gt;Author(s): Souhardh Kotakadi, Jack Aspinall, Matthew Burton, Yi Liang, Yuichi Aihara, Mauro Pasta&lt;/p&gt;</description><author>ScienceDirect Publication: Joule</author><pubDate>Thu, 01 Jan 2026 12:21:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125003563</guid></item><item><title>[ScienceDirect Publication: Joule] Li–Si compound anodes enabling high-performance all-solid-state Li-ion batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2542435125003769?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 17 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Joule, Volume 9, Issue 12&lt;/p&gt;&lt;p&gt;Author(s): Do-Hyeon Kim, Young-Han Lee, Jeong-Myeong Yoon, Pugalenthiyar Thondaiman, Byung Chul Kim, In-Chul Choi, Jeong-Hee Choi, Ki-Joon Jeon, Cheol-Min Park&lt;/p&gt;</description><author>ScienceDirect Publication: Joule</author><pubDate>Thu, 01 Jan 2026 12:21:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125003769</guid></item><item><title>[ScienceDirect Publication: Joule] Boosting ionic conductivity of fluoride electrolytes by polyanion coordination chemistry enabling 5 V-Class all-solid-state batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2542435125004143?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 19 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Joule&lt;/p&gt;&lt;p&gt;Author(s): Huaimin Jin, Xingyu Wang, Simeng Zhang, Xiangzhen Zhu, Chong Liu, Junyi Yue, Jie Qu, Bei Wu, Xu Han, Yueyue Wang, Yang Xu, Han Wu, Liyu Zhou, Mingying Zhang, Hao Lai, Shuo Wang, Jiangwen Liang, Xueliang Sun, Xiaona Li&lt;/p&gt;</description><author>ScienceDirect Publication: Joule</author><pubDate>Thu, 01 Jan 2026 12:21:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125004143</guid></item><item><title>[ScienceDirect Publication: Joule] Machine learning-driven interface material design for high-performance perovskite solar cells with scalability and band-gap universality</title><link>https://www.sciencedirect.com/science/article/pii/S2542435125004453?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 23 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Joule&lt;/p&gt;&lt;p&gt;Author(s): Chenyang Zhang, Yuteng Jia, Bingqian Zhang, Qiangqiang Zhao, Ruida Xu, Shuping Pang, Han Wang, Stefaan De Wolf, Kai Wang&lt;/p&gt;</description><author>ScienceDirect Publication: Joule</author><pubDate>Thu, 01 Jan 2026 12:21:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125004453</guid></item><item><title>[cond-mat updates on arXiv.org] Atomic-scale visualization of d-wave altermagnetism</title><link>https://arxiv.org/abs/2512.24114</link><description>arXiv:2512.24114v1 Announce Type: new
Abstract: Altermagnetism is a newly discovered fundamental form of magnetic order, distinct from conventional ferromagnetism and antiferromagnetism. It uniquely exhibits no net magnetization while simultaneously breaking time-reversal symmetry, a combination previously thought to be mutually exclusive. Although its existence and signatures in momentum space have been established, the direct real-space visualization of its defining rotational symmetry breaking has remained a missing cornerstone. Here, using scanning tunnelling microscopy, we present atomic-scale imaging of electronic states in the candidate material CsV2Se2O. We directly visualize the hallmark symmetry breaking in the form of unidirectional electronic patterns tied to magnetic domain walls and spin defects, as well as elliptical charging rings surrounding those defects. These observed electronic states are all linked to the underlying alternating spin texture. Our work provides the foundational real-space evidence for altermagnetism, moving the field from theoretical and momentum-space probes to direct visual confirmation; thereby opening a path to explore how this unconventional magnetic order couples to and controls other quantum electronic states.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.24114v1</guid></item><item><title>[cond-mat updates on arXiv.org] Insights Into Radiation Damage in YBa$_2$Cu$_3$O$_{7-\delta}$ From Machine-Learned Interatomic Potentials</title><link>https://arxiv.org/abs/2512.24430</link><description>arXiv:2512.24430v1 Announce Type: new
Abstract: Accurate prediction of radiation damage in YBa$_2$Cu$3$O${7-\delta}$ (YBCO) is essential for assessing the performance of high-temperature superconducting (HTS) tapes in compact fusion reactors. Existing empirical interatomic potentials have been used to model radiation damage in stoichiometric YBCO, but fail to describe oxygen-deficient compositions, which are ubiquitous in industrial Rare-Earth Barium Copper Oxide conductors and strongly influence superconducting properties. In this work, we demonstrate that modern machine-learned interatomic potentials enable predictive modelling of radiation damage in YBCO across a wide range of oxygen stoichiometries, with higher fidelity than previous empirical models. We employ two recently developed approaches: an Atomic Cluster Expansion (ACE) potential and a tabulated Gaussian Approximation Potential (tabGAP). Both models accurately reproduce Density Functional Theory (DFT) energies, forces, and threshold displacement energy distributions, providing a reliable description of atomic-scale collision processes. Molecular dynamics simulations of 5 keV cascades predict enhanced peak defect production and recombination relative to a widely used empirical potential, indicating different cascade evolution. By explicitly varying oxygen deficiency, we show that total defect production depends only weakly on stoichiometry, offering insight into the robustness of radiation damage processes in oxygen-deficient YBCO. Finally, fusion-relevant 300 keV cascade simulations reveal amorphous regions with dimensions comparable to the superconducting coherence length, consistent with electron microscopy observations of neutron-irradiated HTS tapes. These results establish machine-learned interatomic potentials as efficient and predictive tools for investigating radiation damage in YBCO across relevant compositions and irradiation conditions.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.24430v1</guid></item><item><title>[cond-mat updates on arXiv.org] Upscaling from ab initio atomistic simulations to electrode scale: The case of manganese hexacyanoferrate, a cathode material for Na-ion batteries</title><link>https://arxiv.org/abs/2512.24816</link><description>arXiv:2512.24816v1 Announce Type: new
Abstract: We present a generalizable scale-bridging computational framework that enables predictive modeling of insertion-type electrode materials from atomistic to device scales. Applied to sodium manganese hexacyanoferrate, a promising cathode material for grid-scale sodium-ion batteries, our methodology employs an active-learning strategy to train a Moment Tensor Potential through iterative hybrid grand-canonical Monte Carlo--molecular dynamics sampling, robustly capturing configuration spaces at all sodiation levels. The resulting machine learning interatomic potential accurately reproduces experimental properties including volume expansion, operating voltage, and sodium concentration-dependent structural transformations, while revealing a four-order-of-magnitude difference in sodium diffusivity between the rhombohedral (sodium-rich) and tetragonal (sodium-poor) phases at 300 K. We directly compute all critical parameters -- temperature- and concentration-dependent diffusivities, interfacial and strain energies, and complete free-energy landscapes -- to feed them into pseudo-2D phase-field simulations that predict phase-boundary propagation and rate-dependent performances across electrode length scales. This multiscale workflow establishes a blueprint for rational computational design of next-generation insertion-type materials, such as battery electrode materials, demonstrating how atomistic insights can be systematically translated into continuum-scale predictions.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.24816v1</guid></item><item><title>[cond-mat updates on arXiv.org] SSCHA-based evolutionary crystal structure prediction at finite temperatures with account for quantum nuclear motion</title><link>https://arxiv.org/abs/2512.24849</link><description>arXiv:2512.24849v1 Announce Type: new
Abstract: Accurate crystal structure prediction (CSP) at finite temperatures with quantum anharmonic effects remains challenging but very prominent in systems with lightweight atoms such as superconducting hydrides. In this work, we integrate machine-learned interatomic potentials (MLIPs) with the stochastic self-consistent harmonic approximation (SSCHA) to enable evolutionary CSP on the quantum anharmonic free-energy landscape. Using LaH$_{10}$ at 150 GPa and 300 K as a test case, we compare two approaches for SSCHA-based CSP: using light-weight active-learning MLIPs (AL-MLIPs) trained on-the-fly from scratch, and foundation models or universal MLIPs (uMLIPs) from the Matbench project. We demonstrate that AL-MLIPs allow to correctly predict the experimentally known cubic Fm$\bar{3}$m phase as the most stable polymorph at 150 GPa but require corrections within the thermodynamic perturbation theory to get consistent results. The uMLIP Mattersim-5m allow to conduct SSCHA-based CSP without requiring per-structure training and even get correct structure ranking near the global minimum, though fine-tuning may be needed for higher accuracy. Our results show that including quantum anharmonicity simplifies the free-energy landscape and is essential for correct stability rankings, that is especially important for high-temperature phases that could be missed in classical 0 K CSP. The proposed approach extends the reach of CSP to systems where quantum nuclear motion and anharmonicity dominate.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.24849v1</guid></item><item><title>[cond-mat updates on arXiv.org] Large language models and the entropy of English</title><link>https://arxiv.org/abs/2512.24969</link><description>arXiv:2512.24969v1 Announce Type: new
Abstract: We use large language models (LLMs) to uncover long-ranged structure in English texts from a variety of sources. The conditional entropy or code length in many cases continues to decrease with context length at least to $N\sim 10^4$ characters, implying that there are direct dependencies or interactions across these distances. A corollary is that there are small but significant correlations between characters at these separations, as we show from the data independent of models. The distribution of code lengths reveals an emergent certainty about an increasing fraction of characters at large $N$. Over the course of model training, we observe different dynamics at long and short context lengths, suggesting that long-ranged structure is learned only gradually. Our results constrain efforts to build statistical physics models of LLMs or language itself.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.24969v1</guid></item><item><title>[cond-mat updates on arXiv.org] Emergence of 3D Superconformal Ising Criticality on the Fuzzy Sphere</title><link>https://arxiv.org/abs/2512.25054</link><description>arXiv:2512.25054v1 Announce Type: new
Abstract: Supersymmetric conformal field theories (SCFTs) form a unique subset of quantum field theories which provide powerful insights into strongly coupled critical phenomena. Here, we present a microscopic and non-perturbative realization of the three-dimensional $\mathcal{N}=1$ superconformal Ising critical point, based on a Yukawa-type coupling between a 3D Ising CFT and a gauged Majorana fermion. Using the recently developed fuzzy sphere regularization, we directly extract the scaling dimensions of low-lying operators via the state-operator correspondence. At the critical point, we demonstrate conformal multiplet structure together with the hallmark of emergent spacetime supersymmetry through characteristic relations between fermionic and bosonic operators. Moreover, by tuning the Yukawa coupling, we explicitly track the evolution of operator spectra from the decoupled Ising-Majorana fixed point to the interacting superconformal fixed point, revealing renormalization-group flow at the operator level. Our results establish a controlled, non-perturbative microscopic route to 3D SCFTs.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.25054v1</guid></item><item><title>[cond-mat updates on arXiv.org] Learning Density Functionals to Bridge Particle and Continuum Scales</title><link>https://arxiv.org/abs/2512.23840</link><description>arXiv:2512.23840v1 Announce Type: cross
Abstract: Predicting interfacial thermodynamics across molecular and continuum scales remains a central challenge in computational science. Classical density functional theory (cDFT) provides a first-principles route to connect microscopic interactions with macroscopic observables, but its predictive accuracy depends on approximate free-energy functionals that are difficult to generalize. Here we introduce a physics-informed learning framework that augments cDFT with neural corrections trained directly against molecular-dynamics data through adjoint optimization. Rather than replacing the theory with a black-box surrogate, we embed compact neural networks within the Helmholtz free-energy functional, learning local and nonlocal corrections that preserve thermodynamic consistency while capturing missing correlations. Applied to Lennard-Jones fluids, the resulting augmented excess free-energy functional quantitatively reproduces equilibrium density profiles, coexistence curves, and surface tensions across a broad temperature range, and accurately predicts contact angles and droplet shapes far beyond the training regime. This approach combines the interpretability of statistical mechanics with the adaptability of modern machine learning, establishing a general route to learned thermodynamic functionals that bridge molecular simulations and continuum-scale models.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.23840v1</guid></item><item><title>[cond-mat updates on arXiv.org] CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution</title><link>https://arxiv.org/abs/2512.23880</link><description>arXiv:2512.23880v1 Announce Type: cross
Abstract: Large language model (LLM) agents currently depend on predefined tools or brittle tool generation, constraining their capability and adaptability to complex scientific tasks. We introduce CASCADE, a self-evolving agentic framework representing an early instantiation of the transition from "LLM + tool use" to "LLM + skill acquisition". CASCADE enables agents to master complex external tools and codify knowledge through two meta-skills: continuous learning via web search and code extraction, and self-reflection via introspection and knowledge graph exploration, among others. We evaluate CASCADE on SciSkillBench, a benchmark of 116 materials science and chemistry research tasks. CASCADE achieves a 93.3% success rate using GPT-5, compared to 35.4% without evolution mechanisms. We further demonstrate real-world applications in computational analysis, autonomous laboratory experiments, and selective reproduction of published papers. Along with human-agent collaboration and memory consolidation, CASCADE accumulates executable skills that can be shared across agents and scientists, moving toward scalable AI-assisted scientific research.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.23880v1</guid></item><item><title>[cond-mat updates on arXiv.org] Assessment of First-Principles Methods in Modeling the Melting Properties of Water</title><link>https://arxiv.org/abs/2512.23940</link><description>arXiv:2512.23940v1 Announce Type: cross
Abstract: First-principles simulations have played a crucial role in deepening our understanding of the thermodynamic properties of water, and machine learning potentials (MLPs) trained on these first-principles data widen the range of accessible properties. However, the capabilities of different first-principles methods are not yet fully understood due to the lack of systematic benchmarks, the underestimation of the uncertainties introduced by MLPs, and the neglect of nuclear quantum effects (NQEs). Here, we systematically assess first-principles methods by calculating key melting properties using path integral molecular dynamics (PIMD) driven by Deep Potential (DP) models trained on data from density functional theory (DFT) with SCAN, revPBE0-D3, SCAN0 and revPBE-D3 functionals, as well as from the MB-pol potential. We find that MB-pol is in qualitatively good agreement with the experiment in all properties tested, whereas the four DFT functionals incorrectly predict that NQEs increase the melting temperature. SCAN and SCAN0 slightly underestimate the density change between water and ice upon melting, but revPBE-D3 and revPBE0-D3 severely underestimate it. Moreover, SCAN and SCAN0 correctly predict that the maximum liquid density occurs at a temperature higher than the melting point, while revPBE-D3 and revPBE0-D3 predict the opposite behavior. Our results highlight limitations in widely used first-principles methods and call for a reassessment of their predictive power in aqueous systems.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.23940v1</guid></item><item><title>[cond-mat updates on arXiv.org] Testing Noise Correlations by an AI-Assisted Two-Qubit Quantum Sensor</title><link>https://arxiv.org/abs/2512.24135</link><description>arXiv:2512.24135v1 Announce Type: cross
Abstract: We introduce and validate a machine learning-assisted protocol to classify time and space correlations of classical noise acting on a quantum system, using two interacting qubits as probe. We consider different classes of noise, according to their Markovianity and spatial correlations. Leveraging the sensitivity of a coherent population transfer protocol under three distinct driving conditions, the various noises are discriminated by only measuring the final transfer efficiencies. This approach reaches around 90% accuracy with a minimal experimental overhead.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.24135v1</guid></item><item><title>[cond-mat updates on arXiv.org] Unsupervised Topological Phase Discovery in Periodically Driven Systems via Floquet-Bloch State</title><link>https://arxiv.org/abs/2512.24822</link><description>arXiv:2512.24822v1 Announce Type: cross
Abstract: Floquet engineering offers an unparalleled platform for realizing novel non-equilibrium topological phases. However, the unique structure of Floquet systems, which includes multiple quasienergy gaps, poses a significant challenge to classification using conventional analytical methods. We propose a novel unsupervised machine learning framework that employs a kernel defined in momentum-time ($\boldsymbol{k},t$) space, constructed directly from Floquet-Bloch eigenstates. This approach is intrinsically data-driven and requires no prior knowledge of the underlying topological invariants, providing a fundamental advantage over prior methods that rely on abstract concepts like the micromotion operator or homotopic transformations. Crucially, this work successfully reveals the intrinsic topological characteristics encoded within the Floquet eigenstates themselves. We demonstrate that our method robustly and simultaneously identifies the topological invariants associated with both the $0$-gap and the $\pi$-gap across various symmetry classes (1D AIII, 1D D, and 2D A), establishing a robust methodology for the systematic classification and discovery of complex non-equilibrium topological matter.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.24822v1</guid></item><item><title>[cond-mat updates on arXiv.org] Dynamic breaking of axial symmetry of acoustic waves in crystals as the origin of nonlinear elasticity and chaos: Analytical model and MD simulations</title><link>https://arxiv.org/abs/2510.04175</link><description>arXiv:2510.04175v2 Announce Type: replace
Abstract: A Chain of Springs and Masses (CSM) model is used in the interpretation of molecular dynamics (MD) simulations of movement of atoms in orientated FCC crystals. A force of dynamic origin is found that is perpendicular to the direction of the external shear pressure. It is proportional to the square of the applied pressure; It causes breaking of axial symmetry for propagation of transverse acoustic waves. It leads to a non-linear elastic response of crystals and to chaotic patterns in the motion of atoms. We provide an analytical derivation of an effective atomistic 3D potential for interaction between crystallographic layers. The potential is found to possess a component that has an anharmonic threefold axial symmetry around one direction. It reduces to the H{\'e}non-Heinen potential in a 2D cross-section, leading to mathematically rich, complex dynamic features. Results of simulation predict displacements of atoms that are inconsistent with the static theory of elasticity that may have been overlooked in experiments.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2510.04175v2</guid></item><item><title>[cond-mat updates on arXiv.org] GoodRegressor: A General-Purpose Symbolic Regression Framework for Physically Interpretable Materials Modeling</title><link>https://arxiv.org/abs/2510.18325</link><description>arXiv:2510.18325v4 Announce Type: replace
Abstract: Symbolic regression offers a promising route toward interpretable machine learning, yet existing methods suffer from poor predictability and computational intractability when exploring large expression spaces. I introduce GoodRegressor, a general-purpose C++-based framework that resolves these limitations while preserving full physical interpretability. By combining hierarchical descriptor construction, interaction discovery, nonlinear transformations, statistically rigorous model selection, and stacking ensemble, GoodRegressor efficiently explores symbolic model spaces such as $1.44 \times 10^{457}$, $5.99 \times 10^{124}$, and $4.20 \times 10^{430}$ possible expressions for oxygen-ion conductors, NASICONs, and superconducting oxides, respectively. Across these systems, it produces compact equations that surpass state-of-the-art black-box models and symbolic regressors, improving $R^2$ by $4 \sim 40$ %. The resulting expressions reveal physical insights, for example, into oxygen-ion transport through coordination environment and lattice flexibility. Independent ensemble runs yield nearly identical regressed values and the identical top-ranked candidate, demonstrating high reproducibility. With scalability up to $10^{4392}$ choices without interaction terms, GoodRegressor provides a foundation for general-purpose interpretable machine intelligence.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2510.18325v4</guid></item><item><title>[cond-mat updates on arXiv.org] Thermodynamic Phase Stability, Structural, Mechanical, Optoelectronic, and Thermoelectric Properties of the III-V Semiconductor AlSb for Energy Conversion Applications</title><link>https://arxiv.org/abs/2512.22277</link><description>arXiv:2512.22277v2 Announce Type: replace
Abstract: This study presents a first principles investigation of the structural, thermodynamic, electronic, optical and thermoelectric properties of aluminum antimonide (AlSb) in its cubic (F-43m) and hexagonal (P63mc) phases. Both structures are dynamically and mechanically stable, as confirmed by phonon calculations and the Born Huang criteria. The lattice constants obtained using the SCAN and PBEsol functionals show good agreement with experimental data. The cubic phase exhibits a direct band gap of 1.66 to 1.78 eV, while the hexagonal phase shows a band gap of 1.48 to 1.59 eV, as confirmed by mBJ and HSE06 calculations. Under external pressure, the band gap decreases in the cubic phase and increases in the hexagonal phase due to different s p orbital hybridization mechanisms. The optical absorption coefficient reaches 1e6 cm-1, which is comparable to or higher than values reported for other III V semiconductors. The Seebeck coefficient exceeds 1500 microV per K under intrinsic conditions, and the thermoelectric performance improves above 600 K due to enhanced phonon scattering and lattice anharmonicity. The calculated formation energies (-1.316 eV for F-43m and -1.258 eV for P63mc) confirm that the cubic phase is thermodynamically more stable. The hexagonal phase exhibits higher anisotropy and lower lattice stiffness, which is favorable for thermoelectric applications. These results demonstrate the strong interplay between crystal symmetry, phonon behavior and charge transport, and provide useful guidance for the design of AlSb based materials for optoelectronic and energy conversion technologies.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.22277v2</guid></item><item><title>[cond-mat updates on arXiv.org] CrystalDiT: A Diffusion Transformer for Crystal Generation</title><link>https://arxiv.org/abs/2508.16614</link><description>arXiv:2508.16614v3 Announce Type: replace-cross
