diff --git a/filtered_feed.xml b/filtered_feed.xml
index 8af5a86..2ddcb5e 100644
--- a/filtered_feed.xml
+++ b/filtered_feed.xml
@@ -1,5 +1,5 @@
 <?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>Wed, 07 Jan 2026 06:33:34 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[cond-mat updates on arXiv.org] A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse design</title><link>https://arxiv.org/abs/2601.02424</link><description>arXiv:2601.02424v1 Announce Type: new 
+<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/"><channel><title>My Customized Papers</title><link>https://github.com/your_username/your_repo</link><description>Aggregated research papers</description><language>en-US</language><lastBuildDate>Wed, 07 Jan 2026 12:44:01 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Outside Back Cover: Rhodopsin‐Mimicking Reversible Photo‐Switchable Chloride Channels Based on Azobenzene‐Appended Semiaza‐Bambusurils for Light‐Controlled Ion Transport and Cancer Cell Apoptosis</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.2025-m0501054600?af=R</link><description>Angewandte Chemie International Edition, EarlyView.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Wed, 07 Jan 2026 05:23:27 GMT</pubDate><guid isPermaLink="true">10.1002/anie.2025-m0501054600</guid></item><item><title>[Wiley: ENERGY &amp; ENVIRONMENTAL MATERIALS: Table of Contents] Thermodynamic Mechanisms of Co‐S Bond Anchoring in Few‐Layered 1T‐MoS2 for Enhanced Capacitive Performance via Spin State Regulation and Ion Diffusion Kinetics</title><link>https://onlinelibrary.wiley.com/doi/10.1002/eem2.70218?af=R</link><description>ENERGY &amp;amp;ENVIRONMENTAL MATERIALS, EarlyView.</description><author>Wiley: ENERGY &amp; ENVIRONMENTAL MATERIALS: Table of Contents</author><pubDate>Wed, 07 Jan 2026 05:20:14 GMT</pubDate><guid isPermaLink="true">10.1002/eem2.70218</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Customizing Ion Transport by Anionphilic Nanofiber‐Polymer Electrolyte for Stable Zinc Metal Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202519057?af=R</link><description>Advanced Materials, EarlyView.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Wed, 07 Jan 2026 05:17:00 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202519057</guid></item><item><title>[cond-mat updates on arXiv.org] A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse design</title><link>https://arxiv.org/abs/2601.02424</link><description>arXiv:2601.02424v1 Announce Type: new 
 Abstract: The synthesis of nanocrystals has been highly dependent on trial-and-error, due to the complex correlation between synthesis parameters and physicochemical properties. Although deep learning offers a potential methodology to achieve generative inverse design, it is still hindered by the scarcity of high-quality datasets that align nanocrystal synthesis routes with their properties. Here, we present the construction of a large-scale, aligned Nanocrystal Synthesis-Property (NSP) database and demonstrate its capability for generative inverse design. To extract structured synthesis routes and their corresponding product properties from literature, we develop NanoExtractor, a large language model (LLM) enhanced by well-designed augmentation strategies. NanoExtractor is validated against human experts, achieving a weighted average score of 88% on the test set, significantly outperforming chemistry-specialized (3%) and general-purpose LLMs (38%). The resulting NSP database contains nearly 160,000 aligned entries and serves as training data for our NanoDesigner, an LLM for inverse synthesis design. The generative capability of NanoDesigner is validated through the successful design of viable synthesis routes for both well-established PbSe nanocrystals and rarely reported MgF2 nanocrystals. Notably, the model recommends a counter-intuitive, non-stoichiometric precursor ratio (1:1) for MgF2 nanocrystals, which is experimentally confirmed as critical for suppressing byproducts. Our work bridges the gap between unstructured literature and data-driven synthesis, and also establishes a powerful human-AI collaborative paradigm for accelerating nanocrystal discovery.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.02424v1</guid></item><item><title>[cond-mat updates on arXiv.org] Protein-Water Energy Transfer via Anharmonic Low-Frequency Vibrations</title><link>https://arxiv.org/abs/2601.02699</link><description>arXiv:2601.02699v1 Announce Type: new 
 Abstract: Heat dissipation is ubiquitous in living systems, which constantly convert distinct forms of energy into each other. The transport of thermal energy in liquids and even within proteins is well understood but kinetic energy transfer across a heterogeneous molecular boundary provides additional challenges. Here, we use atomistic molecular dynamics simulations under steady-state conditions to analyze how a protein dissipates surplus thermal energy into the surrounding solvent. We specifically focus on collective degrees of freedom that govern the dynamics of the system from the diffusive regime to mid-infrared frequencies. Using a fully anharmonic analysis of molecular vibrations, we analyzed their vibrational spectra, temperatures, and heat transport efficiencies. We find that the most efficient energy transfer mechanisms are associated with solvent-mediated friction. However, this mechanism only applies to a small number of degrees of freedom of a protein. Instead, less efficient vibrational energy transfer in the far-infrared dominates heat transfer overall due to a large number of vibrations in this frequency range. A notable by-product of this work is a highly sensitive measure of deviations from energy equi-partition in equilibrium systems, which can be used to analyze non-ergodic properties.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.02699v1</guid></item><item><title>[cond-mat updates on arXiv.org] Interplay of Structure and Dynamics in Solid Polymer Electrolytes: a Molecular Dynamics Study of LiPF6/polypropylene carbonate</title><link>https://arxiv.org/abs/2601.02869</link><description>arXiv:2601.02869v1 Announce Type: new 
 Abstract: Solid-state batteries (SSB) are emerging as next-generation electrochemical energy storage devices. Achieving high energy density in SSB relies on solid polymer electrolytes (SPE) that are electrochemically stable against both lithium metal and high-potential positive electrodes, two conditions that are difficult to satisfy without chemical degradation. In this work, molecular dynamics simulations are employed to investigate the relationship between structure and dynamics in carbonate-based SPE composed of polypropylene carbonate and lithium hexafluorophosphate (LiPF$_6$), at salt concentrations ranging from 0.32 to 1.21 mol$/$kg. Structural properties are analyzed under ambient pressure at the experimentally relevant temperature $T = 353$ K. Since the slow dynamical processes governing ion transport in these systems are inaccessible to direct molecular dynamics, transport properties are simulated at elevated temperatures up to 900 K and extrapolated to $T = 353$ K using Arrhenius behavior. The results reveal strong ionic correlations, a limited fraction of free ions, and a predominance of negatively charged clusters, especially at high salt concentration. At high temperature, the self-diffusion coefficient of Li$^+$ exceeds that of PF$_6^-$ due to weaker Li$^+$-carbonate and ion-ion interactions. However, at $T = 353$ K, Li$^+$ mobility becomes lower than that of the anion, consistent with typical experimental observations in SPE. As expected, the ionic conductivity $\sigma$ increases with temperature, while at $T = 353$ K it exhibits a maximum for salt concentrations between 1.0 and 1.1 mol$/$kg. Overall, the estimated physico-chemical parameters highlight the key role of ion correlations in SPE and suggest strategies to optimize electrolyte performance. The Arrhenius extrapolation approach used here provides valuable insight into ion transport mechanisms in solid polymer electrolytes.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.02869v1</guid></item><item><title>[cond-mat updates on arXiv.org] DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations</title><link>https://arxiv.org/abs/2601.02938</link><description>arXiv:2601.02938v1 Announce Type: new 
@@ -9,7 +9,7 @@ Abstract: Ion exchange kinetic flux equations have been extensively investigated