Abstract: We present CrystalDiT, a diffusion transformer for crystal structure generation that achieves state-of-the-art performance by challenging the trend of architectural complexity. Instead of intricate, multi-stream designs, CrystalDiT employs a unified transformer that imposes a powerful inductive bias: treating lattice and atomic properties as a single, interdependent system. Combined with a periodic table-based atomic representation and a balanced training strategy, our approach achieves 8.78% SUN (Stable, Unique, Novel) rate on MP-20, substantially outperforming recent methods including FlowMM (4.21%) and MatterGen (3.66%). Notably, CrystalDiT generates 63.28% unique and novel structures while maintaining comparable stability rates, demonstrating that architectural simplicity can be more effective than complexity for materials discovery. Our results suggest that in data-limited scientific domains, carefully designed simple architectures outperform sophisticated alternatives that are prone to overfitting.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 01 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2508.16614v3</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Machine Learning‐Augmented Piezoelectric‐Ferroelectret Nanogenerators for Highly Sensitive Respiration Monitoring in Wearable Healthcare</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202522897?af=R</link><description>Advanced Functional Materials, EarlyView.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Wed, 31 Dec 2025 14:25:09 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202522897</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Ultralong‐Cycling‐Life Sodium Metal Capacitors Enabled by Hetero‐Salt Additive Strategy with NaF/LiF Hybrid Solid Electrolyte Interphase</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202525494?af=R</link><description>Advanced Functional Materials, EarlyView.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Wed, 31 Dec 2025 13:54:32 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202525494</guid></item><item><title>[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Challenges in Transitioning from Pellet to Practical Argyrodite-Based All-Solid-State Batteries</title><link>http://dx.doi.org/10.1021/acsenergylett.5c03368</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsenergylett.5c03368/asset/images/medium/nz5c03368_0004.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.5c03368&lt;/div&gt;</description><author>ACS Energy Letters: Latest Articles (ACS Publications)</author><pubDate>Wed, 31 Dec 2025 12:54:38 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsenergylett.5c03368</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Machine Learning–Guided Solvation Engineering of Chiral Viologens for Durable Neutral Aqueous Organic Flow Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202522442?af=R</link><description>Angewandte Chemie International Edition, EarlyView.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Wed, 31 Dec 2025 06:56:15 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202522442</guid></item><item><title>[Nature Communications] Domain oriented universal machine learning potential enables fast exploration of chemical space of battery electrolytes</title><link>https://www.nature.com/articles/s41467-025-67982-0</link><description>&lt;p&gt;Nature Communications, Published online: 31 December 2025; &lt;a href="https://www.nature.com/articles/s41467-025-67982-0"&gt;doi:10.1038/s41467-025-67982-0&lt;/a&gt;&lt;/p&gt;Efficient modeling of battery electrolytes is limited by the accuracy-cost trade-off. Here, authors develop a universal machine learning potential to accurately calculate transport and solvation properties across a broad chemical space.</description><author>Nature Communications</author><pubDate>Wed, 31 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41467-025-67982-0</guid></item><item><title>[Nature Machine Intelligence] Harnessing the power of single-cell large language models with parameter-efficient fine-tuning using scPEFT</title><link>https://www.nature.com/articles/s42256-025-01170-z</link><description>&lt;p&gt;Nature Machine Intelligence, Published online: 31 December 2025; &lt;a href="https://www.nature.com/articles/s42256-025-01170-z"&gt;doi:10.1038/s42256-025-01170-z&lt;/a&gt;&lt;/p&gt;He et al. present a parameter-efficient fine-tuning method for single-cell language models that improves performance on unseen diseases, treatments and cell types.</description><author>Nature Machine Intelligence</author><pubDate>Wed, 31 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s42256-025-01170-z</guid></item><item><title>[Nature Machine Intelligence] Assessing the potential of deep learning for protein–ligand docking</title><link>https://www.nature.com/articles/s42256-025-01160-1</link><description>&lt;p&gt;Nature Machine Intelligence, Published online: 31 December 2025; &lt;a href="https://www.nature.com/articles/s42256-025-01160-1"&gt;doi:10.1038/s42256-025-01160-1&lt;/a&gt;&lt;/p&gt;Morehead et al. introduce the benchmark PoseBench and evaluate the strengths and limitations of current AI-based protein–ligand docking and structure prediction methods.</description><author>Nature Machine Intelligence</author><pubDate>Wed, 31 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s42256-025-01160-1</guid></item><item><title>[ChemRxiv] A Review on Computational Insights into
Anion Exchange Membranes for Water
Electrolysis to Generate Green Hydrogen</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-5jcw1?rft_dat=source%3Ddrss</link><description>Anion exchange membranes (AEMs) have received a lot of attention in electrochemical energy storage
and conversion systems and have become a better choice to generate green hydrogen than their proton
exchange membrane counterparts owing to the non-acidic working conditions as well as the use of nonprecious metal catalytic electrodes. Albeit the safe operating conditions as well as the use of non-precious
metals, the ion conductivity and technology readiness level of AEMs are significantly lower than their PEM
counterparts. It is well accepted that the key factors that drive their performance are anion conductivity,
water uptake and chemical stability. However, there exist several other parameters that influence not
only the KPI’s but also the overall electrochemical performance of AEMs. The objective of this study is to
compile the various physical processes in an anion exchange membrane water electrolyser and focus on
the dominant ones that define the performance of these membranes. We further propose appropriate
methods to predict the KPIs using multiscale approach. In this report, we elaborately discuss the abovementioned points with a note that, this area still requires substantial research and profound
understanding from both experimental and computational point of view. In this article, a comprehensive
review on molecular dynamics simulation methods for anion exchange membranes is extensively
discussed. We also briefly touch upon the data analytics-based approaches to predict ion conductivity in these
membranes.</description><author>ChemRxiv</author><pubDate>Wed, 31 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-5jcw1?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Revealing amyloid-β peptide isoforms, including post-translationally modified species, using electrochemical profiling with a dual-electrode set-up</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-j5v38?rft_dat=source%3Ddrss</link><description>The amyloid-β (Aβ) peptides are crucial biomarkers for the diagnosis of Alzheimer's disease (AD), the most common neurodegenerative disease. The high diversity of the Aβ family provides a significant challenge for recognizing various Aβ forms, which may differ by a single amino acid or a post-translational modification. Such variation at the N-terminus of Aβ peptides leads to changes in their properties associated with typical AD biomolecular mechanisms, such as aggregation or generation of reactive oxygen species (ROS). In this work, a novel method for discriminating Aβ peptides with physiologically occurring truncations and modifications at their N-termini, based on the electrochemical profiling of their Cu(II) complexes, is presented. A dual-electrode set-up incorporating both glassy carbon and gold electrodes, together with Differential Pulse Voltammetry (DPV), was employed to generate unique electrochemical profiles, which were subsequently analyzed using chemometric techniques, including Principal Component Analysis (PCA) for data exploration, and Partial Least Squares Discriminant Analysis (PLS-DA) for classification. By combining electrochemical measurements with machine learning algorithms for pattern recognition, we successfully differentiated the studied Aβ forms, Aβ1-16, Aβ3-16, Aβpyr3-16, Aβ4-16, Aβ5-16, Aβ11-16, and Aβpyr11-16. The integration of machine learning not only enhances detection accuracy but also identifies subtle patterns that could support early-stage diagnostics. These findings support the ongoing development of analytical strategies that seek to improve the detection range and accuracy of Aβ peptides identification in Alzheimer’s disease research.</description><author>ChemRxiv</author><pubDate>Wed, 31 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-j5v38?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Sensing the Acidity of Hydrogen Bond Networks</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-twv66?rft_dat=source%3Ddrss</link><description>The reactivity of hydrogen bond networks (HBNs) is critical to many chemical and biological scenarios. When the HBNs are under constraint, hydrogen bond strength and acidity are affected significantly. HBNs exhibit cooperativity, where connections formed in one part of the HBN influence its behavior elsewhere. We combined experimental and computational approaches to examine the growth of the HBNs of water and hexafluoroisopropanol (HFIP), constrained by an aprotic cosolvent. We independently employed vibrational frequency shift of an acetonitrile probe, 1H NMR chemical shift of an aniline probe, and molecular dynamics with machine learning interatomic potentials, to demonstrate the increase in the hydrogen bond strength with the growth of the HBNs. Finally, using vibrational spectroscopy of a titratable probe, we established that not only the hydrogen bond strength, but also the acidity of HFIP is affected by the changes in the network geometry. These results enable the engineering and measurement of HBNs in confined environments with tailored acidity.</description><author>ChemRxiv</author><pubDate>Wed, 31 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-twv66?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Thiol-bearing tertiary alkylammonium chloride for regulation of PbI2 excess in FAPbI3 perovskite solar cells</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-0wscb-v2?rft_dat=source%3Ddrss</link><description>One of the key strategies for record photovoltaic efficiencies in metal halide perovskite solar cells is the addition of PbI2 excess in a stoichiometric perovskite solution which controls crystallization, passivates defects and induces a preferred orientation in the perovskite layer. However, residual PbI2, typically found in the perovskite layer after crystallization, generates non-radiative recombination centres and promotes ion migration under light and heating stress, thus accelerating performance loss. To mitigate the above issues, a common strategy is the post-deposition of organic ammonium salts which interact in situ with residual PbI2. Here, we adopt a multifunctional alkylammonium salt, 2-diethylaminoethanethiol hydrochloride (DEAET), in which both the thiol (–SH) and protonated tertiary amine groups can strongly bind to PbI₂. Upon deposition of DEAET on top of FAPbI3 film, we show that DEAT decreases the percentage of residual PbI2 by 40% and totally eliminates Pb0. These two effects lead to enhanced radiative recombination, proving a net passivation effect, while chemical analysis (FTIR and liquid-state NMR) explains that this is due to strong interactions between tertiary protonated ammonium (-NH+) and thiol (-SH) groups of DEAT with under-coordinated Pb2+. The stabilization of FAPbI3 black phase along with the establishment of a solid barrier to impede the infiltration of moisture into the perovskite layer over time lead to enhanced operational stability for the as-fabricated solar cells. The encouraging findings of this study lay the foundation for the utilization of tertiary ammonium thiol-based salts as efficient agents for interface engineering in perovskite solar cells.</description><author>ChemRxiv</author><pubDate>Wed, 31 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-0wscb-v2?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] LAMMPS-ANI: Large Scale Molecular Dynamics Simulations
with ANI Neural Network Potential</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-8v03m?rft_dat=source%3Ddrss</link><description>Machine Learning Interatomic Potentials (MLIPs), trained with Quantum Mechanics data, can model
potential energy surfaces for molecular systems with very high accuracy and extreme speedups compared
to reference quantum calculations, offering a powerful tool for studying complex chemical and biological
systems. This work presents the LAMMPS-ANI interface, which scales our ANI neural network potential
models for large systems, demonstrated with up to 100 million atoms across up to 128 NVIDIA A100
GPUs. The high performance of LAMMPS-ANI was achieved through a comprehensive code redesign,
in-depth performance profiling, and advanced GPU performance optimizations. Our benchmarks show
that ANI is 30 to 60 times faster than the state-of-the-art Allegro Model, emphasizing its speed and
efficiency. We highlight our work in large-scale molecular dynamics using ANI potentials, presenting
benchmark results for water boxes (up to 100 million atoms) and a solvated HIV capsid (44 million
atoms). We also present results for accurately simulating complex reaction processes at unprecedented
scales, such as methane combustion (300 thousand atoms) and early Earth chemistry experiment (228
thousand atoms) demonstrating the spontaneous formation of glycine.</description><author>ChemRxiv</author><pubDate>Wed, 31 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-8v03m?rft_dat=source%3Ddrss</guid></item><item><title>[Cell Reports Physical Science] Hierarchical optimization of perovskite solar cell fabrication via step-by-step machine learning</title><link>https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00642-3?rss=yes</link><description>Pu et al. report a hierarchical multi-target Bayesian optimization (MTBO) framework that optimizes the electrospray deposition process for perovskite solar cells. By integrating adaptive constraints and prioritizing thin-film quality across multiple fabrication stages, MTBO efficiently identifies feasible, high-performance conditions, enabling 1.63 eV FA0.82Cs0.18Pb(I0.86Br0.11Cl0.03)3 devices with a champion efficiency of 21.95%.</description><author>Cell Reports Physical Science</author><pubDate>Wed, 31 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00642-3?rss=yes</guid></item><item><title>[ACS Materials Letters: Latest Articles (ACS Publications)] [ASAP] Machine Learning Applications in Predicting Friction Properties of Bearing Steel: A Review</title><link>http://dx.doi.org/10.1021/acsmaterialslett.5c01047</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsmaterialslett.5c01047/asset/images/medium/tz5c01047_0009.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;ACS Materials Letters&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acsmaterialslett.5c01047&lt;/div&gt;</description><author>ACS Materials Letters: Latest Articles (ACS Publications)</author><pubDate>Tue, 30 Dec 2025 19:59:57 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsmaterialslett.5c01047</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Machine Learning-Guided Discovery of Sterically Protected High Triplet Exciplex Hosts for Ultra-Bright Green OLEDs</title><link>http://dx.doi.org/10.1021/jacs.5c16369</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c16369/asset/images/medium/ja5c16369_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.5c16369&lt;/div&gt;</description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Tue, 30 Dec 2025 19:03:11 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c16369</guid></item><item><title>[Wiley: Small Methods: Table of Contents] Standardization and Machine Learning Prediction of Tafel Slope of Pt‐Based Nanocatalysts for High‐Performance HER Catalyst Development</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smtd.202501909?af=R</link><description>Small Methods, EarlyView.</description><author>Wiley: Small Methods: Table of Contents</author><pubDate>Tue, 30 Dec 2025 12:06:41 GMT</pubDate><guid isPermaLink="true">10.1002/smtd.202501909</guid></item><item><title>[npj Computational Materials] Toward high entropy material discovery for energy applications using computational and machine learning methods</title><link>https://www.nature.com/articles/s41524-025-01918-6</link><description>&lt;p&gt;npj Computational Materials, Published online: 30 December 2025; &lt;a href="https://www.nature.com/articles/s41524-025-01918-6"&gt;doi:10.1038/s41524-025-01918-6&lt;/a&gt;&lt;/p&gt;Toward high entropy material discovery for energy applications using computational and machine learning methods</description><author>npj Computational Materials</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41524-025-01918-6</guid></item><item><title>[APL Machine Learning Current Issue] AI agents for photonic integrated circuit design automation</title><link>https://pubs.aip.org/aip/aml/article/3/4/046113/3375781/AI-agents-for-photonic-integrated-circuit-design</link><description>&lt;span class="paragraphSection"&gt;We present photonics intelligent design and optimization, a proof-of-concept multi-agent framework that converts natural-language photonic integrated circuit (PIC) design requests into layout mask files. This work demonstrates end-to-end PIC design automation using large language models (LLMs), with the goal of achieving structurally valid rather than performance-qualified layouts. We compare seven reasoning LLMs using a testbench of 102 design descriptions that ranged from single devices to 112-component PICs. The success rate for single-device designs was up to 91%. For design queries with ≤15 components, o1, Gemini-2.5-pro, and Claude Opus 4 achieved the highest end-to-end pass@5 success rates of ∼57%, with Gemini-2.5-pro requiring the fewest output tokens and the lowest cost. Future work will extend this framework toward performance qualification through expanded datasets, tighter simulation and optimization loops, and fabrication feedback integration.&lt;/span&gt;</description><author>APL Machine Learning Current Issue</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/046113/3375781/AI-agents-for-photonic-integrated-circuit-design</guid></item><item><title>[Applied Physics Letters Current Issue] Rattling-induced anharmonicity and multi-valley enhanced thermoelectric performance in layered SmZnSbO material</title><link>https://pubs.aip.org/aip/apl/article/127/26/261902/3375853/Rattling-induced-anharmonicity-and-multi-valley</link><description>&lt;span class="paragraphSection"&gt;Layered rare-earth oxides have become promising candidates for high-performance thermoelectric (TE) materials on account of the distinctive electronic structures and anisotropic transport properties. In this work, the phonon dynamics, carrier transport, and TE performance of the layered SmZnSbO compound are comprehensively evaluated using first-principles calculations, machine learning interatomic potentials, Boltzmann transport theory, and the two-channel model. The coexistence of weak interlayer van der Waals interactions, robust intralayer covalent bonding interactions, and rattling-like vibrations of Zn atoms synergistically induces significant lattice anharmonicity, resulting in a decreased lattice thermal conductivity (0.84 W/mK@900 K within the framework of the two-channel model) for the SmZnSbO compound. The natural quantum well architecture formed by the alternative conductive [Zn&lt;sub&gt;2&lt;/sub&gt;Sb&lt;sub&gt;2&lt;/sub&gt;]&lt;sup&gt;2−&lt;/sup&gt; layer and the insulated [Sm&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;2&lt;/sub&gt;]&lt;sup&gt;2+&lt;/sup&gt; layer endows quasi-two-dimensional transport characteristics, enabling a high carrier mobility of 34.1 cm&lt;sup&gt;2&lt;/sup&gt;/Vs. Moreover, the multi-valley electronic band structure with an indirect bandgap of 0.80 eV simultaneously optimizes electrical conductivity (&lt;span style="font-style: italic;"&gt;σ&lt;/span&gt;) and Seebeck coefficient (&lt;span style="font-style: italic;"&gt;S&lt;/span&gt;), resulting in an enhanced power factor. Benefiting from these synergistic features, the layered SmZnSbO compound achieves optimal dimensionless figures of merit (&lt;span style="font-style: italic;"&gt;ZT&lt;/span&gt;s) of 1.47 and 1.40 for the &lt;span style="font-style: italic;"&gt;p&lt;/span&gt;-type and &lt;span style="font-style: italic;"&gt;n&lt;/span&gt;-type doping circumstances at 900 K. The current work not only elucidates the thermal and electronic transport mechanisms for the SmZnSbO compound but also establishes a paradigm for designing high-efficiency layered oxide TE materials through combined strategies of quantum confinement, phonon engineering, and multi-valley band convergence.&lt;/span&gt;</description><author>Applied Physics Letters Current Issue</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apl/article/127/26/261902/3375853/Rattling-induced-anharmonicity-and-multi-valley</guid></item><item><title>[Applied Physics Letters Current Issue] Magneto-ionic control of perpendicular anisotropy in epitaxial Mn 4 N films</title><link>https://pubs.aip.org/aip/apl/article/127/26/262405/3375847/Magneto-ionic-control-of-perpendicular-anisotropy</link><description>&lt;span class="paragraphSection"&gt;We report reversible control of the magnetism and perpendicular magnetic anisotropy (PMA) in Mn&lt;sub&gt;4&lt;/sub&gt;N thin films through solid-state magneto-ionic gating. We grow Mn&lt;sub&gt;4&lt;/sub&gt;N on MgO(100) substrates, exhibiting bulk-like magnetization and strain-induced PMA, also promoted by capping the film with material with large spin–orbit coupling. We demonstrate that the interfacial anisotropy can be reversibly tuned through voltage-driven nitrogen ion migration when Mn&lt;sub&gt;4&lt;/sub&gt;N is in contact with a nitrogen-affine metal, such as Ta and V. We also show that solid-state gating effectively enhances the spin–orbit torque switching efficiency by reducing the coercive field without compromising the interface transparency. Finally, we demonstrate that gate-tunable devices can be harnessed for efficient nonvolatile memory functionality.&lt;/span&gt;</description><author>Applied Physics Letters Current Issue</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apl/article/127/26/262405/3375847/Magneto-ionic-control-of-perpendicular-anisotropy</guid></item><item><title>[Applied Physics Letters Current Issue] Predicting anode coatings for solid-state lithium metal batteries via first-principles thermodynamic calculations and hierarchical ion-transport algorithms</title><link>https://pubs.aip.org/aip/apl/article/127/26/263904/3375845/Predicting-anode-coatings-for-solid-state-lithium</link><description>&lt;span class="paragraphSection"&gt;Solid-state lithium metal batteries (SSLMBs) are promising for next-generation energy storage devices due to their superior energy density and excellent safety. Among solid-state electrolytes, garnet-type Li&lt;sub&gt;7&lt;/sub&gt;La&lt;sub&gt;3&lt;/sub&gt;Zr&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;12&lt;/sub&gt; (LLZO) exhibits a wide electrochemical window and high lithium-ion conductivity, but poor electrode contact and Li dendrite growth restrict its practical application. To address these challenges, this study explores the application of thin film coatings composed of Li&lt;sub&gt;8&lt;/sub&gt;&lt;span style="font-style: italic;"&gt;M&lt;/span&gt;P&lt;sub&gt;4&lt;/sub&gt; (&lt;span style="font-style: italic;"&gt;M&lt;/span&gt; = Si, Ge) at the lithium metal anode/LLZO interface. Through comprehensive first-principles thermodynamic calculations and hierarchical ion-transport algorithms, the phase stability, electrochemical stability, chemical stability, ionic transport, Li wettability, and mechanical properties of the candidate materials were systematically predicted and analyzed. Results indicate that the candidate coatings are thermodynamically stable at 0 K, with superior reduction stability against the lithium metal anode and good chemical compatibility with LLZO. Their Li-ion migration barriers are as low as 0.32 eV, enabling room-temperature ionic conductivity of approximately 10&lt;sup&gt;−5&lt;/sup&gt; S/cm. Moreover, the predicted works of adhesion for Li/Li&lt;sub&gt;8&lt;/sub&gt;&lt;span style="font-style: italic;"&gt;M&lt;/span&gt;P&lt;sub&gt;4&lt;/sub&gt; (&lt;span style="font-style: italic;"&gt;M&lt;/span&gt; = Si, Ge) are 0.99 and 0.76 J/m&lt;sup&gt;2&lt;/sup&gt;, respectively, corresponding to the contact angles of 0° and 49.3°, indicating that metallic Li shows good wettability on Li&lt;sub&gt;8&lt;/sub&gt;&lt;span style="font-style: italic;"&gt;M&lt;/span&gt;P&lt;sub&gt;4&lt;/sub&gt; (&lt;span style="font-style: italic;"&gt;M&lt;/span&gt; = Si, Ge) materials. This work provides a comprehensive understanding of the thermodynamic and dynamic behaviors of Li&lt;sub&gt;8&lt;/sub&gt;&lt;span style="font-style: italic;"&gt;M&lt;/span&gt;P&lt;sub&gt;4&lt;/sub&gt; (&lt;span style="font-style: italic;"&gt;M&lt;/span&gt; = Si, Ge) coatings and will guide the experimental design for desired SSLMB anode coatings.&lt;/span&gt;</description><author>Applied Physics Letters Current Issue</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apl/article/127/26/263904/3375845/Predicting-anode-coatings-for-solid-state-lithium</guid></item><item><title>[APL Materials Current Issue] Lithography-free fabrication of transparent, durable surfaces with embedded functional materials in glass nanoholes</title><link>https://pubs.aip.org/aip/apm/article/13/12/121112/3375772/Lithography-free-fabrication-of-transparent</link><description>&lt;span class="paragraphSection"&gt;Touch-enabled technologies, from smartphones to public kiosks, are ubiquitous, yet frequent use turns their surfaces into reservoirs for microbial contamination. Routine alcohol-based cleaning can be impractical on high-touch optical surfaces due to damage risk and usability concerns. Here, we present a scalable approach to transparent, mechanically robust glass surfaces by embedding materials with &lt;span style="font-style: italic;"&gt;ad hoc&lt;/span&gt; functionality into surface glass nanoholes. We demonstrate the concept with copper nanodisks: copper is an established antimicrobial agent, but its wear susceptibility pose challenges for use on transparent displays. Our design shields the functional material from lateral wear while allowing ion diffusion for antimicrobial efficacy. Fabrication uses only wafer-compatible, lithography-free steps: thermal dewetting of a thin silver film to create a nanosized mask; inverting it to a polymer nanoholes mask by etching the silver nanoparticles; wet etching of the glass to form nanoholes; selective copper deposition inside these holes; and liftoff of excess material. The resulting surfaces exhibit mean transmission of 80%–85% in the 380–750 nm range with haze &amp;lt;1% and minimal color shift, compared to uncoated glass. Antimicrobial efficacy, assessed against &lt;span style="font-style: italic;"&gt;Escherichia coli&lt;/span&gt; OP50 under a modified U.S. EPA protocol, shows ≈99% bacterial reduction within one hour. Abrasion tests with a crockmeter simulating finger swipes confirm that the embedded copper remains intact, with no measurable change in optical performance. This embedded design provides a scalable route to integrate antimicrobial functionality into high-touch transparent systems while preserving optical clarity and wear resistance, with potential relevance for medical, consumer, and transportation interfaces.&lt;/span&gt;</description><author>APL Materials Current Issue</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apm/article/13/12/121112/3375772/Lithography-free-fabrication-of-transparent</guid></item><item><title>[ChemRxiv] Augmenting Large Language Models for Automated Discovery of f-Element Extractants</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-b1m2q-v2?rft_dat=source%3Ddrss</link><description>Efficient separation of f-elements is a critical challenge for a wide range of emerging technologies. The chemical similarity among these elements makes the development of selective solvent extraction reagents both slow and difficult. Here, we present a quasi-autonomous, AI-enabled workflow for the design and computational screening of selective extractant ligands. Molecular design is guided by SAFE-MolGen, a large language model-based agentic system that leverages curated extraction data to propose new ligands and preliminarily rank their performance using a supervised machine learning model trained on experimental datasets to consider the impact of realistic experimental conditions. Promising human-approved ligands are then passed to a second automated pipeline that constructs three-dimensional metal-ligand complexes and performs quantum mechanical free energy calculations to directly assess metal selectivity. We demonstrate this approach for Am(III)/Eu(III) separations and report several newly designed ligands predicted to exhibit higher Am(III)/Eu(III) selectivity than the benchmark extractant CyMe4BTBP. This workflow accelerates computational exploration of the molecular space in this data-sparse field and provides a general strategy for the rapid generation and evaluation of novel lanthanide (Ln) and actinide (An) extractants.</description><author>ChemRxiv</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-b1m2q-v2?rft_dat=source%3Ddrss</guid></item><item><title>[RSC - Chem. Sci. latest articles] Solid Dual-Salt Plastic Crystal Electrolyte Enabling Rapid Ion Transfer and Stable Interphases for High-Performance Solid-State Sodium Ion Batteries</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC09186A</link><description>&lt;div&gt;&lt;i&gt;&lt;b&gt;Chem. Sci.&lt;/b&gt;&lt;/i&gt;, 2026, Accepted Manuscript&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5SC09186A, Edge Article&lt;/div&gt;&lt;div&gt;&lt;img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /&gt; Open Access&lt;/div&gt;&lt;div&gt;&lt;a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt; &lt;img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution-NonCommercial 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Yang Jiang, Rui Wang, Peng Xiong, Yangyang Liu, Hongbao Li, Longhai Zhang, Ya You, Chaofeng Zhang&lt;br /&gt;As promising next-generation energy storage systems, solid-state sodium ion batteries (SIBs) are hindered by the low ionic conductivity of their solid electrolytes and poor interfacial compatibility. Here, we developed a...&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Chem. Sci. latest articles</author><pubDate>Tue, 30 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC09186A</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Intelligent Sensing: The Emerging Integration of Machine Learning and Soft Sensors Based on Hydrogels and Ionogels</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202517851?af=R</link><description>Advanced Science, Volume 12, Issue 48, December 29, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Mon, 29 Dec 2025 21:01:21 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202517851</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Pre‐Constructed Mechano‐Electrochemical Adaptive Solid Electrolyte Interphase to Enhance Li+ Diffusion Kinetics and Interface Stability for Chemically Prelithiated SiO Anodes</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202515555?af=R</link><description>Advanced Science, Volume 12, Issue 48, December 29, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Mon, 29 Dec 2025 21:01:21 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202515555</guid></item><item><title>[Wiley: Small: Table of Contents] Unraveling A‐Site Cation Control of Hot Carrier Relaxation in Vacancy‐Ordered Halide Perovskites Through Quantum Dynamics and Interpretable Machine Learning</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202507018?af=R</link><description>Small, Volume 21, Issue 52, December 29, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Mon, 29 Dec 2025 20:38:41 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202507018</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Rational Design of Metal–Organic Frameworks for Pancreatic Cancer Therapy: from Machine Learning Screening to In Vivo Efficacy (Adv. Mater. 52/2025)</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.71868?af=R</link><description>Advanced Materials, Volume 37, Issue 52, December 29, 2025.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Mon, 29 Dec 2025 19:50:02 GMT</pubDate><guid isPermaLink="true">10.1002/adma.71868</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Rational Design of Metal–Organic Frameworks for Pancreatic Cancer Therapy: from Machine Learning Screening to In Vivo Efficacy</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202412757?af=R</link><description>Advanced Materials, Volume 37, Issue 52, December 29, 2025.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Mon, 29 Dec 2025 19:50:02 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202412757</guid></item><item><title>[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Chloride-Based Solid Electrolytes from Crystal Structure to Electrochemical Performance</title><link>http://dx.doi.org/10.1021/acsenergylett.5c03415</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsenergylett.5c03415/asset/images/medium/nz5c03415_0017.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.5c03415&lt;/div&gt;</description><author>ACS Energy Letters: Latest Articles (ACS Publications)</author><pubDate>Mon, 29 Dec 2025 19:20:24 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsenergylett.5c03415</guid></item><item><title>[Wiley: Small: Table of Contents] Enhancing Cycling Stability and Suppressing Lithium Dendrite Formation With A Hierarchical Artificial Solid Electrolyte Interphase Layer on Lithium Anodes for High‐Voltage Lithium Metal Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202512973?af=R</link><description>Small, EarlyView.</description><author>Wiley: Small: Table of Contents</author><pubDate>Mon, 29 Dec 2025 09:13:44 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202512973</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Gradient‐Heterojunction in Solid Electrolytes for Fast‐Charging Dendrite‐Free Solid‐State Lithium Metal Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202519284?af=R</link><description>Advanced Materials, EarlyView.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Mon, 29 Dec 2025 07:59:12 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202519284</guid></item><item><title>[APL Machine Learning Current Issue] Synthetic images from generative AI for compositional analysis of dried solution patterns</title><link>https://pubs.aip.org/aip/aml/article/3/4/046112/3375778/Synthetic-images-from-generative-AI-for</link><description>&lt;span class="paragraphSection"&gt;Image-based identification of solutes from dried drop stains offers a low-cost, scalable alternative to traditional chemical analysis, enabled by artificial intelligence and annotated image libraries. While recent advances in robotic droplet imaging have facilitated the collection of such datasets, experimental acquisition remains a key bottleneck. Here, we explore whether synthetic image generation can supplement or replace real data in the context of salt classification. Using Stable Diffusion with low-rank adaptation, we generate 133 000 synthetic images from as few as &lt;span style="font-style: italic;"&gt;N&lt;/span&gt; = 1–128 experimental images per salt across seven common inorganic solutes. Synthetic images become visually indistinguishable from real ones for &lt;span style="font-style: italic;"&gt;N&lt;/span&gt; ≥ 6; however, quantitative analysis based on 47 image metrics reveals subtle differences that vanish around &lt;span style="font-style: italic;"&gt;N&lt;/span&gt; = 128. When used to train random forest, XGBoost, and multilayer perceptron (MLP) models, synthetic data alone achieve classification accuracies of up to 90% in some low-data regimes. Synthetic augmentation offers a powerful strategy for expanding training sets and enabling image-based chemical classification in data-scarce scenarios, even if it seldom outperforms models trained on real data. The realism of synthetic images also highlights the growing need for tools that detect AI-generated scientific images to ensure data integrity.&lt;/span&gt;</description><author>APL Machine Learning Current Issue</author><pubDate>Mon, 29 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/046112/3375778/Synthetic-images-from-generative-AI-for</guid></item><item><title>[iScience] An Interpretable Machine Learning Model for Predicting the Effectiveness of Low-Dose Methylprednisolone in Treating Long COVID: A Retrospective Study</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02835-4?rss=yes</link><description>Long COVID is a chronic, multisystem disease with limited response to conventional treatments. While low-dose methylprednisolone has shown effectiveness in some patients, individual responses vary, and accurate predictive tools are lacking. This retrospective study included 330 Long COVID patients who received low-dose methylprednisolone treatment across three hospitals. Patients were divided into training (n=202), test (n=33), and external validation sets (n=53, n=42). Using Least Absolute Shrinkage and Selection Operator (LASSO) regression, 38 variables were analyzed to develop six machine learning models.</description><author>iScience</author><pubDate>Mon, 29 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02835-4?rss=yes</guid></item><item><title>[iScience] River plastic hotspot detection from space</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02831-7?rss=yes</link><description>Plastic pollution threatens terrestrial and aquatic ecosystems, and rivers play a central role in transporting and retaining plastics across landscapes. Effective mitigation requires scalable methods to identify riverine plastic accumulation hotspots. Here, we present a semi-automated, cloud-based pipeline that integrates satellite remote sensing and machine learning to detect river plastic hotspots. High-resolution PlanetScope imagery is used to annotate training regions, which are transferred to Sentinel-2 multispectral data to train Random Forest classifiers within Google Earth Engine.</description><author>iScience</author><pubDate>Mon, 29 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02831-7?rss=yes</guid></item><item><title>[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Machine Learning−Accelerated Discovery of Proton-Conducting 2D Materials for Proton Exchange Membranes</title><link>http://dx.doi.org/10.1021/acsnano.5c15161</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsnano.5c15161/asset/images/medium/nn5c15161_0006.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;ACS Nano&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acsnano.5c15161&lt;/div&gt;</description><author>ACS Nano: Latest Articles (ACS Publications)</author><pubDate>Sat, 27 Dec 2025 14:37:43 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsnano.5c15161</guid></item><item><title>[Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)] [ASAP] Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Data set Generation for Training Machine-Learned Interatomic Potentials</title><link>http://dx.doi.org/10.1021/acs.jctc.5c01610</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jctc.5c01610/asset/images/medium/ct5c01610_0010.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;Journal of Chemical Theory and Computation&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acs.jctc.5c01610&lt;/div&gt;</description><author>Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)</author><pubDate>Fri, 26 Dec 2025 18:25:53 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jctc.5c01610</guid></item><item><title>[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Machine Learning Photodynamics Unveils a Controlled H2 Loss Channel in the Methaniminium Cation</title><link>http://dx.doi.org/10.1021/acs.jpclett.5c03196</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpclett.5c03196/asset/images/medium/jz5c03196_0006.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;The Journal of Physical Chemistry Letters&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acs.jpclett.5c03196&lt;/div&gt;</description><author>The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)</author><pubDate>Fri, 26 Dec 2025 17:51:53 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpclett.5c03196</guid></item><item><title>[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Solvent Structure and Dynamics Controlled Memristive Ion Transport in Å-Scale Channels</title><link>http://dx.doi.org/10.1021/acs.jpclett.5c03397</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpclett.5c03397/asset/images/medium/jz5c03397_0005.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;The Journal of Physical Chemistry Letters&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acs.jpclett.5c03397&lt;/div&gt;</description><author>The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)</author><pubDate>Fri, 26 Dec 2025 16:50:38 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpclett.5c03397</guid></item><item><title>[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Design of Open Ion Transport Channels in Thick Electrodes through Directional Freezing-Assisted 3D Printing: Enhancing the Electrochemical Performance of High-Load Electrodes</title><link>http://dx.doi.org/10.1021/acs.jpclett.5c02968</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpclett.5c02968/asset/images/medium/jz5c02968_0007.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;The Journal of Physical Chemistry Letters&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acs.jpclett.5c02968&lt;/div&gt;</description><author>The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)</author><pubDate>Fri, 26 Dec 2025 16:49:57 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpclett.5c02968</guid></item><item><title>[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Machine Learning Analysis of Dimensionality Effects on Bandgap Prediction</title><link>http://dx.doi.org/10.1021/acs.jpcc.5c05232</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpcc.5c05232/asset/images/medium/jp5c05232_0011.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;The Journal of Physical Chemistry C&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acs.jpcc.5c05232&lt;/div&gt;</description><author>The Journal of Physical Chemistry C: Latest Articles (ACS Publications)</author><pubDate>Fri, 26 Dec 2025 16:06:02 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpcc.5c05232</guid></item><item><title>[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Machine Learning-Accelerated Development of Li-Rich Layered Oxides with High Reversible Capacity and Coulombic Efficiency</title><link>http://dx.doi.org/10.1021/acsnano.5c16117</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsnano.5c16117/asset/images/medium/nn5c16117_0006.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;ACS Nano&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acsnano.5c16117&lt;/div&gt;</description><author>ACS Nano: Latest Articles (ACS Publications)</author><pubDate>Fri, 26 Dec 2025 09:21:05 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsnano.5c16117</guid></item><item><title>[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] A complete spatial map of mouse retinal ganglion cells reveals density and gene expression specializations</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2515449122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 52, December 2025. &lt;br /&gt;SignificanceRetinal ganglion cells (RGCs) transmit visual signals to the brain, and their diversity supports specialized visual functions. Using gene expression mapping and machine learning, we charted the spatial distribution of all known RGC types in ...</description><author>Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents</author><pubDate>Fri, 26 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2515449122?af=R</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Mendelian Randomization and Double Machine Learning Modeling Reveal Brain Imaging‐Derived Phenotypes as Functional Contributors to 18 Autoimmune Inflammatory Diseases</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202515675?af=R</link><description>Advanced Science, EarlyView.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Fri, 26 Dec 2025 06:23:13 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202515675</guid></item><item><title>[Wiley: Carbon Energy: Table of Contents] Data‐Driven Design of Scalable Perovskite Film Fabrication via Machine Learning–Guided Processing</title><link>https://onlinelibrary.wiley.com/doi/10.1002/cey2.70164?af=R</link><description>Carbon Energy, EarlyView.</description><author>Wiley: Carbon Energy: Table of Contents</author><pubDate>Fri, 26 Dec 2025 06:22:51 GMT</pubDate><guid isPermaLink="true">10.1002/cey2.70164</guid></item><item><title>[iScience] Entropy-based byte patching transformer for self-supervised pretraining of SMILES data</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02816-0?rss=yes</link><description>Efficient molecular representations are critical for improving the performance and generalization of large language models in chemical learning. Transformer-based architectures have advanced molecular representation learning, yet capturing localized and hierarchical chemical structures remains challenging. We introduce the SMILES Byte-Patch Transformer (SMiBPT), an adaptive model that dynamically segments SMILES and DeepSMILES strings into chemically meaningful substructures through entropy-based byte patching.</description><author>iScience</author><pubDate>Fri, 26 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02816-0?rss=yes</guid></item><item><title>[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Navigating the Catholyte Landscape in All-Solid-State Batteries</title><link>http://dx.doi.org/10.1021/acsenergylett.5c03429</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsenergylett.5c03429/asset/images/medium/nz5c03429_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.5c03429&lt;/div&gt;</description><author>ACS Energy Letters: Latest Articles (ACS Publications)</author><pubDate>Wed, 24 Dec 2025 16:14:16 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsenergylett.5c03429</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Printing Nacre‐Mimetic MXene‐Based E‐Textile Devices for Sensing and Breathing‐Pattern Recognition Using Machine Learning</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202508370?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 52, December 23, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Wed, 24 Dec 2025 15:52:36 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202508370</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Role of Crosslinking and Backbone Segmental Dynamics on Ion Transport in Hydrated Anion‐Conducting Polyelectrolytes</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202514589?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 52, December 23, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Wed, 24 Dec 2025 15:52:36 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202514589</guid></item><item><title>[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Conjunctive population coding integrates sensory evidence to guide adaptive behavior</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2520444122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 52, December 2025. &lt;br /&gt;SignificanceContext-dependent behavior, i.e., the appropriate action selection according to current circumstances, long-term goals, and recent experiences, hallmarks human cognitive flexibility. But which neural mechanisms integrate prior knowledge with ...</description><author>Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents</author><pubDate>Wed, 24 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2520444122?af=R</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Hyperquaternized Biomass‐Derived Solid Electrolytes: Architecting Superionic Conduction for Sustainable Flexible Zinc‐Air Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202505711?af=R</link><description>Advanced Energy Materials, EarlyView.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Wed, 24 Dec 2025 07:08:52 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202505711</guid></item><item><title>[npj Computational Materials] High-throughput computational framework for high-order anharmonic thermal transport in cubic and tetragonal crystals</title><link>https://www.nature.com/articles/s41524-025-01920-y</link><description>&lt;p&gt;npj Computational Materials, Published online: 24 December 2025; &lt;a href="https://www.nature.com/articles/s41524-025-01920-y"&gt;doi:10.1038/s41524-025-01920-y&lt;/a&gt;&lt;/p&gt;High-throughput computational framework for high-order anharmonic thermal transport in cubic and tetragonal crystals</description><author>npj Computational Materials</author><pubDate>Wed, 24 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41524-025-01920-y</guid></item><item><title>[Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)] [ASAP] Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials</title><link>http://dx.doi.org/10.1021/acs.jctc.5c01712</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jctc.5c01712/asset/images/medium/ct5c01712_0007.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.5c01712&lt;/div&gt;</description><author>Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)</author><pubDate>Tue, 23 Dec 2025 19:20:50 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jctc.5c01712</guid></item><item><title>[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Tuning ORR Activity of N-Doped Biphenylene-Based Single-Atom Catalysts via DFT and Machine Learning Synergy</title><link>http://dx.doi.org/10.1021/acs.jpcc.5c06757</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpcc.5c06757/asset/images/medium/jp5c06757_0007.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.5c06757&lt;/div&gt;</description><author>The Journal of Physical Chemistry C: Latest Articles (ACS Publications)</author><pubDate>Tue, 23 Dec 2025 18:21:52 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpcc.5c06757</guid></item><item><title>[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Concomitant Enhancement of the Reorientational Dynamics of the BH4– Anions and Mg2+ Ionic Conductivity in Mg(BH4)2·NH3 upon Ligand Incorporation</title><link>http://dx.doi.org/10.1021/acs.jpcc.5c07031</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpcc.5c07031/asset/images/medium/jp5c07031_0012.