 Abstract: H-theorem provides a microscopic foundation of the Second Law of Thermodynamics and is therefore essential to establishing statistical physics, but at the same time, H-theorem has been subject to controversy that in part persists till this day. To better understand H-theorem and its relation to the arrow of time, we study the equilibration of randomly oriented and positioned hard disks with periodic boundary conditions. Using a model based on the DeepSets architecture, which imposes permutation invariance of the particle labels, we train a model to capture the irreversibility of the H-functional.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2508.14003v3</guid></item><item><title>[cond-mat updates on arXiv.org] Bloch oscillations of helicoidal spin-orbit coupled Bose-Einstein condensates in deep optical lattices</title><link>https://arxiv.org/abs/2509.14873</link><description>arXiv:2509.14873v2 Announce Type: replace 
 Abstract: We consider helicoidal spin-orbit coupled Bose-Einstein condensates in deep optical lattice and study the dynamics of Bloch oscillation. We show that the variation of helicoidal gauge potential with spin-orbit coupling is different in zero-momentum and plane-wave phases. The characteristics of Bloch oscillation are different in the two phases. In the zero-momentum phase, the Bloch oscillation is harmonic while it is anharmonic in the plane-wave phase. The amplitude of Bloch oscillation is found to be affected by the helicoidal gauge potential and spin-orbit coupling. We examine that the decay of Bloch oscillation caused by mean-field interaction can be managed by helicoidal spin-orbit coupling.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2509.14873v2</guid></item><item><title>[cond-mat updates on arXiv.org] Tuning Separator Chemistry: Improving Zn Anode Compatibility via Functionalized Chitin Nanofibers</title><link>https://arxiv.org/abs/2512.19449</link><description>arXiv:2512.19449v2 Announce Type: replace 
 Abstract: Aqueous zinc (Zn) batteries (AZBs) face significant challenges due to the limited compatibility of Zn anodes with conventional separators, leading to dendrite growth, hydrogen evolution reaction (HER), and poor cycling stability. While separator design is crucial for optimizing battery performance, its potential remains underexplored. The commonly used glass fiber (GF) filters were not originally designed as battery separators. To address their limitations, nanochitin derived from waste shrimp shells was used to fabricate separators with varying concentrations of amine and carboxylic functional groups. This study investigates how the type and concentration of these groups influence the separator's properties and performance. In a mild acidic electrolyte that protonates the amine groups, the results showed that the density of both ammonium and carboxylic groups in the separators significantly affected water structure and ionic conductivity. Quasi-Elastic Neutron Scattering (QENS) revealed that low-functionalized chitin, particularly with only ammonium groups, promotes strongly bound water with restricted mobility, thereby enhancing Zn plating and stripping kinetics. These separators exhibit exceptional Zn stability over 2000 hours at low current densities (0.5 mA/cm2), maintaining low overpotentials and stable polarization. Additionally, the full cell consisting of Zn||NaV3O8.1.5H2O showed a cycle life of over 2000 cycles at 2 A/g, demonstrating the compatibility of the nanochitin-based separators with low concentrations of functional surface groups. These results demonstrate the importance of a simple separator design for improving the overall performance of AZBs.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.19449v2</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:2512.24822v2 Announce Type: replace-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>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.24822v2</guid></item><item><title>[Nature Communications] Thermotropic liquid-assisted interface management enables efficient and stable perovskite solar cells and modules</title><link>https://www.nature.com/articles/s41467-025-68231-0</link><description>&lt;p&gt;Nature Communications, Published online: 07 January 2026; &lt;a href="https://www.nature.com/articles/s41467-025-68231-0"&gt;doi:10.1038/s41467-025-68231-0&lt;/a&gt;&lt;/p&gt;In this work, Chang et al. report a thermotropic liquid additive for perovskite solar cells that enables dynamic interface management, simultaneously passivating defects and suppressing ion migration to deliver high efficiency and substantially enhanced operational stability.</description><author>Nature Communications</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41467-025-68231-0</guid></item><item><title>[ChemRxiv] A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-zd9mr-v6?rft_dat=source%3Ddrss</link><description>Computational blind challenges offer critical, unbiased assessment opportunities to assess and accelerate scientific progress, as demonstrated by a breadth of breakthroughs over the last decade. We report the outcomes and key insights from an open science community blind challenge focused on computational methods in drug discovery, using lead optimization data from the AI-driven Structure-enabled Antiviral Platform (ASAP) Discovery Consortium’s pan-coronavirus antiviral discovery program, in partnership with Polaris and the OpenADMET project. This collaborative initiative invited global participants from both academia and industry to develop and apply computational methods to predict the biochemical potency and crystallographic ligand poses of small molecules against key coronavirus targets, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) main protease (Mpro), as well as multiple ADMET assay endpoints, using previously undisclosed comprehensive experimental drug discovery datasets as benchmarks. By evaluating submissions across multiple tasks and compounds, we established performance leaderboards and conducted meta-analyses to assess methodological strengths, common pitfalls, and areas for improvement. This analysis provides a foundation for best practices in real-world machine learning evaluation, grounded in community-driven benchmarking. We also highlight how next-generation platforms, such as Polaris, enable rigorous challenge design, embedded evaluation frameworks, and broad community engagement. This paper reports the collective findings of the challenge, offering a high-level overview of the data, evaluation infrastructure, and top- performing strategies. We further provide context and support for the accompanying papers authored by the challenge participants in this special issue, which explore individual approaches in greater depth. Together, these contributions aim to advance reproducible, trustworthy, and high-impact computational methods in drug discovery, and to explore best practices and pitfalls in future blind challenge design and execution, including planned initiatives for the OpenADMET project.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-zd9mr-v6?rft_dat=source%3Ddrss</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Optimizing solid electrolyte interphase with KOTF for dendrites-free and high-performance Lithium Metal Batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25048984?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 20 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Yangtao Zhou, Dequan Huang, Man Zhang, Guangda Yin, Yi Liang, Qichang Pan, Fenghua Zheng, Sijiang Hu, Hongqiang Wang, Qingyu Li&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25048984</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] A hierarchical sandwich Li&lt;sub&gt;6.4&lt;/sub&gt;Ga&lt;sub&gt;0.2&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;/ZIF-8@SiO&lt;sub&gt;2&lt;/sub&gt;/PVDF-HFP heterostructure with high ionic conductivity for dendrite-free solid-state lithium batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25048583?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 20 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Hu Wang, Shala Yang, Pengfei Pang, Jiangchao Chen, Yongbo Yan, Mingjie Liao, Dazhi Pang, Zheqi Zhang, Yunyun Zhao, Wenping Liu, Huarui Xu, Guisheng Zhu, Kunpeng Jiang&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25048583</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Hierarchical rose-like VS&lt;sub&gt;2&lt;/sub&gt; with sulfur vacancies for high-performance all-solid-state lithium-ion batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25050005?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 20 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Peidian Chong, Shijie Yu, Lin Zheng, Lei Zhang, Mingdeng Wei, Hongfei Liu, Yi Ren, Jianbiao Wang&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25050005</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Prediction of Lithium-ion battery states via combination of implantable sensors and machine learning</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25047243?