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.5c07031&lt;/div&gt;</description><author>The Journal of Physical Chemistry C: Latest Articles (ACS Publications)</author><pubDate>Tue, 23 Dec 2025 13:34:12 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpcc.5c07031</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Interpretable Machine Learning for Battery Prognosis: Retrospect and Prospect</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202503067?af=R</link><description>Advanced Energy Materials, Volume 15, Issue 48, December 23, 2025.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 23 Dec 2025 10:15:25 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202503067</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Novel Sodium‐Rare‐Earth‐Silicate‐Based Solid Electrolytes for All‐Solid‐State Sodium Batteries: Structure, Synthesis, Conductivity, and Interface</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202503468?af=R</link><description>Advanced Energy Materials, Volume 15, Issue 48, December 23, 2025.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 23 Dec 2025 10:15:25 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202503468</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Ambipolar Ion Transport Membranes Enable Stable Noble‐Metal‐Free CO2 Electrolysis in Neutral Media</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202504286?af=R</link><description>Advanced Energy Materials, Volume 15, Issue 48, December 23, 2025.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 23 Dec 2025 10:15:25 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202504286</guid></item><item><title>[Wiley: Small: Table of Contents] Supersaturation‐Driven Co‐Precipitation Enables Scalable Wet‐Chemical Synthesis of High‐Purity Na3InCl6 Solid Electrolyte for Sodium‐Ion Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202509165?af=R</link><description>Small, Volume 21, Issue 51, December 23, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Tue, 23 Dec 2025 07:06:10 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202509165</guid></item><item><title>[Wiley: Small: Table of Contents] Synergistic Co‐Optimization Strategy for Electron‐Ion Transport Kinetics in all‐Solid‐State Sulfurized Polyacrylonitrile Cathodes</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202507810?af=R</link><description>Small, Volume 21, Issue 51, December 23, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Tue, 23 Dec 2025 07:06:10 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202507810</guid></item><item><title>[RSC - Chem. Sci. latest articles] Robust Janus-Faced Quasi-Solid-State Electrolytes Mimicking Honeycomb for Fast Transport and Adequate Supply of Sodium Ions</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC08536E</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/D5SC08536E, Edge Article&lt;/div&gt;&lt;div&gt;&lt;img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /&gt; Open Access&lt;/div&gt;&lt;div&gt;&lt;a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt; &lt;img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution-NonCommercial 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Fang Chen, Yadan Xie, Zhoubin Yu, Na Li, Xiang Ding, Yu Qiao&lt;br /&gt;Quasi-solid-state electrolytes are one of the most promising alternative candidate for traditional liquid state electrolytes with fast ion transport rate, high mechanical strength and wide temperature adaptation. Here we designed...&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Chem. Sci. latest articles</author><pubDate>Tue, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC08536E</guid></item><item><title>[RSC - Chem. Sci. latest articles] Automated Closed-Loop Continuous Flow Block Copolymer Synthesizer</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07307C</link><description>&lt;div&gt;&lt;i&gt;&lt;b&gt;Chem. Sci.&lt;/b&gt;&lt;/i&gt;, 2026, Accepted Manuscript&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5SC07307C, Edge Article&lt;/div&gt;&lt;div&gt;&lt;img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /&gt; Open Access&lt;/div&gt;&lt;div&gt;&lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt; &lt;img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;WeiNian Wong, Daniel Phillips, MD Taifur Rahman, Tanja Junkers&lt;br /&gt;A fully automated continuous flow synthesizer for diblock copolymer (BCP) synthesis was constructed comprising elements of flow chemistry, automation, machine learning and in-line monitoring. A new method using in-line FTIR...&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Chem. Sci. latest articles</author><pubDate>Tue, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07307C</guid></item><item><title>[RSC - Chem. Sci. latest articles] Node-Equivariant Message Passing for Efficient and Accurate Machine Learning Interatomic Potentials</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07248D</link><description>&lt;div&gt;&lt;i&gt;&lt;b&gt;Chem. Sci.&lt;/b&gt;&lt;/i&gt;, 2026, Accepted Manuscript&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5SC07248D, Edge Article&lt;/div&gt;&lt;div&gt;&lt;img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /&gt; Open Access&lt;/div&gt;&lt;div&gt;&lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt; &lt;img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Yaolong Zhang, Hua Guo&lt;br /&gt;Machine learned interatomic potentials, particularly equivariant message-passing (MP) models, have demonstrated high fidelity in representing first-principles data, revolutionizing computational studies in materials science, biophysics, and catalysis. However, these equivariant MP...&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Chem. Sci. latest articles</author><pubDate>Tue, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07248D</guid></item><item><title>[iScience] A Multicenter Multimodel Habitat Radiomics Model for Predicting Immunotherapy Response in Advanced NSCLC</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02783-X?rss=yes</link><description>Robust predictive biomarker is critical for identifying NSCLC patients who may benefit from immunotherapy. This study developed a CT-based habitat model using 590 advanced NSCLC cases. The model was constructed in contrast-enhanced CT images and validated on an independent cohort with non-contrast CT. Tumor volumes were segmented into three subregions via K-means clustering. Radiomic features were extracted from each habitat and used to build predictive models with six machine learning classifiers.The ExtraTrees-based habitat model demonstrated superior predictive performance in the test cohort(AUC = 0.814).</description><author>iScience</author><pubDate>Tue, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02783-X?rss=yes</guid></item><item><title>[Joule] Machine learning-driven interface material design for high-performance perovskite solar cells with scalability and band-gap universality</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00445-3?rss=yes</link><description>Machine learning-driven molecular design integrating correlation analysis, clustering, and LASSO regression discovers BIPA, an efficient interface modifier that concurrently passivates defects, optimizes band alignment, and enhances perovskite crystallinity. This strategy enables high-efficiency, scalable, and stable perovskite solar cells across a wide band-gap range (1.55–1.85 eV).</description><author>Joule</author><pubDate>Tue, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00445-3?rss=yes</guid></item><item><title>[Cell Reports Physical Science] A global thermodynamic-kinetic model capturing the hallmarks of liquid-liquid phase separation and amyloid aggregation</title><link>https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00630-7?rss=yes</link><description>Bhandari et al. develop a unified thermodynamic-kinetic framework that integrates liquid-liquid phase separation (LLPS) with amyloid aggregation. By considering oligomerization and fibrillization in both protein-poor and protein-rich phases, the model reproduces concentration-dependent aggregation kinetics and rationalizes the seemingly contradictory reports on whether LLPS accelerates or suppresses fibril formation.</description><author>Cell Reports Physical Science</author><pubDate>Tue, 23 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00630-7?rss=yes</guid></item><item><title>[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Regulating Solvation Structure and Ion Transport via Lewis-Base Dual-Functional Covalent Organic Polymer Separators for Dendrite-Free Li-Metal Anodes</title><link>http://dx.doi.org/10.1021/acsnano.5c14722</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsnano.5c14722/asset/images/medium/nn5c14722_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.5c14722&lt;/div&gt;</description><author>ACS Nano: Latest Articles (ACS Publications)</author><pubDate>Mon, 22 Dec 2025 20:52:05 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsnano.5c14722</guid></item><item><title>[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Highly Selective Lithium-Ion Separation by Regulating Ion Transport Energy Barriers of Vermiculite Membranes</title><link>http://dx.doi.org/10.1021/acsnano.5c17718</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsnano.5c17718/asset/images/medium/nn5c17718_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.5c17718&lt;/div&gt;</description><author>ACS Nano: Latest Articles (ACS Publications)</author><pubDate>Mon, 22 Dec 2025 18:30:41 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsnano.5c17718</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500092?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Mon, 22 Dec 2025 17:43:04 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500092</guid></item><item><title>[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Multianion Synergism Boosts High-Performance All-Solid-State Lithium Batteries</title><link>http://dx.doi.org/10.1021/acsnano.5c12987</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsnano.5c12987/asset/images/medium/nn5c12987_0008.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.5c12987&lt;/div&gt;</description><author>ACS Nano: Latest Articles (ACS Publications)</author><pubDate>Mon, 22 Dec 2025 14:37:35 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsnano.5c12987</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>[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Maladaptive immunity to the microbiota promotes neuronal hyperinnervation and itch via IL-17A</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2525146122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 52, December 2025. &lt;br /&gt;SignificancePruritus (itch), a phenomenon associated with various inflammatory skin diseases including psoriasis and atopic dermatitis, remains a major unmet clinical need with few effective treatments. While sensory hyperinnervation is a hallmark of ...</description><author>Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents</author><pubDate>Mon, 22 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2525146122?af=R</guid></item><item><title>[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] SR-LLM: An incremental symbolic regression framework driven by LLM-based retrieval-augmented generation</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2516995122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 52, December 2025. &lt;br /&gt;SignificanceScientists have long sought to derive models from extensive observational input–output data, ensuring these models accurately capture the underlying mapping from inputs to outputs while remaining interpretable to humans through clear meanings. ...</description><author>Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents</author><pubDate>Mon, 22 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2516995122?af=R</guid></item><item><title>[Applied Physics Reviews Current Issue] Thermal conductivity limits of MoS 2 and MoSe 2 : Revisiting high-order anharmonic lattice dynamics with machine learning potentials</title><link>https://pubs.aip.org/aip/apr/article/12/4/041424/3375425/Thermal-conductivity-limits-of-MoS2-and-MoSe2</link><description>&lt;span class="paragraphSection"&gt;Group-VI transition metal dichalcogenides (TMDs), MoS&lt;sub&gt;2&lt;/sub&gt; and MoSe&lt;sub&gt;2&lt;/sub&gt;, have emerged as prototypical low-dimensional systems with distinctive phononic and electronic properties, making them attractive for applications in nanoelectronics, optoelectronics, and thermoelectrics. However, their reported lattice thermal conductivities ( κ) remain highly inconsistent, with experimental values and theoretical predictions differing by more than an order of magnitude. These discrepancies stem from uncertainties in measurement techniques, variations in computational protocols, and ambiguities in the treatment of higher-order anharmonic processes. In this study, we critically review these inconsistencies, first by mapping the spread of experimental and modeling results, and then by identifying the methodological origins of divergence. To this end, we bridge first-principles calculations, molecular dynamics simulations, and state-of-the-art machine learning force fields (MLFFs), including recently developed foundation models. We train and benchmark GAP, MACE, NEP, and HIPHIVE against density functional theory and rigorously evaluate the impact of third- and fourth-order phonon scattering processes on κ. The computational efficiency of MLFFs enables us to extend convergence tests beyond conventional limits and to validate predictions through homogeneous nonequilibrium molecular dynamics as well. Our analysis demonstrates that, contrary to some recent claims, fully converged four-phonon processes contribute negligibly to the intrinsic thermal conductivity of both MoS&lt;sub&gt;2&lt;/sub&gt; and MoSe&lt;sub&gt;2&lt;/sub&gt;. These findings not only refine the intrinsic transport limits of 2D TMDs but also establish MLFF-based approaches as a robust and scalable framework for predictive modeling of phonon-mediated thermal transport in low-dimensional materials.&lt;/span&gt;</description><author>Applied Physics Reviews Current Issue</author><pubDate>Mon, 22 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/12/4/041424/3375425/Thermal-conductivity-limits-of-MoS2-and-MoSe2</guid></item><item><title>[iScience] Widely Targeted Metabolomics and Machine Learning Identify Succinate as a Key Metabolite in Sepsis-Associated Encephalopathy</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02781-6?rss=yes</link><description>Sepsis-associated encephalopathy (SAE) is a common and serious complication of sepsis that leads to acute brain dysfunction and long-term cognitive impairment. We used widely targeted LC–MS/MS plasma metabolomics in 29 healthy controls, 32 sepsis patients, and 27 SAE patients, combined with machine learning, to define metabolic patterns across these groups. This approach identified 12 discriminatory metabolites, with succinate showing a stepwise increase from health to sepsis to SAE and associations with clinical severity scores.</description><author>iScience</author><pubDate>Mon, 22 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02781-6?rss=yes</guid></item><item><title>[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>[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>[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>[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>[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>[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>[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>[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>[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] 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>[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] 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>[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>Gastroenterology; Health sciences; Internal medicine; Medical specialty; Medicine</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>[Wiley: Advanced Energy Materials: Table of Contents] Machine Learning Descriptors for Mapping Structure‐Property‐Performance Relationships of Perovskite Solar Cells</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202505294?af=R</link><description>Advanced Energy Materials, EarlyView.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Sat, 13 Dec 2025 07:01:43 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202505294</guid></item><item><title>[ACS Materials Letters: Latest Articles (ACS Publications)] [ASAP] Reaction Pathway Informed Strategy for Fast Solid-State Synthesis of Garnet-Type Solid Electrolyte</title><link>http://dx.doi.org/10.1021/acsmaterialslett.5c01262</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsmaterialslett.5c01262/asset/images/medium/tz5c01262_0005.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;ACS Materials Letters&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acsmaterialslett.5c01262&lt;/div&gt;</description><author>ACS Materials Letters: Latest Articles (ACS Publications)</author><pubDate>Fri, 12 Dec 2025 14:10:55 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsmaterialslett.5c01262</guid></item><item><title>[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Structural Aspects, Ionic Conductivity, and Electrochemical Properties of New Bromine-Substituted Alkali-Based Crystalline Phases MTa(Nb)X6–yBry (M = Li, Na, K; X = Cl, F)</title><link>http://dx.doi.org/10.1021/acsenergylett.5c02904</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsenergylett.5c02904/asset/images/medium/nz5c02904_0007.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;ACS Energy Letters&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acsenergylett.5c02904&lt;/div&gt;</description><author>ACS Energy Letters: Latest Articles (ACS Publications)</author><pubDate>Fri, 12 Dec 2025 13:47:45 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsenergylett.5c02904</guid></item><item><title>[APL Machine Learning Current Issue] Smart detection of plant nutrient deficiencies using machine learning and image fusion</title><link>https://pubs.aip.org/aip/aml/article/3/4/046111/3374702/Smart-detection-of-plant-nutrient-deficiencies</link><description>&lt;span class="paragraphSection"&gt;Plant nutrient deficiencies are a continual challenge for enhancing global crop output and food security. Historically, manual inspections have been plagued by subjectivity, inefficiency, and restricted scalability, necessitating the development of enhanced detection algorithms. This research introduces a novel approach utilizing image processing and machine learning to enhance detection accuracy and practical applicability. This strategy promotes classification stability by integrating machine learning classifiers, including k-Nearest Neighbors, Artificial Neural Networks, Decision Trees, and Linear Discriminant Analysis, with fusion techniques such as Majority Voting and Mean Fusion. The experiments utilize Leave-One-Out Cross-Validation for model evaluation to address dataset variability and deliver thorough assessments. The study’s results indicate that the suggested system surpasses existing systems in accuracy, precision, recall, and F1 score, attaining an overall accuracy of 98.17%. The method is effective across various noise and resolution parameters, allowing for scalability in precision agriculture applications. This discovery not only enhances the diagnosis of plant nutrient deficiencies but also enables further investigations into real-time plant health monitoring.&lt;/span&gt;</description><author>APL Machine Learning Current Issue</author><pubDate>Fri, 12 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/046111/3374702/Smart-detection-of-plant-nutrient-deficiencies</guid></item><item><title>[RSC - Digital Discovery latest articles] PEMD: a high-throughput simulation and analysis framework for solid polymer electrolytes</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00454C</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00454C" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2026, Advance Article&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00454C, Paper&lt;/div&gt;&lt;div&gt;&lt;img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /&gt; Open Access&lt;/div&gt;&lt;div&gt;&lt;a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt; &lt;img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution-NonCommercial 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Shendong Tan, Bochun Liang, Dexin Lu, Chaoyuan Ji, Wenke Ji, Zihui Li, Tingzheng Hou&lt;br /&gt;PEMD is an open-source Python framework that integrates polymer construction, force-field parameterization, multiscale simulation, and property analysis, with standardized workflows for screening and data-driven design of solid polymer electrolytes.&lt;br /&gt;To cite this article before page numbers are assigned, use the DOI form of citation above.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Fri, 12 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00454C</guid></item><item><title>[AI for Science - latest papers] Investigating CO adsorption on Cu(111) and Rh(111) surfaces using machine learning exchange-correlation functionals</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae21fa</link><description>The ‘CO adsorption puzzle’, a persistent failure of utilizing generalized gradient approximations in density functional theory to replicate CO’s experimental preference for top-site adsorption on transition-metal surfaces, remains a critical barrier in surface chemistry. While hybrid functionals such as HSE06 partially resolve this discrepancy, their prohibitive computational cost limits broader applications. We tackle this issue by adopting the Deep Kohn–Sham (DeePKS) method to train machine-learned exchange-correlation functionals. Principal component analysis reveals that the input descriptors for electronic structures separate distinctly across different chemical environments, enabling the DeePKS models to generalize to multi-element systems. We train system-specific DeePKS models for transition-metal surfaces Cu(111) and Rh(111). These models successfully recover experimental site preferences, yielding adsorption energy differences of about 10 meV compared to HSE06, and can be applied to study unseen adsorbate coverages. Furthermore, a single model for the two surfaces is trained, and the model achieves comparable accuracy in predicting not only adsorption energies and site preference but also potential energy surfaces and relaxed surface adsorption structures. The work demonstrates a promising path towards universal models, enabling catalyst exploration with hybrid functional accuracy at substantially reduced cost.</description><author>AI for Science - latest papers</author><pubDate>Fri, 12 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae21fa</guid></item><item><title>[iScience] Consensus machine learning identifies cell death gene signature for carotid artery stenosis diagnosis</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02658-6?rss=yes</link><description>Carotid artery stenosis (CAS) is a major contributor to ischemic stroke, and molecular tools for its early detection remain limited. To address this need, we integrated one in-house RNA-seq cohort with eight public datasets comprising 696 samples, together with proteomic profiling, qRT-PCR, single-cell sequencing, and FYCO1 silencing experiments. From 1,258 curated cell death–related genes, candidates were filtered by logistic regression across cohorts, and ten machine learning algorithms were combined into 105 model configurations to derive a consensus diagnostic classifier.