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 20 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Zijun Huang, Feng Tong, Guo Chen, Xuan Chen, Xianjie Xu, Zhefu Mu, Jiaxin Sun, Sheng Huang, Xiuquan Gu&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25047243</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] A review on metal–organic framework-based polymer solid-state electrolytes for energy storage</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25049096?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 20 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Zelong Zhuang, Xiaojin Yang, Jie Cui, Jingwei Liu, Xueming Yang&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25049096</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Descriptor and graph-based molecular representations in prediction of copolymer properties using machine learning</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625008183?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: October 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 264&lt;/p&gt;&lt;p&gt;Author(s): Elaheh Kazemi-Khasragh, Rocío Mercado, Carlos Gonzalez, Maciej Haranczyk&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 06 Jan 2026 12:43:08 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625008183</guid></item><item><title>[Recent Articles in Phys. Rev. B] Signatures of coherent phonon transport in frequency-dependent lattice thermal conductivity</title><link>http://link.aps.org/doi/10.1103/kn91-g9hh</link><description>Author(s): Đorđe Dangić&lt;br /&gt;&lt;p&gt;Thermal transport in highly anharmonic, amorphous, or alloyed materials often deviates from the predictions of conventional phonon-based models. First-principles approaches have introduced a coherent contribution to account for these deviations and to explain ultralow lattice thermal conductivity, b…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 113, 024301] Published Tue Jan 06, 2026</description><author>Recent Articles in Phys. Rev. B</author><pubDate>Tue, 06 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/kn91-g9hh</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Accelerating the Discovery of High‐Conductivity Glass Electrolytes via Machine Learning</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202503813?af=R</link><description>Advanced Energy Materials, EarlyView.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 06 Jan 2026 05:35:12 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202503813</guid></item><item><title>[cond-mat updates on arXiv.org] Physically-Constrained Autoencoder-Assisted Bayesian Optimization for Refinement of High-Dimensional Defect-Sensitive Single Crystalline Structure</title><link>https://arxiv.org/abs/2601.00855</link><description>arXiv:2601.00855v1 Announce Type: new 
+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>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.24822v2</guid></item><item><title>[Nature Communications] Thermotropic liquid-assisted interface management enables efficient and stable perovskite solar cells and modules</title><link>https://www.nature.com/articles/s41467-025-68231-0</link><description>&lt;p&gt;Nature Communications, Published online: 07 January 2026; &lt;a href="https://www.nature.com/articles/s41467-025-68231-0"&gt;doi:10.1038/s41467-025-68231-0&lt;/a&gt;&lt;/p&gt;In this work, Chang et al. report a thermotropic liquid additive for perovskite solar cells that enables dynamic interface management, simultaneously passivating defects and suppressing ion migration to deliver high efficiency and substantially enhanced operational stability.</description><author>Nature Communications</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41467-025-68231-0</guid></item><item><title>[ChemRxiv] A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-zd9mr-v6?rft_dat=source%3Ddrss</link><description>Computational blind challenges offer critical, unbiased assessment opportunities to assess and accelerate scientific progress, as demonstrated by a breadth of breakthroughs over the last decade. We report the outcomes and key insights from an open science community blind challenge focused on computational methods in drug discovery, using lead optimization data from the AI-driven Structure-enabled Antiviral Platform (ASAP) Discovery Consortium’s pan-coronavirus antiviral discovery program, in partnership with Polaris and the OpenADMET project. This collaborative initiative invited global participants from both academia and industry to develop and apply computational methods to predict the biochemical potency and crystallographic ligand poses of small molecules against key coronavirus targets, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) main protease (Mpro), as well as multiple ADMET assay endpoints, using previously undisclosed comprehensive experimental drug discovery datasets as benchmarks. By evaluating submissions across multiple tasks and compounds, we established performance leaderboards and conducted meta-analyses to assess methodological strengths, common pitfalls, and areas for improvement. This analysis provides a foundation for best practices in real-world machine learning evaluation, grounded in community-driven benchmarking. We also highlight how next-generation platforms, such as Polaris, enable rigorous challenge design, embedded evaluation frameworks, and broad community engagement. This paper reports the collective findings of the challenge, offering a high-level overview of the data, evaluation infrastructure, and top- performing strategies. We further provide context and support for the accompanying papers authored by the challenge participants in this special issue, which explore individual approaches in greater depth. Together, these contributions aim to advance reproducible, trustworthy, and high-impact computational methods in drug discovery, and to explore best practices and pitfalls in future blind challenge design and execution, including planned initiatives for the OpenADMET project.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-zd9mr-v6?rft_dat=source%3Ddrss</guid></item><item><title>[Nature Communications] Uncertainty-aware machine learning to predict non-cancer human toxicity for the global chemicals market</title><link>https://www.nature.com/articles/s41467-025-67374-4</link><description>&lt;p&gt;Nature Communications, Published online: 07 January 2026; &lt;a href="https://www.nature.com/articles/s41467-025-67374-4"&gt;doi:10.1038/s41467-025-67374-4&lt;/a&gt;&lt;/p&gt;Uncertainty-aware machine learning models predict human toxicity for more than 100,000 chemicals, highlighting potency and uncertainty hotspots to guide safer use and to focus efforts to improve prediction confidence.</description><author>Nature Communications</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41467-025-67374-4</guid></item><item><title>[ChemRxiv] Learning EXAFS from atomic structure through physics-informed machine learning</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-sv3f6?rft_dat=source%3Ddrss</link><description>Extended X-ray absorption fine structure (EXAFS) provides element-specific access to local atomic environments and is widely used to relate structure and reactivity across chemical systems. However, quantitative EXAFS interpretation still relies on manually constructed structural models and extensive parameter tuning, creating a growing bottleneck as experimental datasets increase in size and complexity. Addressing this bottleneck requires a direct and systematic mapping between atomic structure and EXAFS response. Here we introduce AI-EXAFS, a physics-informed graph neural network that predicts full EXAFS spectra directly from three-dimensional atomic coordinates. By formulating the learning problem around the physical principles governing EXAFS signal formation, the model learns transferable structure–spectrum relationships and eliminates the need for user-defined parameter selection at inference. Trained on 86,000 transition-metal complexes, AI-EXAFS reproduces reference theoretical spectra with accuracy consistent with established EXAFS analysis practice and generalizes to experimentally relevant systems, including platinum single-atom catalysts. AI-EXAFS provides an accurate and readily deployable forward model for EXAFS, enabling standardized first-pass structural screening and offering a scalable foundation for future extensions toward more realistic and data-rich EXAFS analysis.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-sv3f6?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Defined by Shape: Elucidating the Molecular Recognition of Dynamic Loops with Covalent Ligands</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-qwc85?rft_dat=source%3Ddrss</link><description>Protein loops harness conformational heterogeneity to perform an array of functions, ranging from catalyzing enzymatic reactions to communicating allosteric signals. Although attractive targets for small molecule modulation, these functional hubs are often considered unligandable due to their lack of well-defined binding pockets and highly dynamic structure. Recent studies, however, have demonstrated the power of covalent chemistry to selectively capture cryptic pockets formed by protein loops. Herein, we leverage machine learning to elucidate the molecular basis of covalent ligand:loop recognition in the transcriptional coactivator Med25. Key to our success was classification by ligand shape prior to model training, which led to descriptive and predictive models. The models were experimentally validated through the synthesis and in vitro testing of novel top-ranked ligands, revealing canonical structure-affinity relationships, including an activity cliff. Further feature analyses identified traditional topological and spatial parameters predictive of binding, and molecular modeling uncovered a potential binding pocket with at least two distinct conformations with high shape complementarity. Collectively, these findings reveal the hidden potential of dynamic loops as specific sites for covalent small molecule modulation, challenging the notion that protein loops are unligandable and demonstrating their capacity for exquisite, shape-based molecular recognition.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-qwc85?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] QuantumPDB: A Workflow for High-Throughput Quantum Cluster Model Generation from Protein Structures</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-w5x1d?rft_dat=source%3Ddrss</link><description>Computational modeling of enzymes provides molecular-level insight into catalysis, but the preparation of quantum mechanical (QM) calculations starting from experimental structures is a significant bottleneck for high-throughput studies. Automated tools developed to accelerate this process may fail to generalize across distinct active site chemistries and geometries. To overcome these limitations, we present QuantumPDB, a Python package that automates the generation of hierarchical coordination/interaction spheres around an active center to create QM cluster models directly from raw protein structures. The workflow integrates structure cleaning, protonation state assignment, and QM calculation setup. It uses chemically meaningful models constructed from contact-based interaction spheres derived from Voronoi tessellation, enabling accurate representation of complex active site geometries. We provide an overview of our modular code and describe how it may be employed to automate high-throughput protein screening. To demonstrate its utility, we curated a dataset of 989 holo-enzymes from the PDB and performed QM calculations on 1,673 enzyme cluster models of 842 of these enzymes. Analysis of computed properties suggests that enzyme environments simulated with density functional theory consistently modulate substrate charge toward neutrality and reduce the substrate dipole moment. This phenomenon appears to be general, even in cases where the active site consists predominantly of neutral residues. By automating and standardizing multi-sphere QM model construction, QuantumPDB provides a robust platform for large-scale, data-driven investigations of proteins.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-w5x1d?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Generalization and Usability of Co-Folded GPCR–Ligand Complexes: A Physics-Guided Assessment</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-1rkqz?rft_dat=source%3Ddrss</link><description>Deep learning co-folding models for end-to-end protein–ligand structure prediction mark a major advance beyond AlphaFold2, yet their reliability for decision-making in drug discovery remains unclear. Here, we benchmark Boltz, a state-of-the-art co-folding model, using a curated set of ligand-bound human G protein-coupled receptors (GPCRs) from families unseen during training. We find that the receptor backbones are generally predicted with reasonable accuracy, but ligand poses often deviate significantly from experimental structures. We then evaluate physics-based refinement with rigid-receptor (Glide) and induced-fit docking (IFD-MD) methods, which recover more than half of the misplaced ligands to near-experimental accuracy. As conventional evaluations for co-folded structures focus on distance-based metrics such as root-mean-squared deviation (RMSD), which can miss subtle but consequential binding-site errors, we carry out a further assessment of Boltz performance using free-energy perturbation (FEP+), which is both accurate and sensitive to starting-structure quality, on curated congeneric ligand series with known binding affinities that target the GPCRs. A significant fraction of the 14 congeneric series tested in this fashion fail to reproduce experimental binding affinities via FEP+ when employing the Boltz generated complex, even when the binding-site RMSD is low in some cases. IFD-MD rescues these failures and restores retrospective FEP signals to native-like level for all of these series. Together, these results delineate current generalization and usability limits of co-folded GPCR–ligand complexes and motivate a workflow that pairs deep learning predictions with physics-based refinement and validation before high-stakes decisions in drug discovery.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-1rkqz?rft_dat=source%3Ddrss</guid></item><item><title>[Communications Physics] Interpolation-based coordinate descent method for parameterized quantum circuits</title><link>https://www.nature.com/articles/s42005-025-02473-8</link><description>&lt;p&gt;Communications Physics, Published online: 07 January 2026; &lt;a href="https://www.nature.com/articles/s42005-025-02473-8"&gt;doi:10.1038/s42005-025-02473-8&lt;/a&gt;&lt;/p&gt;Parameterized quantum circuits are a common tool in variational quantum algorithms and quantum machine learning. The authors design an interpolation-based coordinate descent method that reconstructs the cost landscape from a few circuit runs and achieves more efficient training than standard gradient and coordinate descent methods in our numerical tests.</description><author>Communications Physics</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s42005-025-02473-8</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Optimizing solid electrolyte interphase with KOTF for dendrites-free and high-performance Lithium Metal Batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25048984?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 20 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Yangtao Zhou, Dequan Huang, Man Zhang, Guangda Yin, Yi Liang, Qichang Pan, Fenghua Zheng, Sijiang Hu, Hongqiang Wang, Qingyu Li&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25048984</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] A hierarchical sandwich Li&lt;sub&gt;6.4&lt;/sub&gt;Ga&lt;sub&gt;0.2&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;/ZIF-8@SiO&lt;sub&gt;2&lt;/sub&gt;/PVDF-HFP heterostructure with high ionic conductivity for dendrite-free solid-state lithium batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25048583?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 20 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Hu Wang, Shala Yang, Pengfei Pang, Jiangchao Chen, Yongbo Yan, Mingjie Liao, Dazhi Pang, Zheqi Zhang, Yunyun Zhao, Wenping Liu, Huarui Xu, Guisheng Zhu, Kunpeng Jiang&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25048583</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Hierarchical rose-like VS&lt;sub&gt;2&lt;/sub&gt; with sulfur vacancies for high-performance all-solid-state lithium-ion batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25050005?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 20 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Peidian Chong, Shijie Yu, Lin Zheng, Lei Zhang, Mingdeng Wei, Hongfei Liu, Yi Ren, Jianbiao Wang&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25050005</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Prediction of Lithium-ion battery states via combination of implantable sensors and machine learning</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25047243?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 20 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Zijun Huang, Feng Tong, Guo Chen, Xuan Chen, Xianjie Xu, Zhefu Mu, Jiaxin Sun, Sheng Huang, Xiuquan Gu&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25047243</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] A review on metal–organic framework-based polymer solid-state electrolytes for energy storage</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25049096?