</description><author>iScience</author><pubDate>Fri, 12 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02658-6?rss=yes</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] A Cost‐Effective and Scalable Machine Learning Approach for Quality Assessment of Fresh Maize Kernel Using NIR Spectroscopy</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202512750?af=R</link><description>Advanced Science, Volume 12, Issue 46, December 11, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 11 Dec 2025 09:23:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202512750</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] High‐Performance Zinc–Bromine Rechargeable Batteries Enabled by In‐Situ Formed Solid Electrolyte Interphase</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202508646?af=R</link><description>Advanced Science, Volume 12, Issue 46, December 11, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 11 Dec 2025 09:23:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202508646</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Nonalcoholic Fatty Liver Disease Exacerbates the Advancement of Renal Fibrosis by Modulating Renal CCR2+PIRB+ Macrophages Through the ANGPTL8/PIRB/ALOX5AP Axis</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202509351?af=R</link><description>Advanced Science, Volume 12, Issue 46, December 11, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 11 Dec 2025 09:23:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202509351</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Inverse Design of Metal‐Organic Frameworks for CH4/N2 Separation Enabled by Coupled Machine Learning and Genetic Algorithms</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202513146?af=R</link><description>Advanced Science, Volume 12, Issue 46, December 11, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 11 Dec 2025 09:23:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202513146</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Synergistic Effect of Dual‐Functional Groups in MOF‐Modified Separators for Efficient Lithium‐Ion Transport and Polysulfide Management of Lithium‐Sulfur Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202515034?af=R</link><description>Advanced Science, Volume 12, Issue 46, December 11, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 11 Dec 2025 09:23:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202515034</guid></item><item><title>[Proceedings of the National Academy of Sciences: Physical Sciences] Evaluating large language models in biomedical data science challenges through a classroom experiment</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2521062122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 50, December 2025. &lt;br /&gt;SignificanceLarge language models (LLMs) are increasingly used in science and engineering, yet their real-world effectiveness in data analysis remains unclear. In this study, graduate students used LLMs to tackle biomedical data challenges on Kaggle, a ...</description><author>Proceedings of the National Academy of Sciences: Physical Sciences</author><pubDate>Thu, 11 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2521062122?af=R</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Red Phosphorus@SnSe0.5S0.5 Core‐Shell Heterostructure Enables Superior Sodium Storage via Synergistic Ion Diffusion and Polyphosphides Trapping</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202510369?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 50, December 9, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Thu, 11 Dec 2025 06:58:45 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202510369</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Dual‐Site Ni Nanoparticles‐Ru Clusters Anchored on Hierarchical Carbon with Decoupled Gas and Ion Diffusion Channels Enabling Low‐Overpotential, Highly Stable Li‐CO2 Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202514453?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 50, December 9, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Thu, 11 Dec 2025 06:58:45 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202514453</guid></item><item><title>[RSC - Digital Discovery latest articles] Toward smart CO2 capture by the synthesis of metal organic frameworks using large language models</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00446B</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00446B" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2026, Advance Article&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00446B, Paper&lt;/div&gt;&lt;div&gt;&lt;img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /&gt; Open Access&lt;/div&gt;&lt;div&gt;&lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt; &lt;img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Hossein Mashhadimoslem, Mohammad Ali Abdol, Kourosh Zanganeh, Ahmed Shafeen, Encheng Liu, Sohrab Zendehboudi, Ali Elkamel, Aiping Yu&lt;br /&gt;This research focuses on collecting experimental CO&lt;small&gt;&lt;sub&gt;2&lt;/sub&gt;&lt;/small&gt; adsorption data from 433 scientific papers to address the challenges of MOF synthesis methods and the correlation of MOF structure and the effect of their structure on CO&lt;small&gt;&lt;sub&gt;2&lt;/sub&gt;&lt;/small&gt; adsorption using LLMs.&lt;br /&gt;To cite this article before page numbers are assigned, use the DOI form of citation above.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Thu, 11 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00446B</guid></item><item><title>[Wiley: ENERGY &amp; ENVIRONMENTAL MATERIALS: Table of Contents] Tailoring Thermophysical Properties and Multiscale Machine Learning Modeling of 2D Nanomaterial‐Infused Beeswax as a Green NePCM for Sustainable Thermal Management Systems</title><link>https://onlinelibrary.wiley.com/doi/10.1002/eem2.70194?af=R</link><description>ENERGY &amp;amp;ENVIRONMENTAL MATERIALS, EarlyView.</description><author>Wiley: ENERGY &amp; ENVIRONMENTAL MATERIALS: Table of Contents</author><pubDate>Wed, 10 Dec 2025 09:54:56 GMT</pubDate><guid isPermaLink="true">10.1002/eem2.70194</guid></item><item><title>[RSC - Digital Discovery latest articles] Optimizing Data Extraction from Materials Science Literature: A Study of Tools Using Large Language Models</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00482A</link><description>&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2025, Accepted Manuscript&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00482A, Paper&lt;/div&gt;&lt;div&gt;&lt;img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /&gt; Open Access&lt;/div&gt;&lt;div&gt;&lt;a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt; &lt;img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution-NonCommercial 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Wenkai Ning, Jeffrey Robert Reimers, Musen Li, Rika Kobayashi&lt;br /&gt;Large Language Models (LLMs) are increasingly utilized for large-scale extraction and organization of unstructured data owing to their exceptional Natural Language Processing (NLP) capabilities. Empowering materials design, vast amounts of...&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Wed, 10 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00482A</guid></item><item><title>[RSC - Chem. Sci. latest articles] A solid composite electrolyte based on three-dimensional structured zeolite networks for high-performance solid-state lithium metal batteries</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC05786H</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=D5SC05786H" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Chem. Sci.&lt;/b&gt;&lt;/i&gt;, 2026, Advance Article&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5SC05786H, Edge Article&lt;/div&gt;&lt;div&gt;&lt;img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /&gt; Open Access&lt;/div&gt;&lt;div&gt;&lt;a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt; &lt;img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution-NonCommercial 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Zhaodi Luo, Yuxin Cui, Zixuan Zhang, Malin Li, Jihong Yu&lt;br /&gt;We report a composite solid electrolyte, 3D Zeo/PEO, constructed by integrating a 3D zeolite network into a LiTFSI–PEO matrix, which boosts the performance of batteries by regulating the Li&lt;small&gt;&lt;sup&gt;+&lt;/sup&gt;&lt;/small&gt; conduction and deposition, as well as SEI formation.&lt;br /&gt;To cite this article before page numbers are assigned, use the DOI form of citation above.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Chem. Sci. latest articles</author><pubDate>Sun, 07 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC05786H</guid></item><item><title>[Proceedings of the National Academy of Sciences: Physical Sciences] Local equations describe unreasonably efficient stochastic algorithms in random K-SAT</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2510153122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 122, Issue 49, December 2025. &lt;br /&gt;SignificanceThe difficulties of algorithmic dynamics in highly nonconvex landscapes are central in several research areas, from hard combinatorial optimization to machine learning. However, it is unclear why and how some particular algorithms find ...</description><author>Proceedings of the National Academy of Sciences: Physical Sciences</author><pubDate>Fri, 05 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2510153122?af=R</guid></item><item><title>[ACS Materials Letters: Latest Articles (ACS Publications)] [ASAP] Beyond Conventional Sodium Superionic Conductor: Fe-Substituted Na3V2(PO4)2F3 Cathodes with Accelerated Charge Transport via Polyol Reflux for Sodium-Ion Batteries</title><link>http://dx.doi.org/10.1021/acsmaterialslett.5c01502</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsmaterialslett.5c01502/asset/images/medium/tz5c01502_0006.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;ACS Materials Letters&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acsmaterialslett.5c01502&lt;/div&gt;</description><author>ACS Materials Letters: Latest Articles (ACS Publications)</author><pubDate>Thu, 04 Dec 2025 13:33:58 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsmaterialslett.5c01502</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Non‐Monotonic Ion Conductivity in Lithium‐Aluminum‐Chloride Glass Solid‐State Electrolytes Explained by Cascading Hopping</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202509205?af=R</link><description>Advanced Science, Volume 12, Issue 45, December 4, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 04 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202509205</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Re‐Purposing a Modular Origami Manipulator Into an Adaptive Physical Computer for Machine Learning and Robotic Perception</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202509389?af=R</link><description>Advanced Science, Volume 12, Issue 45, December 4, 2025.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 04 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202509389</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Ultrastable Calcium Metal Anodes Enabled by a Strongly Coordinated Electrolyte Derived Bilayer Solid Electrolyte Interphase</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202510711?af=R</link><description>Advanced Materials, Volume 37, Issue 48, December 3, 2025.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Thu, 04 Dec 2025 07:04:36 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202510711</guid></item><item><title>[RSC - Digital Discovery latest articles] Understanding and mitigating distribution shifts for universal machine learning interatomic potentials</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00260E</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00260E" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2026, Advance Article&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00260E, Paper&lt;/div&gt;&lt;div&gt;&lt;img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /&gt; Open Access&lt;/div&gt;&lt;div&gt;&lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt; &lt;img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Tobias Kreiman, Aditi S. Krishnapriyan&lt;br /&gt;We find common distribution shifts that pose challenges for universal machine learning interatomic potentials (MLIPs). We develop test-time refinement strategies that mitigate the shifts and provide insights into why MLIPs struggle to generalize.&lt;br /&gt;To cite this article before page numbers are assigned, use the DOI form of citation above.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Thu, 04 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00260E</guid></item><item><title>[iScience] Physical Cognition in Altered Gravity: Link Between Sensorimotor and Cognitive Adaptability</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02604-5?rss=yes</link><description>A hallmark of human intelligence is rapid adaptation to changing environments. Yet the link between sensorimotor recalibration to new physical conditions and cognitive updating of internal models remains unclear. We addressed this using altered gravity as a model system. In a within-subject study, 25 adults completed a virtual-reality task requiring motor adjustment to non-terrestrial gravities and an online problem-solving task requiring physical reasoning under matched gravity manipulations. Adaptability in each domain was computed relative to performance under terrestrial gravity.</description><author>iScience</author><pubDate>Thu, 04 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02604-5?rss=yes</guid></item><item><title>[Wiley: Small: Table of Contents] Label‐Free Detection of Nuclear Envelope Nucleoporation using 2D Morphological Embeddings and Machine Learning</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202504402?af=R</link><description>Small, Volume 21, Issue 48, December 3, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Wed, 03 Dec 2025 15:24:49 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202504402</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Machine Learning‐Enabled Polymer Discovery for Enhanced Pulmonary siRNA Delivery</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202502805?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 49, December 2, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Wed, 03 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202502805</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Enhanced Potassium Ion Diffusion and Interface Stability Enabled by Potassiophilic rGO/CNTs/NaF Micro‐Lattice Aerogel for High‐Performance Potassium Metal Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202508586?af=R</link><description>Advanced Functional Materials, Volume 35, Issue 49, December 2, 2025.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Wed, 03 Dec 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202508586</guid></item><item><title>[Nature Reviews Physics] Predicting high-entropy alloy phases with machine learning</title><link>https://www.nature.com/articles/s42254-025-00903-8</link><description>&lt;p&gt;Nature Reviews Physics, Published online: 03 December 2025; &lt;a href="https://www.nature.com/articles/s42254-025-00903-8"&gt;doi:10.1038/s42254-025-00903-8&lt;/a&gt;&lt;/p&gt;Omokhuwele Umoru explains how generative adversarial networks can help to predict the phases of high-entropy alloys.</description><author>Nature Reviews Physics</author><pubDate>Wed, 03 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s42254-025-00903-8</guid></item><item><title>[iScience] AI enhancing differential diagnosis of acute chronic obstructive pulmonary disease and acute heart failure</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02458-7?rss=yes</link><description>Cardiovascular medicine; Respiratory medicine; Machine learning</description><author>iScience</author><pubDate>Wed, 03 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02458-7?rss=yes</guid></item><item><title>[iScience] United multi-omics and machine learning refine regulatory T cell-defined hepatocellular carcinoma subtypes</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02589-1?rss=yes</link><description>Hepatocellular carcinoma (HCC) is highly heterogeneous and aggressive, and the absence of precision individual treatment regimen enables repeated immune escape. Exploiting Treg-marker genes as key classifiers, we used 10 clustering algorithms to integrate the multi-omics HCC patients data and combined with 10 machine learning (ML) algorithms to delineate molecular subtypes to define molecular subtypes predictive of prognosis and immune response. We identified two cancer subtypes (CSs) that are associated with prognosis, with the second subtype (CS2) showing the most favourable prognostic outcomes.</description><author>iScience</author><pubDate>Wed, 03 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02589-1?rss=yes</guid></item><item><title>[Matter] Unknowium, beyond the banana, and AI discovery in materials science</title><link>https://www.cell.com/matter/fulltext/S2590-2385(25)00557-0?rss=yes</link><description>Recently, the proportion of papers implementing some sort of artificial intelligence (AI) or machine learning (ML) methods in materials science has been growing. It’s hard to ignore such a powerful and exciting tool. Relatedly, I have just returned from the Pujiang Innovation Forum held in Shanghai, China, where I participated in the “AI for Materials Science” session (Figure 1A), speaking as a lowly editor among global experts in the field.</description><author>Matter</author><pubDate>Wed, 03 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/matter/fulltext/S2590-2385(25)00557-0?rss=yes</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Taming Metal–Solid Electrolyte Interface Instability via Metal Strain Hardening</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202303500?af=R</link><description>Advanced Energy Materials, Volume 15, Issue 45, December 2, 2025.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 02 Dec 2025 13:55:59 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202303500</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Self‐Liquefying Conformal Nanocoatings via Phase‐Convertible Ion Conductors for Stable All‐Solid‐State Batteries (Adv. Energy Mater. 45/2025)</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.70345?af=R</link><description>Advanced Energy Materials, Volume 15, Issue 45, December 2, 2025.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 02 Dec 2025 13:55:59 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.70345</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Multiscale Design Strategies of Interface‐Stabilized Solid Electrolytes and Dynamic Interphase Decoding from Atomic‐to‐Macroscopic Perspectives</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202502938?af=R</link><description>Advanced Energy Materials, Volume 15, Issue 45, December 2, 2025.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 02 Dec 2025 13:55:59 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202502938</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Self‐Liquefying Conformal Nanocoatings via Phase‐Convertible Ion Conductors for Stable All‐Solid‐State Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202503562?af=R</link><description>Advanced Energy Materials, Volume 15, Issue 45, December 2, 2025.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 02 Dec 2025 13:55:59 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202503562</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] LoRA-Chem: Modular Machine Learning for Multitask Prediction in Organic Reactions</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202506542?af=R</link><description>&lt;p&gt;&lt;img src="https://www.chinesechemsoc.org/cms/asset/e0757683-26df-454a-a9ca-b322a8b7935e/keyimage.jpg" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;CCS Chemistry, Ahead of Print.&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI:10.31635/ccschem.025.202506542&lt;/div&gt;The prediction of organic chemical reactions has historically presented significant challenges owing to the inherent complexity and mechanistic diversity of reaction processes. In this work, we present LoRA-Chem, an innovative modular framework that ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Tue, 02 Dec 2025 04:48:31 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202506542?af=R</guid></item><item><title>[iScience] Dimensionality modulated generative AI for safe biomedical dataset augmentation</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02582-9?rss=yes</link><description>Generative artificial intelligence can expand small biomedical datasets but may amplify noise and distort statistical relationships. We developed genESOM, a framework integrating an error control system into a generative AI method based on emergent self-organizing maps. By separating structure learning from data synthesis, genESOM enables dimensionality modulation and injection of engineered diagnostic features, i.e., permuted versions of real variables, as negative controls that track feature importance stability.</description><author>iScience</author><pubDate>Tue, 02 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02582-9?rss=yes</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500147?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Mon, 01 Dec 2025 22:39:43 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500147</guid></item><item><title>[APL Machine Learning Current Issue] RTNinja : A generalized machine learning framework for analyzing random telegraph noise signals in nanoelectronic devices</title><link>https://pubs.aip.org/aip/aml/article/3/4/046109/3373556/RTNinja-A-generalized-machine-learning-framework</link><description>&lt;span class="paragraphSection"&gt;Random telegraph noise is a prevalent variability phenomenon in nanoelectronic devices, arising from stochastic carrier exchange at defect sites and critically impacting device reliability and performance. Conventional analysis techniques often rely on restrictive assumptions or manual interventions, limiting their applicability to complex, noisy datasets. Here, we introduce &lt;span style="font-style: italic;"&gt;RTNinja&lt;/span&gt;, a generalized, fully automated machine learning framework for the unsupervised analysis of random telegraph noise signals. &lt;span style="font-style: italic;"&gt;RTNinja&lt;/span&gt; deconvolves complex signals to identify the number and characteristics of hidden individual sources without requiring prior knowledge of the system. The framework comprises two modular components: &lt;span style="font-style: italic;"&gt;LevelsExtractor&lt;/span&gt;, which uses Bayesian inference and model selection to denoise and discretize the signal, and &lt;span style="font-style: italic;"&gt;SourcesMapper&lt;/span&gt;, which infers source configurations through probabilistic clustering and optimization. To evaluate performance, we developed a Monte Carlo simulator that generates labeled datasets spanning broad signal-to-noise ratios and source complexities; across 7000 such datasets, &lt;span style="font-style: italic;"&gt;RTNinja&lt;/span&gt; consistently demonstrated high-fidelity signal reconstruction and accurate extraction of source amplitudes and activity patterns. Our results demonstrate that &lt;span style="font-style: italic;"&gt;RTNinja&lt;/span&gt; offers a robust, scalable, and device-agnostic tool for random telegraph noise characterization, enabling large-scale statistical benchmarking, reliability-centric technology qualification, predictive failure modeling, and device physics exploration in next-generation nanoelectronics.&lt;/span&gt;</description><author>APL Machine Learning Current Issue</author><pubDate>Mon, 01 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/046109/3373556/RTNinja-A-generalized-machine-learning-framework</guid></item><item><title>[iScience] A pilot study: Incorporating Treponema pallidum antigens into machine learning models for accurate syphilis treatment outcome assessment</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02536-2?rss=yes</link><description>Health informatics; disease; artificial intelligence applications</description><author>iScience</author><pubDate>Mon, 01 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02536-2?rss=yes</guid></item><item><title>[iScience] Bathymetry of the Philippine sea with convolution neural network from multisource marine geodetic data</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02546-5?rss=yes</link><description>Earth sciences; oceanography; geodesy; machine learning</description><author>iScience</author><pubDate>Fri, 28 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02546-5?rss=yes</guid></item><item><title>[iScience] Interpretable machine learning for urothelial cells classification and risk scoring in urine cytology</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02520-9?rss=yes</link><description>Health sciences; Cancer; Machine learning</description><author>iScience</author><pubDate>Thu, 27 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02520-9?rss=yes</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Machine Learning‐Assisted Second‐Order Perturbation Theory for Chemical Potential Correction Toward Hubbard U Determination</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500160?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Wed, 26 Nov 2025 03:49:32 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500160</guid></item><item><title>[Wiley: Chinese Journal of Chemistry: Table of Contents] Locked Coplanar Conformation Boosts Rapid Electron/Ion Transport in Linear Polyimide Cathodes for Sodium‐Ion Storage</title><link>https://onlinelibrary.wiley.com/doi/10.1002/cjoc.70366?af=R</link><description>Chinese Journal of Chemistry, EarlyView.