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 20 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Zelong Zhuang, Xiaojin Yang, Jie Cui, Jingwei Liu, Xueming Yang&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25049096</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Descriptor and graph-based molecular representations in prediction of copolymer properties using machine learning</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625008183?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: October 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 264&lt;/p&gt;&lt;p&gt;Author(s): Elaheh Kazemi-Khasragh, Rocío Mercado, Carlos Gonzalez, Maciej Haranczyk&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 06 Jan 2026 12:43:08 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625008183</guid></item><item><title>[Recent Articles in Phys. Rev. B] Signatures of coherent phonon transport in frequency-dependent lattice thermal conductivity</title><link>http://link.aps.org/doi/10.1103/kn91-g9hh</link><description>Author(s): Đorđe Dangić&lt;br /&gt;&lt;p&gt;Thermal transport in highly anharmonic, amorphous, or alloyed materials often deviates from the predictions of conventional phonon-based models. First-principles approaches have introduced a coherent contribution to account for these deviations and to explain ultralow lattice thermal conductivity, b…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 113, 024301] Published Tue Jan 06, 2026</description><author>Recent Articles in Phys. Rev. B</author><pubDate>Tue, 06 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/kn91-g9hh</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Accelerating the Discovery of High‐Conductivity Glass Electrolytes via Machine Learning</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202503813?af=R</link><description>Advanced Energy Materials, EarlyView.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 06 Jan 2026 05:35:12 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202503813</guid></item><item><title>[cond-mat updates on arXiv.org] Physically-Constrained Autoencoder-Assisted Bayesian Optimization for Refinement of High-Dimensional Defect-Sensitive Single Crystalline Structure</title><link>https://arxiv.org/abs/2601.00855</link><description>arXiv:2601.00855v1 Announce Type: new 
 Abstract: Physical properties and functionalities of materials are dictated by global crystal structures as well as local defects. To establish a structure-property relationship, not only the crystallographic symmetry but also quantitative knowledge about defects are required. Here we present a hybrid Machine Learning framework that integrates a physically-constrained variational-autoencoder (pcVAE) with different Bayesian Optimization (BO) methods to systematically accelerate and improve crystal structure refinement with resolution of defects. We chose the pyrochlore structured Ho2Ti2O7 as a model system and employed the GSAS2 package for benchmarking crystallographic parameters from Rietveld refinement. However, the function space of these material systems is highly nonlinear, which limits optimizers like traditional Rietveld refinement, into trapping at local minima. Also, these naive methods don't provide an extensive learning about the overall function space, which is essential for large space, large time consuming explorations to identify various potential regions of interest. Thus, we present the approach of exploring the high Dimensional structure parameters of defect sensitive systems via pretrained pcVAE assisted BO and Sparse Axis Aligned BO. The pcVAE projects high-Dimensional diffraction data consisting of thousands of independently measured diffraction orders into a lowD latent space while enforcing scaling invariance and physical relevance. Then via BO methods, we aim to minimize the L2 norm based chisq errors in the real and latent spaces separately between experimental and simulated diffraction patterns, thereby steering the refinement towards potential optimum crystal structure parameters. We investigated and compared the results among different pcVAE assisted BO, non pcVAE assisted BO, and Rietveld refinement.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.00855v1</guid></item><item><title>[cond-mat updates on arXiv.org] A Chemically Grounded Evaluation Framework for Generative Models in Materials Discovery</title><link>https://arxiv.org/abs/2601.00886</link><description>arXiv:2601.00886v1 Announce Type: new 
 Abstract: Generative models hold great promise for accelerating materials discovery, but their evaluation often overlooks the chemical validity and stability requirements crucial to real-world applications. Density Functional Theory (DFT) simulations are the gold standard for evaluating such properties but are computationally intensive and inaccessible to non-experts. We propose a chemically grounded, user-friendly evaluation framework that integrates DFT-based stability analysis with commonly used machine learning (ML) metrics. Through systematic experiments using both perturbative and generative methods, we demonstrate that conventional ML metrics can misrepresent chemical feasibility. To address this, we propose new insights on robust metrics and highlight the importance of simulation-informed evaluation for developing reliable generative models in materials science.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.00886v1</guid></item><item><title>[cond-mat updates on arXiv.org] Disordered Dynamics in High Dimensions: Connections to Random Matrices and Machine Learning</title><link>https://arxiv.org/abs/2601.01010</link><description>arXiv:2601.01010v1 Announce Type: new 
 Abstract: We provide an overview of high dimensional dynamical systems driven by random matrices, focusing on applications to simple models of learning and generalization in machine learning theory. Using both cavity method arguments and path integrals, we review how the behavior of a coupled infinite dimensional system can be characterized as a stochastic process for each single site of the system. We provide a pedagogical treatment of dynamical mean field theory (DMFT), a framework that can be flexibly applied to these settings. The DMFT single site stochastic process is fully characterized by a set of (two-time) correlation and response functions. For linear time-invariant systems, we illustrate connections between random matrix resolvents and the DMFT response. We demonstrate applications of these ideas to machine learning models such as gradient flow, stochastic gradient descent on random feature models and deep linear networks in the feature learning regime trained on random data. We demonstrate how bias and variance decompositions (analysis of ensembling/bagging etc) can be computed by averaging over subsets of the DMFT noise variables. From our formalism we also investigate how linear systems driven with random non-Hermitian matrices (such as random feature models) can exhibit non-monotonic loss curves with training time, while Hermitian matrices with the matching spectra do not, highlighting a different mechanism for non-monotonicity than small eigenvalues causing instability to label noise. Lastly, we provide asymptotic descriptions of the training and test loss dynamics for randomly initialized deep linear neural networks trained in the feature learning regime with high-dimensional random data. In this case, the time translation invariance structure is lost and the hidden layer weights are characterized as spiked random matrices.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.01010v1</guid></item><item><title>[cond-mat updates on arXiv.org] Predicting Coherent B2 Stability in Ru-Containing Refractory Alloys Through Thermodynamic Elastic Design Maps</title><link>https://arxiv.org/abs/2601.01326</link><description>arXiv:2601.01326v1 Announce Type: new 
@@ -51,7 +51,7 @@ To unify these effects, we introduce a dimensionless Degree of Gelation (DoG), s
 