</description><author>Wiley: Chinese Journal of Chemistry: Table of Contents</author><pubDate>Mon, 24 Nov 2025 07:33:36 GMT</pubDate><guid isPermaLink="true">10.1002/cjoc.70366</guid></item><item><title>[APL Machine Learning Current Issue] A hybrid neural architecture: Online attosecond x-ray characterization</title><link>https://pubs.aip.org/aip/aml/article/3/4/046107/3373060/A-hybrid-neural-architecture-Online-attosecond-x</link><description>&lt;span class="paragraphSection"&gt;The emergence of high-repetition-rate x-ray free-electron lasers (XFELs), such as SLAC’s LCLS-II, serves as our canonical example for autonomous controls that necessitate high-throughput diagnostics paired with streaming computational pipelines capable of single-shot analysis with extremely low latency. We present the deterministic characterization with an integrated parallelizable hybrid resolver architecture, a hybrid machine learning framework designed for fast, accurate analysis of XFEL diagnostics using angular streaking-based sinogram images. This architecture integrates convolutional neural networks and bidirectional long short-term memory models to denoise input, identify x-ray sub-spike features, and extract sub-spike relative delays with sub-30 attosecond temporal resolution. Deployed on low-latency hardware, it achieves over 10 kHz throughput with 168.3 &lt;span style="font-style: italic;"&gt;μ&lt;/span&gt;s inference latency, indicating scalability to 14 kHz with field-programmable gate array integration. By transforming regression tasks into classification problems and leveraging optimized error encoding, we achieve high precision with low-latency performance that is critical for real-time streaming event selection and experimental control feedback signals. This represents a key development in real-time control pipelines for next-generation autonomous science, generally, and high repetition-rate x-ray experiments in particular.&lt;/span&gt;</description><author>APL Machine Learning Current Issue</author><pubDate>Fri, 21 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/046107/3373060/A-hybrid-neural-architecture-Online-attosecond-x</guid></item><item><title>[Joule] Accelerated discovery of CO2-to-C3-hydrocarbon electrocatalysts with human-in-the-loop</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00394-0?rss=yes</link><description>Despite advances in automation and AI, accelerating discovery in heterogeneous electrocatalysts remains hindered by the experimental challenges of building integrated platforms for synthesis and evaluation, as well as limited performance-relevant data. This work integrates accelerated experimentation, machine learning, and domain expertise to efficiently explore CO2-to-C3 electrocatalysts, adding new mechanistic and data-driven insights to energy science.</description><author>Joule</author><pubDate>Fri, 21 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00394-0?rss=yes</guid></item><item><title>[Joule] Redox-mediated solid-state doping of Spiro-OMeTAD for efficient and robust perovskite photovoltaics</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00398-8?rss=yes</link><description>The volatile and unstable nature of conventional dopants severely limits the performance and operational lifetime of perovskite solar cells. Here, we demonstrate a solid-state doping strategy, enabling a uniform nanoscale doping profile while effectively suppressing ion migration. This strategy yields perovskite solar cells with a certified efficiency of 26.34% and high device stability.</description><author>Joule</author><pubDate>Fri, 21 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00398-8?rss=yes</guid></item><item><title>[AI for Science - latest papers] Learning to be simple</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae1d98</link><description>In this work we employ machine learning to understand structured mathematical data involving finite groups and derive a theorem about necessary properties of generators of finite simple groups. We create a database of all two-generated subgroups of the symmetric group on n-objects and conduct a classification of finite simple groups among them using shallow feed-forward neural networks. We show that this neural network classifier can decipher the property of simplicity with varying accuracies depending on the features. Our neural network model leads to a natural conjecture concerning the generators of a finite simple group. We subsequently prove this conjecture. This new toy theorem comments on the necessary properties of generators of finite simple groups. We show this explicitly for a class of sporadic groups for which the result holds. Our work further makes the case for a machine motivated study of algebraic structures in pure mathematics and highlights the possibility of generating new conjectures and theorems in mathematics with the aid of machine learning.</description><author>AI for Science - latest papers</author><pubDate>Thu, 20 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae1d98</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Taguchi–Bayesian Sampling: A Roadmap for Polymer Database Construction Toward Small Representative Machine Learning</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500150?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Wed, 19 Nov 2025 05:00:22 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500150</guid></item><item><title>[iScience] An explainable machine learning model predicts 30-day readmission after vertebral augmentation</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02400-9?rss=yes</link><description>Orthopedics; Machine learning</description><author>iScience</author><pubDate>Wed, 19 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02400-9?rss=yes</guid></item><item><title>[Wiley: SmartMat: Table of Contents] Machine Learning Design of Soft Magnetoresponsive Materials With Multidirectional Fields</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smm2.70051?af=R</link><description>SmartMat, Volume 6, Issue 6, December 2025.</description><author>Wiley: SmartMat: Table of Contents</author><pubDate>Tue, 18 Nov 2025 08:00:00 GMT</pubDate><guid isPermaLink="true">10.1002/smm2.70051</guid></item><item><title>[RSC - Digital Discovery latest articles] Advancing metal organic framework and covalent organic framework design via the digital-intelligent paradigm</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00401B</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00401B" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2026, Advance Article&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00401B, Review Article&lt;/div&gt;&lt;div&gt;&lt;img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /&gt; Open Access&lt;/div&gt;&lt;div&gt;&lt;a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt; &lt;img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution-NonCommercial 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Bing Ma, Na Qin, Qianqian Yan, Wei Zhou, Sheng Zhang, Xiao Wang, Lipiao Bao, Xing Lu&lt;br /&gt;AI and machine learning combined with multiscale simulations accelerate framework materials design. This review summarizes AI-assisted strategies for synthesis prediction, condition optimization, and inverse functional design.&lt;br /&gt;To cite this article before page numbers are assigned, use the DOI form of citation above.&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Tue, 18 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00401B</guid></item><item><title>[Applied Physics Reviews Current Issue] Synergistic integration of metasurfaces and quantum photonics: Pathways to next-generation technologies</title><link>https://pubs.aip.org/aip/apr/article/12/4/041318/3372590/Synergistic-integration-of-metasurfaces-and</link><description>&lt;span class="paragraphSection"&gt;The convergence of metamaterials and quantum optics heralds a transformative era in photonic technologies, poised to revolutionize applications ranging from information processing and imaging to sensing and beyond. This review explores the synergistic integration of metasurfaces—engineered sub-wavelength planar structures—and quantum optics, which exploits quantum mechanical principles to manipulate light at the most granular level. We outline the design principles, fabrication processes, and computational challenges involved in creating quantum metasurfaces, discussing both forward and inverse design approaches. Advances in nanofabrication and intelligent optimization techniques, such as machine learning and topology optimization, have enabled the development of metasurfaces with unparalleled control over electromagnetic waves. We examine recent progress in using quantum metasurfaces for single-photon and multi-photon generation, quantum imaging, and quantum sensing, showcasing how these innovations achieve unprecedented precision and novel functionalities. Additionally, we highlight the integration of metasurfaces into quantum light manipulation, emphasizing their role in enhancing wavefront shaping and entanglement control. By providing a comprehensive survey of current advancements and future research directions, this review highlights the vast potential of metasurfaces and quantum optics at the crossroads, setting the stage for next-generation technological innovations that will define the forthcoming decade.&lt;/span&gt;</description><author>Applied Physics Reviews Current Issue</author><pubDate>Mon, 17 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/12/4/041318/3372590/Synergistic-integration-of-metasurfaces-and</guid></item><item><title>[AI for Science - latest papers] Universal machine learning potentials for systems with reduced dimensionality</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae1208</link><description>We present a benchmark designed to evaluate the predictive capabilities of universal machine learning interatomic potentials (MLIPs) across systems of varying dimensionality. Specifically, our benchmark tests zero- (molecules, atomic clusters, etc), one- (nanowires, nanoribbons, nanotubes, etc), two- (atomic layers and slabs) and three-dimensional (3D) (bulk materials) compounds. The benchmark reveals that while all tested models demonstrate excellent performance for 3D systems, accuracy degrades progressively for lower-dimensional structures. The best performing models for geometry optimization are orbital version 2, equiformerV2, and the equivariant Smooth Energy Network, with the equivariant Smooth Energy Network also providing the most accurate energies. Our results indicate that the best models yield, on average, errors in the atomic positions in the range of 0.01–0.02 Å and errors in the energy below 10 meV atom−1 across all dimensionalities. These results demonstrate that state-of-the-art universal MLIPs have reached sufficient accuracy to serve as direct replacements for density functional theory calculations, at a small fraction of the computational cost, in simulations spanning the full range from isolated atoms to bulk solids. More significantly, the best performing models already enable efficient simulations of complex systems containing subsystems of mixed dimensionality, opening new possibilities for modeling realistic materials and interfaces.</description><author>AI for Science - latest papers</author><pubDate>Mon, 17 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae1208</guid></item><item><title>[Cell Reports Physical Science] Conjugated polyelectrolyte-aptamer hybrid for organic-electrochemical-transistor-based sensing</title><link>https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00564-8?rss=yes</link><description>Jiang et al. present biofunctionalized organic mixed ionic-electronic conductors (OMIECs), specifically single-component materials that integrate high specificity with semiconducting properties, exemplified by p(NDI-T-ZI/EG)-aptamer. This hybrid design enables covalent attachment of diverse functional units, thereby expanding the library of sensory OMIECs for future diagnostic applications.</description><author>Cell Reports Physical Science</author><pubDate>Mon, 17 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00564-8?rss=yes</guid></item><item><title>[Cell Reports Physical Science] CatBench framework for benchmarking machine learning interatomic potentials in adsorption energy predictions for heterogeneous catalysis</title><link>https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00567-3?rss=yes</link><description>Moon et al. introduce CatBench, a framework that systematically evaluates machine learning interatomic potentials for predicting molecular adsorption from small to large molecules on catalyst surfaces. Testing 13 state-of-the-art models across ≥47,000 reactions, they identify optimal accuracy-speed trade-offs and provide quantitative guidance for selecting models for practical catalyst discovery.</description><author>Cell Reports Physical Science</author><pubDate>Mon, 17 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00567-3?rss=yes</guid></item><item><title>[Wiley: ENERGY &amp; ENVIRONMENTAL MATERIALS: Table of Contents] Liquid‐Phase Synthesis of Halide Solid Electrolytes for All‐Solid‐State Batteries Using Organic Solvents</title><link>https://onlinelibrary.wiley.com/doi/10.1002/eem2.70184?af=R</link><description>ENERGY &amp;amp;ENVIRONMENTAL MATERIALS, EarlyView.</description><author>Wiley: ENERGY &amp; ENVIRONMENTAL MATERIALS: Table of Contents</author><pubDate>Fri, 14 Nov 2025 14:05:17 GMT</pubDate><guid isPermaLink="true">10.1002/eem2.70184</guid></item><item><title>[AI for Science - latest papers] TorchSim: an efficient atomistic simulation engine in PyTorch</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae1799</link><description>We introduce TorchSim, an open-source atomistic simulation engine tailored for the machine learned interatomic potential (MLIP) era. By rewriting core atomistic simulation primitives in PyTorch, TorchSim can achieve orders of magnitude acceleration for popular MLIPs. Unlike existing molecular dynamics (MD) packages, which simulate one system at a time, TorchSim performs batched simulations that efficiently utilize modern GPUs by evolving multiple systems concurrently. TorchSim supports MD integrators, structural relaxation optimizers, both machine-learned and classical interatomic potentials (such as Lennard–Jones, Morse, soft-sphere), batching with automatic memory management, differentiable simulation, and integration with popular materials informatics tools.</description><author>AI for Science - latest papers</author><pubDate>Fri, 14 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae1799</guid></item><item><title>[AI for Science - latest papers] Graph learning metallic glass discovery from Wikipedia</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae1b20</link><description>Synthesizing new materials efficiently is highly demanded in various research fields. However, this process is usually slow and expensive, especially for metallic glasses, whose formation strongly depends on the optimal combinations of multiple elements to resist crystallization. This constraint renders only several thousands of candidates explored in the vast material space since 1960. Recently, data-driven approaches armed by advanced machine learning techniques provided alternative routes for intelligent materials design. Due to data scarcity and immature material encoding, the conventional tabular data is usually mined by statistical learning algorithms, giving limited model predictability and generalizability. Here, we propose sophisticated data learning from material network representations. The node elements are encoded from the Wikipedia by a language model. Graph neural networks with versatile architectures are designed to serve as recommendation systems to explore hidden relationships among materials. By employing Wikipedia embeddings from different languages, we assess the capability of natural languages in materials design. Our study proposes a new paradigm to harvesting new amorphous materials and beyond with artificial intelligence.</description><author>AI for Science - latest papers</author><pubDate>Fri, 14 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae1b20</guid></item><item><title>[ACS Materials Au: Latest Articles (ACS Publications)] [ASAP] Molecular Modeling-Based Machine Learning for Accurate Prediction of Gas Diffusivity and Permeability in Metal–Organic Frameworks</title><link>http://dx.doi.org/10.1021/acsmaterialsau.5c00111</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsmaterialsau.5c00111/asset/images/medium/mg5c00111_0007.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;ACS Materials Au&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acsmaterialsau.5c00111&lt;/div&gt;</description><author>ACS Materials Au: Latest Articles (ACS Publications)</author><pubDate>Wed, 12 Nov 2025 18:15:35 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsmaterialsau.5c00111</guid></item><item><title>[Recent Articles in PRX Energy] Dynamic Vacancy Levels in ${\mathrm{Cs}\mathrm{Pb}\mathrm{Cl}}_{3}$ Obey Equilibrium Defect Thermodynamics</title><link>http://link.aps.org/doi/10.1103/dxmb-8s96</link><description>Author(s): Irea Mosquera-Lois and Aron Walsh&lt;br /&gt;&lt;p&gt;This study of halide perovskites uses advanced molecular dynamics simulations with machine learning force fields to identify dynamic defect levels and their impact on the material’s optoelectronic properties.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/dxmb-8s96.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 043008] Published Wed Nov 12, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Wed, 12 Nov 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/dxmb-8s96</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Accelerating Primary Screening of USP8 Inhibitors from Drug Repurposing Databases with Tree‐Based Machine Learning</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500108?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Tue, 11 Nov 2025 04:16:54 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500108</guid></item><item><title>[Joule] Entropy-guided discovery of denary trirutile antimonates for electrocatalytic chlorine evolution</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00381-2?rss=yes</link><description>Entropy-guided trirutile antimonates are developed as highly efficient catalysts for the chlorine evolution reaction. By integrating machine learning, DFT calculations, and operando experiments, this work uncovers atomic-level mechanisms governing catalytic activity and stability.</description><author>Joule</author><pubDate>Tue, 11 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00381-2?rss=yes</guid></item><item><title>[AI for Science - latest papers] Surface stability modeling with universal machine learning interatomic potentials: a comprehensive cleavage energy benchmarking study</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae1408</link><description>Machine learning interatomic potentials (MLIPs) have revolutionized computational materials science by bridging the gap between quantum mechanical accuracy and classical simulation efficiency, enabling unprecedented exploration of materials properties across the periodic table. Despite their remarkable success in predicting bulk properties, no systematic evaluation has assessed how well these universal MLIPs (uMLIPs) can predict cleavage energies, a critical property governing fracture, catalysis, surface stability, and interfacial phenomena. Here, we present a comprehensive benchmark of 19 state-of-the-art uMLIPs for cleavage energy prediction using our previously established density functional theory database of 36 718 slab structures spanning elemental, binary, and ternary metallic compounds. We evaluate diverse architectural paradigms, analyzing their performance across chemical compositions, crystal systems, thickness, and surface orientations. Our zero-shot evaluation results reveal that training data composition dominates architectural sophistication: models trained on the Open Materials 2024 (OMat24) dataset, which emphasizes non-equilibrium configurations, achieve mean absolute percentage errors below 6% and correctly identify the thermodynamically most stable surface terminations in 87% of cases, without any explicit surface energy training. In contrast, architecturally identical models trained on equilibrium-only datasets show five-fold higher errors, while models trained on surface-adsorbate data fail catastrophically with a 17-fold degradation. Remarkably, simpler architectures trained on appropriate data achieve comparable accuracy to complex transformers while offering 10–100× computational speedup. These findings fundamentally reframe MLIP development priorities: highlighting that strategic training-data generation with appropriate non-equilibrium sampling deserves equal or greater attention than architectural complexity.</description><author>AI for Science - latest papers</author><pubDate>Fri, 07 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae1408</guid></item><item><title>[Joule] Li–Si compound anodes enabling high-performance all-solid-state Li-ion batteries</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00376-9?rss=yes</link><description>Li–Si compound anodes, exemplified by Li2.33Si, overcome the degradation issues of conventional Si anodes by combining high ionic and electronic conductivity, favorable mechanical properties, and a negligible-volume-change Li-storage mechanism (Li2.33 + αSi, 0 &lt; α &lt; 0.92), thereby enabling high areal capacity, long cycle life, and fast rate capability in all-solid-state Li-ion batteries.</description><author>Joule</author><pubDate>Fri, 07 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00376-9?rss=yes</guid></item><item><title>[ACS Physical Chemistry Au: Latest Articles (ACS Publications)] [ASAP] Machine Learning as a Method for Retrieving Pressure Values by Analyzing Spectral Line Parameters: The Hydrochloric Acid Case</title><link>http://dx.doi.org/10.1021/acsphyschemau.5c00097</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsphyschemau.5c00097/asset/images/medium/pg5c00097_0010.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;ACS Physical Chemistry Au&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acsphyschemau.5c00097&lt;/div&gt;</description><author>ACS Physical Chemistry Au: Latest Articles (ACS Publications)</author><pubDate>Tue, 04 Nov 2025 19:09:10 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsphyschemau.5c00097</guid></item><item><title>[Applied Physics Reviews Current Issue] Dynamic landscape of chemiresistive breathomic nanosensors based on fifth-generation chips for complex disease diagnosis and healthcare monitoring</title><link>https://pubs.aip.org/aip/apr/article/12/4/041311/3371043/Dynamic-landscape-of-chemiresistive-breathomic</link><description>&lt;span class="paragraphSection"&gt;The growing global population is overwhelming the existing medical infrastructure, demanding a pressing need for the advancement of early-stage and point-of-care disease diagnostics. Conventional techniques are mostly invasive, time-consuming, expensive, sophisticated, and centered at urban facilities. Moreover, they are unable to address the biological complexities related to critical diseases, disorders, and pandemics, resulting in associated high morbidity and mortality. To address this gap, miniaturized fifth-generation sensing chips provide alternatives in terms of accessibility, affordability, and adaptability, being point-of-care and minimally invasive diagnostics. In this context, Breathomic chips based on nanoscale semiconductors have shown their potential for noninvasive, personalized, and on-site operation, offering the capability to identify volatile organic compounds/gases as disease biomarkers from exhaled breath and enabling early disease detection. However, the practical implementation of these sensors in real-time medical contexts remains challenging due to factors including the lack of clinical trials, dedicated data analysis, understanding of the complexities, public awareness, scalability, and accessibility. This comprehensive review critically summarizes the landscape of breath biomarkers detecting fifth-generation chemiresistive chips for human disease diagnosis, methodically outlining associated challenges, alternative strategies, and prospects for clinical implementations and commercial advancement. It details the biological origins of biomarkers, the diverse sensing modalities, and the underlying mechanisms pertaining to breathomic biomarker diagnosis. Furthermore, it highlights the integration of digital-age technologies, including nanotechnology, artificial intelligence, bioinformatics, and machine learning, for high-performance breathomic chips. These next-generation smart sensory chips have the potential to revolutionize medical healthcare facilities, improving patient outcomes, understanding prognosis, and aiding the UN's sustainable development goals.&lt;/span&gt;</description><author>Applied Physics Reviews Current Issue</author><pubDate>Tue, 04 Nov 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/12/4/041311/3371043/Dynamic-landscape-of-chemiresistive-breathomic</guid></item><item><title>[tandf: Materials Research Letters: Table of Contents] Machine learning-assisted design of strong and ductile BCC high-entropy alloys</title><link>https://www.tandfonline.com/doi/full/10.1080/21663831.2025.2577751?af=R</link><description>Volume 13, Issue 12, December 2025, Page 1260-1268&lt;br /&gt;. &lt;br /&gt;</description><author>tandf: Materials Research Letters: Table of Contents</author><pubDate>Thu, 30 Oct 2025 12:22:23 GMT</pubDate><guid isPermaLink="true">/doi/full/10.1080/21663831.2025.2577751?af=R</guid></item><item><title>[Wiley: InfoMat: Table of Contents] Delicate design of lithium‐ion bridges in hybrid solid electrolyte for wide‐temperature adaptive solid‐state lithium metal batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/inf2.70095?af=R</link><description>InfoMat, EarlyView.</description><author>Wiley: InfoMat: Table of Contents</author><pubDate>Wed, 29 Oct 2025 00:36:10 GMT</pubDate><guid isPermaLink="true">10.1002/inf2.70095</guid></item><item><title>[APL Machine Learning Current Issue] Building an affordable self-driving lab: Practical machine learning experiments for physics education using Internet-of-Things</title><link>https://pubs.aip.org/aip/aml/article/3/4/046105/3369966/Building-an-affordable-self-driving-lab-Practical</link><description>&lt;span class="paragraphSection"&gt;Machine learning (ML) is transforming modern physics research, but practical, hands-on experience with ML techniques remains limited due to cost and complexity barriers. To address this gap, we introduce an affordable, autonomous, Internet-of-Things (IoT)-enabled experimental platform designed specifically for applied physics education. Utilizing an Arduino microcontroller, a customizable multi-wavelength light emitting diode array, and photosensors, our setup generates diverse, real-time optical datasets ideal for training and evaluating foundational ML algorithms, including traversal methods, Bayesian inference, and deep learning. The platform facilitates a closed-loop, self-driving experimental workflow, encompassing automated data collection, preprocessing, model training, and validation. Through systematic performance comparisons, we demonstrate the superior ability of deep learning to capture complex nonlinear relationships compared to traversal and Bayesian methods. At ∼$60, this open-source IoT platform provides an accessible, practical pathway for students to master advanced ML concepts, promoting deeper conceptual insights and essential technical skills required for the next generation of physicists and engineers.&lt;/span&gt;</description><author>APL Machine Learning Current Issue</author><pubDate>Wed, 29 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/046105/3369966/Building-an-affordable-self-driving-lab-Practical</guid></item><item><title>[APL Machine Learning Current Issue] Data integration and data fusion approaches in self-driving labs: A perspective</title><link>https://pubs.aip.org/aip/aml/article/3/4/040901/3369947/Data-integration-and-data-fusion-approaches-in</link><description>&lt;span class="paragraphSection"&gt;Self-driving laboratories (SDLs) are transforming materials discovery by combining automation, machine learning, and real-time feedback. Yet, their success depends on robust data integration and fusion methods capable of handling materials data that are heterogeneous, sparse, and multi-scale. Such data span theoretical models, simulations, and experimental techniques across diverse spatial and temporal scales, creating significant challenges for interoperability and analysis. This perspective reviews the state-of-the-art techniques, including knowledge graphs, structured pipelines, multimodal machine learning, and physics-informed models, that are enabling materials science and SDLs to unify and learn from disparate data sources, identify critical challenges, and propose forward-looking directions to enhance data readiness, interoperability, and predictive power in SDLs. We also highlight emerging methods such as transformer architectures, zero-shot learning, and real-time stream processing, and discuss the critical need for more scalable, interpretable, and adaptive solutions to fully realize autonomous materials innovation. By mapping out both the current landscape and future opportunities, we argue that next-generation data integration and fusion are not just enablers but essential pillars for achieving fully autonomous, adaptive, and intelligent SDL systems capable of addressing the complexities of hierarchical and multifunctional materials.&lt;/span&gt;</description><author>APL Machine Learning Current Issue</author><pubDate>Wed, 29 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/040901/3369947/Data-integration-and-data-fusion-approaches-in</guid></item><item><title>[Applied Physics Reviews Current Issue] Strain engineering of van Hove singularity and coupled itinerant ferromagnetism in quasi-2D oxide superlattices</title><link>https://pubs.aip.org/aip/apr/article/12/4/041406/3369932/Strain-engineering-of-van-Hove-singularity-and</link><description>&lt;span class="paragraphSection"&gt;Engineering van Hove singularities (vHss) near the Fermi level, if feasible, offers a powerful route to control exotic quantum phases in electronic and magnetic behaviors. However, conventional approaches rely primarily on chemical and electrical doping and focus mainly on local electrical or optical measurements, limiting their applicability to coupled functionalities. In this study, a vHs-induced insulator-metal transition coupled with a ferromagnetic phase transition was empirically achieved in atomically designed quasi-2D SrRuO&lt;sub&gt;3&lt;/sub&gt; (SRO) superlattices via epitaxial strain engineering, which has not been observed in conventional 3D SRO systems. Theoretical calculations revealed that epitaxial strain effectively modulates the strength and energy positions of vHs of specific Ru orbitals, driving correlated phase transitions in the electronic and magnetic ground states. X-ray absorption spectroscopy confirmed the anisotropic electronic structure of quasi-2D SRO modulated by epitaxial strain. Magneto-optic Kerr effect and electrical transport measurements demonstrated modulated magnetic and electronic phases. Furthermore, magneto-electrical measurements detected significant anomalous Hall effect signals and ferromagnetic magnetoresistance, indicating the presence of magnetically coupled charge carriers in the 2D metallic regime. This study establishes strain engineering as a promising platform for tuning vHss and resultant itinerant ferromagnetism of low-dimensional correlated quantum systems.&lt;/span&gt;</description><author>Applied Physics Reviews Current Issue</author><pubDate>Tue, 28 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/12/4/041406/3369932/Strain-engineering-of-van-Hove-singularity-and</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Machine Learning‐Enhanced Random Matrix Theory Design for Human Immunodeficiency Virus Vaccine Development</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500124?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Mon, 27 Oct 2025 03:21:44 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500124</guid></item><item><title>[Applied Physics Reviews Current Issue] 3D-printed lithium-metal batteries: Multiscale architectures, hybrid technologies, and monolithic integration for next-generation energy storage</title><link>https://pubs.aip.org/aip/apr/article/12/4/041310/3369005/3D-printed-lithium-metal-batteries-Multiscale</link><description>&lt;span class="paragraphSection"&gt;Lithium-metal anodes, with their unmatched theoretical capacity (3860 mAh g&lt;sup&gt;−1&lt;/sup&gt;) and ultra-low electrochemical potential (−3.04 V vs standard hydrogen electrode), are pivotal for next-generation high-energy-density batteries. However, their practical deployment is hindered by persistent challenges—dendritic growth, unstable solid electrolyte interphases (SEIs), and severe volumetric expansion. Emerging as a transformative solution, three-dimensional (3D) printing enables the rational design of multiscale architectures (e.g., micro-lattice anodes and gradient-porous cathodes) and hybrid solid-state electrolytes to address these limitations. This review presents a pioneering synthesis of 3D printing's role in lithium-metal battery engineering, focusing on its capacity to regulate lithium-ion flux, stabilize SEIs, and suppress dendrite proliferation through hierarchical structural control. We systematically analyze four key additive manufacturing technologies (inkjet printing, direct ink writing, fused deposition modeling, and stereolithography), delineating their unique advantages in tailoring ion transport pathways and mechanical robustness. Furthermore, we propose multi-material co-printing strategies to resolve interfacial incompatibilities in monolithic lithium-metal batteries, a critical barrier in current research. By bridging additive manufacturing with electrochemical fundamentals, this work outlines a roadmap to harness 3D printing's full potential, addressing scalability challenges and advancing applications in aerospace, wearables, and biomedical devices where energy density and safety are paramount.&lt;/span&gt;</description><author>Applied Physics Reviews Current Issue</author><pubDate>Thu, 23 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/12/4/041310/3369005/3D-printed-lithium-metal-batteries-Multiscale</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Harnessing Large Language Models to Advance Microbiome Research: From Sequence Analysis to Clinical Applications</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500038?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Tue, 21 Oct 2025 05:48:44 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500038</guid></item><item><title>[AI for Science - latest papers] AInstein: numerical Einstein metrics via machine learning</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae1117</link><description>A new semi-supervised machine learning package is introduced which successfully solves the Euclidean vacuum Einstein equations with a cosmological constant, without any symmetry assumptions. The model architecture contains subnetworks for each patch in the manifold-defining atlas. Each subnetwork predicts the components of a metric in its associated patch, with the relevant Einstein conditions of the form being used as independent loss components (here , where n is the dimension of the Riemannian manifold, and the Einstein constant ). To ensure the consistency of the global structure of the manifold, another loss component is introduced across the patch subnetworks which enforces the coordinate transformation between the patches, , for an appropriate analytically known Jacobian J. We test our method for the case of spheres represented by a pair of patches in dimensions 2, 3, 4, and 5. In dimensions 2 and 3, the geometries have been fully classified. However, it is unknown whether a Ricci-flat metric can exist on spheres in dimensions 4 and 5. This work hints against the existence of such a metric.</description><author>AI for Science - latest papers</author><pubDate>Thu, 16 Oct 2025 23:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae1117</guid></item><item><title>[Matter] SpectroGen: A physically informed generative artificial intelligence for accelerated cross-modality spectroscopic materials characterization</title><link>https://www.cell.com/matter/fulltext/S2590-2385(25)00477-1?rss=yes</link><description>SpectroGen seamlessly couples physics-driven distribution models with a variable autoencoder to generate synthetic spectra indistinguishable from real data. By speeding up high-throughput screening, it closes the gap between AI-based materials discovery and experimental confirmation. Its flexible architecture accommodates diverse spectroscopic techniques, extending its utility across multiple scientific domains. The synergy of rapid AI-driven design and swift AI-enabled characterization expedites validation of innovative materials, bridging lab-based discovery and industry-ready applications to address urgent societal needs.</description><author>Matter</author><pubDate>Tue, 14 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/matter/fulltext/S2590-2385(25)00477-1?rss=yes</guid></item><item><title>[Chem] Precisely modulating Li2CO3 coverage on Ni-rich cathode boosts sulfide solid-state lithium battery performance</title><link>https://www.cell.com/chem/fulltext/S2451-9294(25)00366-3?rss=yes</link><description>It is of great importance to address the issues of stability and charge transfer at the cathode/electrolyte interface in all-solid-state lithium batteries (ASSLBs). We proposed a CO2 atmosphere treatment to precisely modulate Li2CO3 coverage on Ni-rich layered oxide cathodes (NRLOs) with minimal damage, effectively utilizing the advantageous effect while avoiding the harmful effect of surficial Li2CO3 on the interface. This fundamental mechanism offers insights for optimizing the NRLO/sulfide solid electrolyte interface and advancing high-energy-density ASSLBs.</description><author>Chem</author><pubDate>Tue, 14 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/chem/fulltext/S2451-9294(25)00366-3?rss=yes</guid></item><item><title>[Matter] Dynamic pressure mapping of infant cervical spines using a wearable magnetoelastic patch</title><link>https://www.cell.com/matter/fulltext/S2590-2385(25)00529-6?rss=yes</link><description>To enable continuous monitoring of infant cervical motion, we present a kirigami-inspired soft magnetoelastic patch that conforms intimately to the cervical position without compromising comfort. By leveraging passive magnetic sensing and flexible structural design, the patch captures subtle biomechanical changes during cervical movements. Integrated with machine learning classification, it enables intelligent recognition of stress patterns, providing a non-invasive and adaptive solution for early assessment of infant cervical motion.</description><author>Matter</author><pubDate>Fri, 10 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/matter/fulltext/S2590-2385(25)00529-6?rss=yes</guid></item><item><title>[Matter] Machine learning-driven ligand engineering decodes and controls structural distortions in 2D perovskites</title><link>https://www.cell.com/matter/fulltext/S2590-2385(25)00525-9?rss=yes</link><description>This work deciphers how ligand molecular descriptors (i.e., nitrogen content, hydrogen bonding, and π-conjugation) govern structural distortions and optoelectronic properties in 2D perovskites. The authors demonstrate that machine learning can quantitatively correlate these descriptors with octahedral distortions (92.6% prediction accuracy) and enable the targeted synthesis of six new perovskites with tunable band gaps (1.91–2.39 eV). The established structure-property relationships and machine learning-driven design paradigm represent a transformative approach for accelerating the discovery of functional perovskites, bridging computational prediction with experimental validation for optoelectronic applications.</description><author>Matter</author><pubDate>Fri, 10 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/matter/fulltext/S2590-2385(25)00525-9?rss=yes</guid></item><item><title>[Applied Physics Reviews Current Issue] The enduring legacy of scanning spreading resistance microscopy: Overview, advancements, and future directions</title><link>https://pubs.aip.org/aip/apr/article/12/4/041305/3366934/The-enduring-legacy-of-scanning-spreading</link><description>&lt;span class="paragraphSection"&gt;Scanning spreading resistance microscopy (SSRM) has recently celebrated 30 years of existence when counting from the original patent of 1994. In this time, the technique has experienced an incredible journey with substantial evolutions that transformed SSRM from a small-scale experiment into a staple for chip manufacturing laboratories for physical analysis of materials, failure analysis, and process development of integrated circuits. As the nanoelectronics industry is ready for a new inflection point, with the introduction of nanosheet field-effect transistor to replace FinFETs and cell track scaling architectures such as the complementary field-effect transistors, SSRM is once again at a turning point. This review aims to highlight the state-of-the-art while discussing the emerging challenges introduced by the ever-increasing complexity in complementary metal–oxide–semiconductor (CMOS) manufacturing. We start by illustrating the unique capability of the SSRM technique, its origin, and its evolution. Next, we continue by showing the considerable research effort that enabled SSRM to transition to a tomographic sensing method in support of FinFET transistors. Here, the high aspect ratio fin geometry and the complex contacts technology have imposed important modifications to the original method. Later, we elaborate on some of the key challenges introduced by the upcoming device transition from three-sided channel FinFETs into nanosheet FETs, i.e., offering a four-sided electrostatic control of the channel. Finally, we present the use of machine learning for automation in carrier calibration with increased accuracy. We close by introducing some of the concepts that we consider promising for further extension of SSRM to obtain sub-nm structural information and doping profiles in the area of advanced FinFETs and nanosheet FET technologies, including (a) correlative analysis flow, (b) liquid-assisted probing, and (c) top–down and bottom–up multi-probe sensing schemes to merge low- and high-pressure SSRM scans.&lt;/span&gt;</description><author>Applied Physics Reviews Current Issue</author><pubDate>Wed, 08 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apr/article/12/4/041305/3366934/The-enduring-legacy-of-scanning-spreading</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Oxygen Vacancy-Modulated Molybdenum Oxide/Montmorillonite Heterointerface Nanoarchitectures for Plasmon-Enhanced Solar-Osmotic Energy Harvesting</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202506132?af=R</link><description>&lt;p&gt;&lt;img src="https://www.chinesechemsoc.org/cms/asset/762823f9-fd0a-4aff-acde-289220f569a8/keyimage.png" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;CCS Chemistry, Ahead of Print.&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI:10.31635/ccschem.025.202506132&lt;/div&gt;Two-dimensional (2D) nanofluidic membranes with precise ion transport kinetics hold transformative potential for next-generation ion-selective membranes, yet their practical implementation in osmotic energy conversion remains constrained by inherent ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Mon, 06 Oct 2025 03:22:16 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202506132?af=R</guid></item><item><title>[APL Machine Learning Current Issue] Deep learning model of myofilament cooperative activation and cross-bridge cycling in cardiac muscle</title><link>https://pubs.aip.org/aip/aml/article/3/4/046101/3365811/Deep-learning-model-of-myofilament-cooperative</link><description>&lt;span class="paragraphSection"&gt;Cardiac muscle contraction is driven by the cross-bridge cycle, where myosin heads generate force by cyclically attaching to and pulling on actin filaments using energy from ATP. Modeling this process is central to understanding cardiac sarcomere mechanics. In this study, we developed supervised machine learning (ML) models using artificial neural networks (ANNs) to simulate cross-bridge cycling and muscle behavior under isosarcometric, isometric, and isotonic conditions. Trained on synthetic data, the ANN captured nonlinear dependencies among calcium concentration, stiffness, sarcomere length, temperature, and force output. Error analysis through histograms and unity-line scatterplots validated prediction accuracy and identified underfitting and overfitting patterns. Comparisons across ANN architectures showed how hidden layer complexity affects model generalization. The present deep learning models accurately reproduced key physiological behaviors, including steady-state force–Ca&lt;sup&gt;2+&lt;/sup&gt; relations, sarcomere length changes, and force–velocity relations, and matched theoretical results. This work demonstrates the potential of ML tools to enhance cardiac muscle modeling and exploit existing experimental datasets for improved prediction of cardiac muscle diseases.&lt;/span&gt;</description><author>APL Machine Learning Current Issue</author><pubDate>Fri, 03 Oct 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/aml/article/3/4/046101/3365811/Deep-learning-model-of-myofilament-cooperative</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Discovery of Novel Materials with Giant Dielectric Constants via First‐Principles Phonon Calculations and Machine Learning</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500141?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Tue, 30 Sep 2025 06:30:24 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500141</guid></item><item><title>[AI for Science - latest papers] FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae0808</link><description>We introduce a rapid, accurate framework for computing atomic migration barriers in crystals by combining universal machine‐learning force fields (MLFFs) with 3D potential‐energy‐surface sampling and interpolation. Our method suppresses periodic self‐interactions via supercell expansion, builds a continuous potential energy surface (PES) from MLFF energies on a spatial grid, and extracts minimum‐energy pathways without predefined nudged elastic band (NEB) images. For a benchmark set of twelve electrode and electrolyte materials, including LiCoO2, LiFePO4, and Li10GeP2S12, our MLFF‐derived barriers lie within tens of meV of density functional theory (DFT) and experiment values, while achieving a ∼100-fold speedup over standard DFT‐NEB calculations. We benchmark GPTFF, CHGNet, and MACE, showing that fine‐tuning on PBE/PBE + U data further enhances accuracy. Ultimately, we introduce an open‐source package for high‐throughput materials screening and interactive PES visualization.</description><author>AI for Science - latest papers</author><pubDate>Mon, 29 Sep 2025 23:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae0808</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500060?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Wed, 24 Sep 2025 13:21:08 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500060</guid></item><item><title>[Recent Articles in PRX Energy] Reconstructions and Dynamics of $β$-Lithium Thiophosphate Surfaces</title><link>http://link.aps.org/doi/10.1103/5hf9-hlj6</link><description>Author(s): Hanna Türk, Davide Tisi, and Michele Ceriotti&lt;br /&gt;&lt;p&gt;Machine-learning-based molecular dynamics simulations of the solid electrolyte &lt;math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"&gt;&lt;mi&gt;β&lt;/mi&gt;&lt;/math&gt;-Li&lt;math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;/msub&gt;&lt;/math&gt;PS&lt;math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mn&gt;4&lt;/mn&gt;&lt;/msub&gt;&lt;/math&gt; under realistic conditions reveal dynamic surface structure and reactivity.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/5hf9-hlj6.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 033010] Published Tue Aug 26, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Tue, 26 Aug 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/5hf9-hlj6</guid></item><item><title>[Matter] CGformer: Transformer-enhanced crystal graph network with global attention for material property prediction</title><link>https://www.cell.com/matter/fulltext/S2590-2385(25)00423-0?rss=yes</link><description>Designing new materials for better batteries is a major challenge, especially for complex “high-entropy” materials with countless atomic combinations. We developed a novel AI model, CGformer, that looks at the entire crystal structure to accurately predict material properties. By screening nearly 150,000 candidates, our AI identified promising new sodium-ion solid electrolytes. We successfully synthesized these materials, and they showed excellent performance, validating our AI-driven discovery pipeline. This work provides a powerful tool to accelerate the design of next-generation energy materials.</description><author>Matter</author><pubDate>Wed, 20 Aug 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/matter/fulltext/S2590-2385(25)00423-0?rss=yes</guid></item><item><title>[Recent Articles in PRX Energy] Large-Scale Simulation Unveiled Superior Potassium-Based Solid Electrolyte with High Ionic Conductivity and Excellent Electrochemical Stability in ${M}_{5}{\mathrm{YSi}}_{4}{\mathrm{O}}_{12}$ ($M=\mathrm{Li},\mathrm{K}$)</title><link>http://link.aps.org/doi/10.1103/8wkh-238p</link><description>Author(s): Zhao Li, Jiaxiang Li, Congwei Xie, Keith Butler, Fei Du, and Yu Xie&lt;br /&gt;&lt;p&gt;Advanced computational modeling predicts the ionic conductivity and electrochemical stability of a promising potassium-based solid electrolyte. The approach highlights the importance of longer length and time scales during simulations, achievable with machine learning potentials.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/8wkh-238p.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 033007] Published Thu Aug 14, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Thu, 14 Aug 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/8wkh-238p</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Interpretable Machine Learning for Solvent‐Dependent Carrier Mobility in Solution‐Processed Organic Thin Films</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500078?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Fri, 08 Aug 2025 09:54:45 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500078</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Macrophage Phenotype Detection Methodology on Textured Surfaces via Nuclear Morphology Using Machine Learning</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500055?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Fri, 01 Aug 2025 08:40:28 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500055</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500079?