 This rheology–machine-learning framework reframes lung sealant development from a static materials optimization problem to a controllable, process-driven design strategy. By quantitatively linking applicator-level parameters to failure-relevant mechanical outcomes—airtightness, compliance, and resistance to delamination—it provides a mechanistic and generalizable foundation for the design of injectable hydrogels, bioadhesives, and tissue-interfacing</description><author>ChemRxiv</author><pubDate>Mon, 05 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-zkrvp?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Discovery of β-Sheet Peptide Assembly Codes via an Experimentally Validated Predictive Computational Platform</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-4c5h0?rft_dat=source%3Ddrss</link><description>Deciphering the sequence codes governing ordered peptide assemblies remains challenging due to the need to explore vast sequence space with atomic resolution. Here, we present an experimentally validated computational framework combining hybrid-resolution molecular dynamics and machine learning for the discovery of β-sheet-rich amyloid-forming peptides. Through exhaustive simulations of all 8,000 tripeptides, we demonstrate that the widely used aggregation propensity (AP) is not effective in predicting β-sheet assembly. We introduce Amyloid-Like Tendency (ALT), a metric enabled by our hybrid-resolution simulations that effectively identifies cross-β architectures. Leveraging this physics-informed dataset, we further fine-tuned the Uni-Mol model to efficiently screen 160,000 tetrapeptides. Experimental validation of 46 candidates confirmed a predictive accuracy of ~85%, yielding 26 novel amyloid-forming peptides, including multiple hydrogelators. Mechanistic analysis reveals that specific sidechain stacking and central amino acid identity, beyond generic hydrophobicity, dictate ordered assembly. This establishes a scalable pipeline for the targeted design of functional peptide materials.</description><author>ChemRxiv</author><pubDate>Mon, 05 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-4c5h0?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Continued Challenges in High-Throughput Materials Predictions: MatterGen predicts compounds from the training dataset.</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-mkls8-v2?rft_dat=source%3Ddrss</link><description>High-throughput computational tools and generative AI models aim to revolutionise materials discovery by enabling the rapid prediction of novel inorganic compounds. However, these tools face persistent challenges with modelling compounds where multiple elements occupy the same crystallographic site, often leading to misclassification of known disordered phases as new ordered compounds. Recently, Microsoft revealed MatterGen as a tool for predicting new materials. As a proof of concept, MatterGen was used to predict the novel compound TaCr2O6, which was subsequently synthesised in a disordered form as Ta1/3Cr2/3O2. However, detailed crystallographic analysis, presented in this paper, reveals that this is not a novel compound but is identical to the already known compound Ta1/2Cr1/2O2 reported in 1972 and actually included in MatterGen’s training dataset. These findings underscore the necessity of rigorous human verification in AI-assisted materials research, limiting their use for rapid and large-scale prediction of new materials. While generative models hold great promise, their effectiveness is currently limited by unresolved issues with disorder prediction and dataset validation. Improved integration with crystallographic expertise is essential to realise their full potential.</description><author>ChemRxiv</author><pubDate>Mon, 05 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-mkls8-v2?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Pressure- and Temperature-Dependent Ionic Transport in Ag₄Zr₃S₈ Nanocrystal Pellets</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-t7qrx?rft_dat=source%3Ddrss</link><description>Nanocrystal (NC)–derived solid electrolytes provide access to compositionally complex and metastable ion conductors, yet their measured transport properties are often dominated by extrinsic contact effects. We probe the coupled roles of temperature, uniaxial pressure, pellet microstructure, and electrode material on the electrochemical impedance response of Ag₄Zr₃S₈ NC pellets. Ag₄Zr₃S₈ NCs were synthesized via colloidal routes using distinct sulfur sources and consolidated into pellets with controlled surface chemistry. EIS was performed over 298–393 K and 0.43–8.67 MPa using blocking and non-blocking electrodes. Pressure-dependent Nyquist analysis shows impedance is overwhelmingly dominated by interfacial and constriction resistances, with pressure primarily reducing contact limitations rather than altering intrinsic ion transport. Temperature–pressure heat maps of the high-frequency resistance reveal thermally activated transport strongly modulated by mechanical contact and electrode compatibility. These results establish pressure-resolved impedance spectroscopy as a diagnostic framework for separating intrinsic and extrinsic transport contributions in NC-based solid electrolytes.</description><author>ChemRxiv</author><pubDate>Mon, 05 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-t7qrx?rft_dat=source%3Ddrss</guid></item><item><title>[iScience] Mechanistic Evidence for Dibutyl Phthalate as an Environmental Trigger for Inflammatory Bowel Disease</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02880-9?rss=yes</link><description>Dibutyl phthalate (DBP) is a ubiquitous pollutant, but its molecular link to inflammatory bowel disease (IBD) is undefined. We employed an integrative network toxicology framework, combining DBP target databases with IBD patient transcriptomics to address this gap. A computational pipeline using machine learning and molecular docking predicted a core six-gene signature (KYNU, PCK1, LCN2, CDC25B, EPHB4, SORD). We validated these predictions in human colonic epithelial cells (NCM460). DBP exposure induced a pro-inflammatory state and upregulated the core genes, with LCN2 showing the strongest response.</description><author>iScience</author><pubDate>Mon, 05 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02880-9?rss=yes</guid></item><item><title>[Applied Physics Letters Current Issue] Bidirectional optically modulated In 2 O 3  transistors with inorganic solid electrolyte gating for neuromorphic visual systems</title><link>https://pubs.aip.org/aip/apl/article/128/1/013301/3376081/Bidirectional-optically-modulated-In2O3</link><description>&lt;span class="paragraphSection"&gt;Inspired by retinal visual processing, we demonstrate a bidirectional optically controlled neuromorphic In&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt; transistor based on an inorganic solid electrolyte Li&lt;sub&gt;1+x&lt;/sub&gt;Al&lt;sub&gt;x&lt;/sub&gt;Ti&lt;sub&gt;2-x&lt;/sub&gt;(PO&lt;sub&gt;4&lt;/sub&gt;)&lt;sub&gt;3&lt;/sub&gt; (LATP) gate dielectric. The device exhibits light-controlled bidirectional visual bipolar cell behavior, exhibiting excitatory and inhibitory responses under ultraviolet (275 nm) and green light (520 nm) stimuli, respectively. X-ray photoelectron spectroscopy and capacitance–frequency measurements reveal that mobile Li&lt;sup&gt;+&lt;/sup&gt; ions in the LATP dielectric layer can adsorb electrons and form Coulombic binding states, thereby dynamically modulating photogenerated carrier transport. Optical pulse trains dynamically regulate the channel current, enabling bidirectional optical neural plasticity. Furthermore, a large-area device array was employed for image encoding and retinal damage simulation, highlighting its potential for artificial vision and neuromorphic computing. These findings establish an effective strategy for developing bidirectional optical, reconfigurable, and large-scale integrable neuromorphic devices, providing additional insights into the role of dielectric layer ion dynamics in neuromorphic optoelectronics.&lt;/span&gt;</description><author>Applied Physics Letters Current Issue</author><pubDate>Mon, 05 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apl/article/128/1/013301/3376081/Bidirectional-optically-modulated-In2O3</guid></item><item><title>[ScienceDirect Publication: Acta Materialia] Dual Engine-driven Strategy for Advanced Copper Alloy Design employing Large Language Models</title><link>https://www.sciencedirect.com/science/article/pii/S1359645425011735?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 3 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Acta Materialia&lt;/p&gt;&lt;p&gt;Author(s): Fei Tan, Zixuan Zhao, Yanbin Jiang, Wenchao Zhang, Tong Xie, Wei Chen, Muzhi Ma, Yangfan Liu, Yanpeng Ye, Zhu Xiao, Qian Lei, Guofu Xu, Jie Ren, Yuyuan Zhao, Zhou Li&lt;/p&gt;</description><author>ScienceDirect Publication: Acta Materialia</author><pubDate>Sun, 04 Jan 2026 18:28:43 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1359645425011735</guid></item><item><title>[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Minimally Invasive, Label-Free, Point-of-Care Histopathological Diagnostic Platform of Malignant Tumors of the Female Reproductive System Based on Raman Spectroscopy and Machine Learning</title><link>http://dx.doi.org/10.1021/acs.jpclett.5c03704</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpclett.5c03704/asset/images/medium/jz5c03704_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.5c03704&lt;/div&gt;</description><author>The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)</author><pubDate>Sun, 04 Jan 2026 17:52:36 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpclett.5c03704</guid></item><item><title>[ChemRxiv] Atomistic insights into the chemical stability and ionic transport at Li-metal/Li-Argyrodite interfaces</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-5d9nq?rft_dat=source%3Ddrss</link><description>All solid-state batteries (ASSBs) based on solid-state electrolytes (SSEs) are a novel Li-ion battery technology with the potential of enhanced safety, longer lifetimes, and increased energy density when coupled with the Li-metal anode. Li-Argyrodite (Li6PS5Cl) is a promising SSE with high ionic conductivity, produced using cheap and sustainable precursors, and therefore of interest to both academia and industry. Like many other sulfide-based SSEs, it is however unstable against Li-metal. Using ab-initio and machine-learning methods, we simulate three representative Li-metal/Li-Argyrodite interface models to investigate whether the exact surface termination affects the chemical stability and ion transport capability. We present a systematic approach to create low-energy interfaces by screening 28 low Miller-index surface terminations of Li-argyrodite and coupling them with Li-metal. Custom-made machine-learned interatomic potentials trained on ab-initio data enable the simulation of large interface models with over 2000 atoms for 5 ns. We find that all three interfaces decompose into an amorphous solid-electrolyte interphase (SEI) layer, consisting of Li3P, Li2S and LiCl, which then crystallizes into an antifluorite phase Li2S{1-x-y}P{x}Cl{y}; {x,y = 0.14-0.15}. A two orders of magnitude decrease in Li-ion flux shows that the crystalline SEI layer is a sluggish ion conductor, similar to Li2S. While all three interfaces form the same crystalline SEI layer, the exact rates of the decomposition and crystallisation depend on the actual surface composition. These atomic-level insights could potentially be used to control the SEI formation in sulphide-based SSEs and others.</description><author>ChemRxiv</author><pubDate>Sun, 04 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-5d9nq?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Cellulose Coating Altered the Electro-Chemo-Mechanical Evolution of Sodium Thioantimonate Electrolyte in Solid-state Sodium Batteries: An Operando Raman Study</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-bwt22?rft_dat=source%3Ddrss</link><description>All-solid-state batteries (ASSBs) attracted increasing attention due to their improved safety and energy densities; yet electrolyte decomposition and subsequent contact loss limited the interfacial stability of ASSBs. Herein, we report an operando Raman characterization that provides high voltage, time, and spatial resolutions, which enables simultaneous analysis of interfacial decomposition mechanism and morphological evolution. Using Na3SbS4 electrolyte (NSS) and its carboxymethyl-cellulose-encapsulated analogue (NSS-CMC) as exemplars, we precisely contrasted the subtle differences in the two-step reduction mechanism of the two electrolytes. In both systems, Na3SbS3 formed as an intermediate, and Na3Sb binary as one major final product; while the CMC coating altered the kinetics of Na3SbS3 formation and consumption, and extended the formation potential of Na3Sb from 1.35 V (seen in NSS) to 0.50 V (vs. Na/Na+). Oxidation of NSS and NSS-CMC both occur near 2.20 V, although CMC coating altered the crystallinity of the oxidative products. Simultaneously, we captured phenomena that are unique to solid-state electrochemical systems such as particle relocation, morphological change, and reversed reactions. We inferred CMC’s dual role as a voltage barrier and a mechanical buffer in suppressing the electro-chemo-mechanical decomposition of NSS electrolyte. The deep mechanistic insights unravel the exact modification needed for improved interfacial stability.</description><author>ChemRxiv</author><pubDate>Sun, 04 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-bwt22?rft_dat=source%3Ddrss</guid></item><item><title>[ScienceDirect Publication: Artificial Intelligence Chemistry] Accelerated green material and solvent discovery with chemistry- and physics-guided generative AI</title><link>https://www.sciencedirect.com/science/article/pii/S2949747725000235?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 2 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Artificial Intelligence Chemistry&lt;/p&gt;&lt;p&gt;Author(s): Eslam G. Al-Sakkari, Ahmed Ragab, Marzouk Benali, Olumoye Ajao, Daria C Boffito, Hanane Dagdougui&lt;/p&gt;</description><author>ScienceDirect Publication: Artificial Intelligence Chemistry</author><pubDate>Sat, 03 Jan 2026 12:38:39 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2949747725000235</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Minutes‐Scale Ultrafast Synthesis of New Oxyhalides Solid Electrolytes with Interfacial Ionic Conduction for All‐Solid‐State Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202516259?af=R</link><description>Angewandte Chemie International Edition, Volume 65, Issue 1, 2 January 2026.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Sat, 03 Jan 2026 06:30:47 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202516259</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Potential‐Gated Polymer Integrates Reversible Ion Transport and Storage for solid‐state Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202513365?af=R</link><description>Advanced Materials, Volume 38, Issue 1, 2 January 2026.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Sat, 03 Jan 2026 06:20:51 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202513365</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Generative Artificial Intelligence Navigated Development of Solvents for Next Generation High‐Performance Magnesium Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202510083?af=R</link><description>Advanced Materials, Volume 38, Issue 1, 2 January 2026.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Sat, 03 Jan 2026 06:20:51 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202510083</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Generality‐Driven Optimization of Enantio‐ and Regioselective Mono‐Reduction of 1,2‐Dicarbonyls by High‐Throughput Experimentation and Machine Learning</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202519425?af=R</link><description>Angewandte Chemie International Edition, Volume 65, Issue 1, 2 January 2026.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Sat, 03 Jan 2026 06:15:46 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202519425</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] An All‐Solid‐State Li–Cu Battery via Cuprous/Lithium‐Ion Halide Solid Electrolyte</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202518966?af=R</link><description>Angewandte Chemie International Edition, Volume 65, Issue 1, 2 January 2026.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Sat, 03 Jan 2026 06:15:46 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202518966</guid></item><item><title>[iScience] AI-Driven Routing and Layered Architectures for Intelligent ICT in Nanosensor Networked Systems</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(26)00001-5?rss=yes</link><description>This review examines the emerging integration of nanosensor networks with modern information and communication technologies to address critical needs in healthcare, environmental monitoring, and smart infrastructure. It evaluates how machine learning and artificial intelligence techniques improve data processing, energy management, real-time communication, and scalable system coordination within nanosensor environments. The analysis compares major learning approaches, including supervised, unsupervised, reinforcement, and deep learning methods, and highlights their effectiveness in data routing, anomaly detection, security, and predictive maintenance.</description><author>iScience</author><pubDate>Sat, 03 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(26)00001-5?rss=yes</guid></item><item><title>[ChemRxiv] The growing role of open source software in molecular modeling</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-6n5lz?rft_dat=source%3Ddrss</link><description>The increasing importance and predictive power of modern molecular modeling, driven by physics- and machine learning-based methods, necessitates a new collaborative architecture to replace the isolated, traditional model of software development. The traditional approach often led to redundant engineering effort, high costs, and opaque systems that limit reproducibility, independent scrutiny, and scientific independence.  