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Thu, 24 Jul 2025 10:45:19 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500079</guid></item><item><title>[Recent Articles in PRX Energy] Origin of Intrinsically Low Thermal Conductivity in a Garnet-Type Solid Electrolyte: Linking Lattice and Ionic Dynamics with Thermal Transport</title><link>http://link.aps.org/doi/10.1103/6wj2-kzhh</link><description>Author(s): Yitian Wang, Yaokun Su, Jesús Carrete, Huanyu Zhang, Nan Wu, Yutao Li, Hongze Li, Jiaming He, Youming Xu, Shucheng Guo, Qingan Cai, Douglas L. Abernathy, Travis Williams, Kostiantyn V. Kravchyk, Maksym V. Kovalenko, Georg K.H. Madsen, Chen Li, and Xi Chen&lt;br /&gt;&lt;p&gt;Using a comprehensive experimental and computational approach, this work analyzes the intrinsically low thermal conductivity of solid ionic conductor Li&lt;math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;6&lt;/mn&gt;&lt;mo lspace="0" rspace="0"&gt;.&lt;/mo&gt;&lt;mn&gt;5&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;La&lt;math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;/msub&gt;&lt;/math&gt;Zr&lt;math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;1&lt;/mn&gt;&lt;mo lspace="0" rspace="0"&gt;.&lt;/mo&gt;&lt;mn&gt;5&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;Ta&lt;math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo lspace="0" rspace="0"&gt;.&lt;/mo&gt;&lt;mn&gt;5&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;O&lt;math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mn&gt;12&lt;/mn&gt;&lt;/msub&gt;&lt;/math&gt;, a promising electrolyte for all-solid-state batteries.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/6wj2-kzhh.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 033004] Published Thu Jul 17, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Thu, 17 Jul 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/6wj2-kzhh</guid></item><item><title>[Recent Articles in PRX Energy] A Comparative Study of Solid Electrolyte Interphase Evolution in Ether and Ester-Based Electrolytes for $\mathrm{Na}$-ion Batteries</title><link>http://link.aps.org/doi/10.1103/jfvb-wp5w</link><description>Author(s): Liang Zhao, Sara I.R. Costa, Yue Chen, Jack R. Fitzpatrick, Andrew J. Naylor, Oleg Kolosov, and Nuria Tapia-Ruiz&lt;br /&gt;&lt;p&gt;Diglyme-based electrolytes promote a thin, uniform, and stable solid electrolyte interphase that can extend the lifespan of sodium-ion batteries, as shown using advanced spectroscopic and electrochemical techniques.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/jfvb-wp5w.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 033002] Published Tue Jul 15, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Tue, 15 Jul 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/jfvb-wp5w</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Autonomous Machine Learning‐Based Classification and Arrangement of Submillimeter Objects Using a Capillary Force Gripper</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500068?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Wed, 09 Jul 2025 08:01:30 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500068</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500031?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Wed, 09 Jul 2025 07:56:18 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500031</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500074?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Fri, 27 Jun 2025 08:27:19 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500074</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500022?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Fri, 27 Jun 2025 08:15:35 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500022</guid></item><item><title>[ACS Materials Au: Latest Articles (ACS Publications)] [ASAP] Historical Data Mining Deep Dive into Machine Learning-Aided 2D Materials Research in Electrochemical Applications</title><link>http://dx.doi.org/10.1021/acsmaterialsau.5c00030</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsmaterialsau.5c00030/asset/images/medium/mg5c00030_0020.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;ACS Materials Au&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acsmaterialsau.5c00030&lt;/div&gt;</description><author>ACS Materials Au: Latest Articles (ACS Publications)</author><pubDate>Mon, 23 Jun 2025 15:22:16 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsmaterialsau.5c00030</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Machine Learning‐Assisted Infectious Disease Detection in Low‐Income Areas: Toward Rapid Triage of Dengue and Zika Virus Using Open‐Source Hardware</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500049?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Mon, 23 Jun 2025 08:20:28 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500049</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500033?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Fri, 20 Jun 2025 08:36:19 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500033</guid></item><item><title>[Wiley: Advanced Intelligent Discovery: Table of Contents] Predicting High‐Resolution Spatial and Spectral Features in Mass Spectrometry Imaging with Machine Learning and Multimodal Data Fusion</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aidi.202500021?af=R</link><description>Advanced Intelligent Discovery, EarlyView.</description><author>Wiley: Advanced Intelligent Discovery: Table of Contents</author><pubDate>Wed, 18 Jun 2025 08:10:58 GMT</pubDate><guid isPermaLink="true">10.1002/aidi.202500021</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Efficient Pure Organic Near-Infrared Room-Temperature Phosphorescence Based on n/π Orbital Decoupling</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202405319?af=R</link><description>&lt;p&gt;&lt;img src="https://www.chinesechemsoc.org/cms/asset/91960c98-a47a-4293-846c-f1e9b670d740/keyimage.jpg" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;CCS Chemistry, Ahead of Print.&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI:10.31635/ccschem.025.202405319&lt;/div&gt;Developing pure organic room-temperature phosphorescence (RTP) materials remains an enormous challenge, especially for efficient near-infrared (NIR) RTP materials. Herein, a functional unit combination strategy is employed to design a series of pure ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Sat, 14 Jun 2025 05:08:51 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202405319?af=R</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Identification of Cu-N2 Sites for Zn-Air Batteries in Harsh Electrolytes: Computer Virtual Screening, Machine Learning, and Practical Application</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505577?af=R</link><description>&lt;p&gt;&lt;img src="https://www.chinesechemsoc.org/cms/asset/48f68dbe-4a5c-4ae6-a581-ca6b1854a4fe/keyimage.jpg" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;CCS Chemistry, Ahead of Print.&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI:10.31635/ccschem.025.202505577&lt;/div&gt;Nonprecious-metal catalysts possess great potential to replace noble metals in catalysis; however, selecting efficient candidates through experiments is time-consuming and costly. Herein, we employed a data-driven virtual screening (VS) method to discover ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Sat, 14 Jun 2025 04:39:17 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505577?af=R</guid></item><item><title>[Recent Articles in PRX Energy] Correlating Local Morphology and Charge Dynamics via Kelvin Probe Force Microscopy to Explain Photoelectrode Performance</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.023010</link><description>Author(s): Maryam Pourmahdavi, Mauricio Schieda, Ragle Raudsepp, Steffen Fengler, Jiri Kollmann, Yvonne Pieper, Thomas Dittrich, Thomas Klassen, and Francesca M. Toma&lt;br /&gt;&lt;p&gt;Charge transport in photoelectrodes for photoelectrochemical cells is influenced by microstructural variations; here, the authors use Kelvin Probe Force Microscopy to correlate local morphology with optoelectronic properties toward optimizing materials toward material optimization.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.023010.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 023010] Published Mon Jun 09, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Mon, 09 Jun 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.023010</guid></item><item><title>[Chinese Chemical Society: CCS Chemistry: Table of Contents] Minimal Sulfur-Grafted Graphite Anode with Accelerated Interfacial Kinetics for Fast-Charging Lithium-Ion Batteries</title><link>https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505705?af=R</link><description>&lt;p&gt;&lt;img src="https://www.chinesechemsoc.org/cms/asset/3b36947a-a194-4971-8441-76ab0db62773/keyimage.png" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;CCS Chemistry, Ahead of Print.&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI:10.31635/ccschem.025.202505705&lt;/div&gt;The significant capacity decay and undesired metallic Li plating of graphite anode resulting from sluggish Li-ion diffusion kinetics at the graphite/electrolyte interface have largely hindered the fast-charging capability of lithium-ion batteries (LIBs). ...</description><author>Chinese Chemical Society: CCS Chemistry: Table of Contents</author><pubDate>Wed, 28 May 2025 08:32:07 GMT</pubDate><guid isPermaLink="true">https://ccs-stag.literatumonline.com/doi/abs/10.31635/ccschem.025.202505705?af=R</guid></item><item><title>[Recent Articles in PRX Energy] Resolving the Solvation Structure and Transport Properties of Aqueous Zinc Electrolytes from Salt-in-Water to Water-in-Salt Using Neural Network Potential</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.023004</link><description>Author(s): Chuntian Cao, Arun Kingan, Ryan C. Hill, Jason Kuang, Lei Wang, Chunyi Zhang, Matthew R. Carbone, Hubertus van Dam, Shinjae Yoo, Shan Yan, Esther S. Takeuchi, Kenneth J. Takeuchi, Xifan Wu, AM Milinda Abeykoon, Amy C. Marschilok, and Deyu Lu&lt;br /&gt;&lt;p&gt;A neural network potential model is developed for ZnCl&lt;math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/msub&gt;&lt;/math&gt; electrolytes that provides atomic scale insights into the solvation structure and ionic conductivity. The results agree well with experiment and shed light on the performance of aqueous zinc-ion batteries across a wide concentration range of ZnCl&lt;math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/msub&gt;&lt;/math&gt; electrolytes.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.023004.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 023004] Published Fri Apr 11, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Fri, 11 Apr 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.023004</guid></item><item><title>[Recent Articles in PRX Energy] Crystal Structure Prediction and Phase Stability in Highly Anharmonic Silver-Based Chalcohalide Antiperovskites</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.023002</link><description>Author(s): Pol Benítez, Cibrán López, Cong Liu, Ivan Caño, Josep-Lluís Tamarit, Edgardo Saucedo, and Claudio Cazorla&lt;br /&gt;&lt;p&gt;Using theoretical first-principles methods, stable and metastable phases of silver-based chalcohalide anti-perovskites are predicted, offering insight into their stability for potential energy and optoelectronic applications.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.023002.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 023002] Published Thu Apr 03, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Thu, 03 Apr 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.023002</guid></item><item><title>[Recent Articles in PRX Energy] Unraveling Temperature-Induced Vacancy Clustering in Tungsten: From Direct Microscopy to Atomistic Insights via Data-Driven Bayesian Sampling</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.013008</link><description>Author(s): Anruo Zhong, Clovis Lapointe, Alexandra M. Goryaeva, Kazuto Arakawa, Manuel Athènes, and Mihai-Cosmin Marinica&lt;br /&gt;&lt;p&gt;This study reveals how anharmonic, entropy-driven stabilization of di-vacancies at elevated temperatures reconcile theoretical predictions with experimental observations of vacancy clustering in tungsten, a prime candidate material for fusion reactors.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.013008.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 013008] Published Tue Feb 25, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Tue, 25 Feb 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.013008</guid></item><item><title>[Recent Articles in PRX Energy] Constant-Current Nonequilibrium Molecular Dynamics Approach for Accelerated Computation of Ionic Conductivity Including Ion-Ion Correlation</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.013005</link><description>Author(s): Ryoma Sasaki, Yoshitaka Tateyama, and Debra J. Searles&lt;br /&gt;&lt;p&gt;A new “constant-current” nonequilibrium molecular dynamics simulation method accelerates ionic conductivity calculations by up to two orders of magnitude while accurately capturing ion-ion correlations, enabling more efficient screening of solid electrolytes and revealing important low-temperature conduction behaviors.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.013005.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 013005] Published Wed Feb 19, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Wed, 19 Feb 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.013005</guid></item><item><title>[Recent Articles in PRX Energy] Detecting Attacks and Estimating States of Power Grids from Partial Observations with Machine Learning</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.013003</link><description>Author(s): Zheng-Meng Zhai, Mohammadamin Moradi, and Ying-Cheng Lai&lt;br /&gt;&lt;p&gt;Using data-driven machine learning models, this work demonstrates a method to map the dynamical state of a full power grid from limited observations, enabling the user to locate disruptions with information only from other areas of the grid.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.013003.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 013003] Published Tue Feb 04, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Tue, 04 Feb 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.013003</guid></item><item><title>[Recent Articles in PRX Energy] 3D Reconstruction of a Nuclear Reactor by Muon Tomography: Structure Validation and Anomaly Detection</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.013002</link><description>Author(s): Baptiste Lefevre, Julien Vogel, Héctor Gomez, David Attié, Laurent Gallego, Philippe Gonzales, Bertrand Lesage, Philippe Mas, and Daniel Pomarède&lt;br /&gt;&lt;p&gt;Muon tomography, a non-invasive technique that can be used to image large, inaccessible structures, is combined with machine learning to create a 3D reconstruction of a historical nuclear reactor that reveals material density variations, including potential anomalies in the graphite core.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.013002.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 013002] Published Tue Jan 28, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Tue, 28 Jan 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.013002</guid></item><item><title>[Recent Articles in PRX Energy] Determining Parameters of Metal-Halide Perovskites Using Photoluminescence with Bayesian Inference</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.4.013001</link><description>Author(s): Manuel Kober-Czerny, Akash Dasgupta, Seongrok Seo, Florine M. Rombach, David P. McMeekin, Heon Jin, and Henry J. Snaith&lt;br /&gt;&lt;p&gt;Experiment and machine learning are combined to extract key material parameters and insight into charge carrier transport in metal halide perovskites for solar cell applications.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.4.013001.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 4, 013001] Published Tue Jan 14, 2025</description><author>Recent Articles in PRX Energy</author><pubDate>Tue, 14 Jan 2025 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.4.013001</guid></item><item><title>[Recent Articles in PRX Energy] Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.3.023006</link><description>Author(s): Hengrui Zhang (张恒睿), Tianxing Lai (来天行), Jie Chen, Arumugam Manthiram, James M. Rondinelli, and Wei Chen&lt;br /&gt;&lt;p&gt;MolSets, a machine learning model that integrates graph neural networks with permutation invariant architecture, addresses multilevel complexity for effective prediction of molecular mixture properties, thus accelerating lithium battery electrolyte design.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.3.023006.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 3, 023006] Published Wed Jun 12, 2024</description><author>Recent Articles in PRX Energy</author><pubDate>Wed, 12 Jun 2024 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.3.023006</guid></item><item><title>[Recent Articles in PRX Energy] Temperature Impact on Lithium Metal Morphology in Lithium Reservoir-Free Solid-State Batteries</title><link>http://link.aps.org/doi/10.1103/PRXEnergy.3.023003</link><description>Author(s): Min-Gi Jeong, Kelsey B. Hatzell, Sourim Banerjee, Bairav S. Vishnugopi, and Partha P. Mukherjee&lt;br /&gt;&lt;p&gt;Toward enhancing performance in reservoir-free solid-state batteries, confocal imaging experiments are combined with meso-scale modeling to unveil vertical and horizontal growth mechanisms at varying temperatures of lithium metal at an agyrodite solid electrolyte|stainless steel interface.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/PRXENERGY/key_images/10.1103/PRXEnergy.3.023003.png" width="200" /&gt;&lt;br /&gt;[PRX Energy 3, 023003] Published Fri May 17, 2024</description><author>Recent Articles in PRX Energy</author><pubDate>Fri, 17 May 2024 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PRXEnergy.3.023003</guid></item><item><title>[Recent Articles in Rev. Mod. Phys.] &lt;i&gt;Colloquium&lt;/i&gt;: Advances in automation of quantum dot devices control</title><link>http://link.aps.org/doi/10.1103/RevModPhys.95.011006</link><description>Author(s): Justyna P. Zwolak and Jacob M. Taylor&lt;br /&gt;&lt;p&gt;A promising platform for quantum computing consists of arrays of quantum dots. However, operating these devices presents a challenging control problem, since the location of the dots and the charges they contain must be reliably and reproducibly matched with the gate voltages. This Colloquium explains how automated control protocols that make use of machine learning techniques can succeed in systems where heuristic control is not feasible.&lt;/p&gt;&lt;img height="" src="https://cdn.journals.aps.org/journals/RMP/key_images/10.1103/RevModPhys.95.011006.png" width="200" /&gt;&lt;br /&gt;[Rev. Mod. Phys. 95, 011006] Published Fri Feb 17, 2023</description><author>Recent Articles in Rev. Mod. Phys.</author><pubDate>Fri, 17 Feb 2023 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/RevModPhys.95.011006</guid></item><item><title>[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Hydrogen as promoter and inhibitor of superionicity: A case study on Li-N-H systems</title><link>http://link.aps.org/doi/10.1103/PhysRevB.82.024304</link><description>Author(s): Andreas Blomqvist, C. Moysés Araújo, Ralph H. Scheicher, Pornjuk Srepusharawoot, Wen Li, Ping Chen, and Rajeev Ahuja&lt;br /&gt;&lt;p&gt;Materials which possess a high lithium ion conductivity are very attractive for battery and fuel cell applications. Hydrogenation of the fast-ion conductor lithium nitride $({\text{Li}}_{3}\text{N})$ leads to the formation of lithium imide $({\text{Li}}_{2}\text{NH})$ and subsequently of lithium ami…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 82, 024304] Published Mon Jul 26, 2010</description><author>PRB: Dynamics, dynamical systems, lattice effects, quantum solids</author><pubDate>Mon, 26 Jul 2010 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PhysRevB.82.024304</guid></item><item><title>[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Nonadiabatic effects of rattling phonons and $4f$ excitations in $\text{Pr}{({\text{Os}}_{1−x}{\text{Ru}}_{x})}_{4}{\text{Sb}}_{12}$</title><link>http://link.aps.org/doi/10.1103/PhysRevB.81.224305</link><description>Author(s): Peter Thalmeier&lt;br /&gt;&lt;p&gt;In the skutterudite compounds the anharmonic “rattling” oscillations of $4f$-guest ions in the surrounding ${\text{Sb}}_{12}$ host cages are found to have significant influence on the low-temperature properties. Recently specific-heat analysis of $\text{Pr}{({\text{Os}}_{1−x}{\text{Ru}}_{x})}_{4}{\t…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 81, 224305] Published Fri Jun 18, 2010</description><author>PRB: Dynamics, dynamical systems, lattice effects, quantum solids</author><pubDate>Fri, 18 Jun 2010 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PhysRevB.81.224305</guid></item><item><title>[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Ionic conductivity of nanocrystalline yttria-stabilized zirconia: Grain boundary and size effects</title><link>http://link.aps.org/doi/10.1103/PhysRevB.81.184301</link><description>Author(s): O. J. Durá, M. A. López de la Torre, L. Vázquez, J. Chaboy, R. Boada, A. Rivera-Calzada, J. Santamaria, and C. Leon&lt;br /&gt;&lt;p&gt;We report on the effect of grain size on the ionic conductivity of yttria-stabilized zirconia samples synthesized by ball milling. Complex impedance measurements, as a function of temperature and frequency are performed on $10\text{ }\text{mol}\text{ }\mathrm{%}$ yttria-stabilized zirconia nanocryst…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 81, 184301] Published Mon May 10, 2010</description><author>PRB: Dynamics, dynamical systems, lattice effects, quantum solids</author><pubDate>Mon, 10 May 2010 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PhysRevB.81.184301</guid></item><item><title>[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Calculating the anharmonic free energy from first principles</title><link>http://link.aps.org/doi/10.1103/PhysRevB.81.172301</link><description>Author(s): Zhongqing Wu&lt;br /&gt;&lt;p&gt;We developed a method to calculate the anharmonic free energy without requiring any adjustable parameter. The requisite computations are first-principles quasiharmonic calculations plus an additional Canonical (NVT) ensemble first-principles molecular-dynamics simulation and, therefore, are affordab…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 81, 172301] Published Fri May 07, 2010</description><author>PRB: Dynamics, dynamical systems, lattice effects, quantum solids</author><pubDate>Fri, 07 May 2010 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PhysRevB.81.172301</guid></item><item><title>[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Phason dynamics in one-dimensional lattices</title><link>http://link.aps.org/doi/10.1103/PhysRevB.81.064302</link><description>Author(s): Hansjörg Lipp, Michael Engel, Steffen Sonntag, and Hans-Rainer Trebin&lt;br /&gt;&lt;p&gt;In quasicrystals, the phason degree of freedom and the inherent anharmonic potentials lead to complex dynamics, which cannot be described by the usual phonon modes of motion. We have constructed simple one-dimensional model systems, the dynamic Fibonacci chain, and approximants thereof. They allow u…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 81, 064302] Published Thu Feb 25, 2010</description><author>PRB: Dynamics, dynamical systems, lattice effects, quantum solids</author><pubDate>Thu, 25 Feb 2010 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PhysRevB.81.064302</guid></item><item><title>[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] &lt;i&gt;Ab initio&lt;/i&gt; construction of interatomic potentials for uranium dioxide across all interatomic distances</title><link>http://link.aps.org/doi/10.1103/PhysRevB.80.174302</link><description>Author(s): P. Tiwary, A. van de Walle, and N. Grønbech-Jensen&lt;br /&gt;&lt;p&gt;We provide a methodology for generating interatomic potentials for use in classical molecular-dynamics simulations of atomistic phenomena occurring at energy scales ranging from lattice vibrations to crystal defects to high-energy collisions. A rigorous method to objectively determine the shape of a…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 80, 174302] Published Wed Nov 25, 2009</description><author>PRB: Dynamics, dynamical systems, lattice effects, quantum solids</author><pubDate>Wed, 25 Nov 2009 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PhysRevB.80.174302</guid></item><item><title>[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] One-dimensional nanostructure-guided chain reactions: Harmonic and anharmonic interactions</title><link>http://link.aps.org/doi/10.1103/PhysRevB.80.174301</link><description>Author(s): Nitish Nair and Michael S. Strano&lt;br /&gt;&lt;p&gt;We have performed a parametric study of self-propagating chain reactions along a one-dimensional bead-spring array. The coupling between beads is modeled using harmonic and anharmonic Fermi-Pasta-Ulam (FPU)-$β$ and ${φ}^{4}$ potentials. The parameters that define the system are the activation energy…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 80, 174301] Published Fri Nov 13, 2009</description><author>PRB: Dynamics, dynamical systems, lattice effects, quantum solids</author><pubDate>Fri, 13 Nov 2009 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/PhysRevB.80.174301</guid></item></channel></rss>
Reference in New Issue View Git Blame Copy Permalink