 
-This perspective advocates for permissively licensed open source software as a scientific and economic multiplier by reducing the duplication of effort, enabling scientific validation of modeling tools, and frictionless experimentation with new ideas. Coordinated, multi-project consortia, such as Open Force Field, Open Free Energy, OpenFold, and OpenADMET have formed to collaboratively build shared computational infrastructure and release all methods under permissive licenses. The success of these large-scale efforts requires organizational structures that extend beyond code. The Open Molecular Software Foundation (OMSF), a US nonprofit, serves as a domain-specific institutional home and fiscal sponsor. By providing governance, administrative infrastructure, and dedicated research software engineers, OMSF aligns incentives across academic and industrial stakeholders. This framework enables a synergistic ecosystem where projects interoperate to accelerate innovation, eliminate duplication, and ensure long-term software sustainability, thereby creating durable foundations that elevate the entire molecular modeling community.</description><author>ChemRxiv</author><pubDate>Sat, 03 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-6n5lz?rft_dat=source%3Ddrss</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Tracing Lithophilic Sites: In Situ Nanovisualization of Their Migration and Degradation in All-Solid-State Lithium Batteries</title><link>http://dx.doi.org/10.1021/jacs.5c19144</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c19144/asset/images/medium/ja5c19144_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.5c19144&lt;/div&gt;</description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Fri, 02 Jan 2026 13:23:31 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c19144</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Metal−Organic Framework Ion Conductor‐Based Polymer Solid Electrolytes for Long‐Cycle Lithium Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202511014?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 1, 2 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Fri, 02 Jan 2026 11:53:16 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202511014</guid></item><item><title>[Wiley: Small: Table of Contents] Regulating Interface Chemistry to Construct a Stable Solid Electrolyte Interphase for Long‐Life Zinc Metal Anodes</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202511310?af=R</link><description>Small, Volume 22, Issue 1, 2 January 2026.</description><author>Wiley: Small: Table of Contents</author><pubDate>Fri, 02 Jan 2026 11:26:58 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202511310</guid></item><item><title>[Recent Articles in Phys. Rev. Lett.] Common Sublattice-Pure Van Hove Singularities in the Kagome Superconductors $A{\mathrm{V}}_{3}{\mathrm{Sb}}_{5}$ ($A=\mathrm{K}$, Rb, Cs)</title><link>http://link.aps.org/doi/10.1103/njg9-jpkh</link><description>Author(s): Yujie Lan, Yuhao Lei, Congcong Le, Brenden R. Ortiz, Nicholas C. Plumb, Milan Radovic, Xianxin Wu, Ming Shi, Stephen D. Wilson, and Yong Hu&lt;br /&gt;&lt;p&gt;Kagome materials offer a versatile platform for exploring correlated and topological quantum states, where Van Hove singularities (VHSs) play a pivotal role in driving electronic instabilities, exhibiting distinct behaviors depending on electron filling and interaction settings. In the recently disc…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. Lett. 136, 016401] Published Fri Jan 02, 2026</description><author>Recent Articles in Phys. Rev. Lett.</author><pubDate>Fri, 02 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/njg9-jpkh</guid></item><item><title>[Recent Articles in Phys. Rev. Lett.] Half-Quantized Chiral Edge Current in a $C=1/2$ Parity Anomaly State</title><link>http://link.aps.org/doi/10.1103/vxcb-rwbl</link><description>Author(s): Deyi Zhuo, Bomin Zhang, Humian Zhou, Han Tay, Xiaoda Liu, Zhiyuan Xi, Chui-Zhen Chen, and Cui-Zu Chang&lt;br /&gt;&lt;p&gt;A single massive Dirac surface band is predicted to exhibit a half-quantized Hall conductance, a hallmark of the $C=1/2$ parity anomaly state in quantum field theory. Experimental signatures of the $C=1/2$ parity anomaly state have been observed in semimagnetic topological insulator (TI) bilayers, y…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. Lett. 136, 016601] Published Fri Jan 02, 2026</description><author>Recent Articles in Phys. Rev. Lett.</author><pubDate>Fri, 02 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/vxcb-rwbl</guid></item><item><title>[Wiley: ENERGY &amp; ENVIRONMENTAL MATERIALS: Table of Contents] Synergistic Enhancement of Modified‐PVDF Humidity Sensitivity via Chemical Adsorption‐Ionic Conductivity and its Application in Intelligent Powered Air‐Purifying Respirator</title><link>https://onlinelibrary.wiley.com/doi/10.1002/eem2.70119?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 09:41:25 GMT</pubDate><guid isPermaLink="true">10.1002/eem2.70119</guid></item><item><title>[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] In Situ Electric-Field Guided Assembly of Ordered Bilayer Solid Electrolyte Interphase (SEI) Enables High-Current Zinc Metal Anodes</title><link>http://dx.doi.org/10.1021/acs.jpclett.5c03386</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpclett.5c03386/asset/images/medium/jz5c03386_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.5c03386&lt;/div&gt;</description><author>The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)</author><pubDate>Fri, 02 Jan 2026 09:07:52 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpclett.5c03386</guid></item><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>[Joule] Seeing the unseen: Real-time tracking of battery cycling-to-failure via surface strain</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00453-2?rss=yes</link><description>This study proposes a strain-based approach to address passive failures in lithium-ion batteries, which present spontaneous safety risks often indistinguishable from routine degradation using conventional diagnostics. By establishing a strain-failure correlation, we introduce a slope-based threshold and a failure-proximity index to characterize degradation-to-failure transitions. Incorporating strain-informed machine learning, it effectively detects early failure onset and estimates proximity. This scalable approach is suitable for real-time, onboard monitoring, supporting safer and more reliable battery operation.</description><author>Joule</author><pubDate>Fri, 02 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00453-2?rss=yes</guid></item><item><title>[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Understanding and Mitigating Lithium Metal Anode Failure in All-Solid-State Batteries with Inorganic Solid Electrolytes</title><link>http://dx.doi.org/10.1021/acsenergylett.5c03333</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsenergylett.5c03333/asset/images/medium/nz5c03333_0006.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;ACS Energy Letters&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acsenergylett.5c03333&lt;/div&gt;</description><author>ACS Energy Letters: Latest Articles (ACS Publications)</author><pubDate>Thu, 01 Jan 2026 18:39:05 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsenergylett.5c03333</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. 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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; 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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; 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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 
+This perspective advocates for permissively licensed open source software as a scientific and economic multiplier by reducing the duplication of effort, enabling scientific validation of modeling tools, and frictionless experimentation with new ideas. Coordinated, multi-project consortia, such as Open Force Field, Open Free Energy, OpenFold, and OpenADMET have formed to collaboratively build shared computational infrastructure and release all methods under permissive licenses. The success of these large-scale efforts requires organizational structures that extend beyond code. The Open Molecular Software Foundation (OMSF), a US nonprofit, serves as a domain-specific institutional home and fiscal sponsor. By providing governance, administrative infrastructure, and dedicated research software engineers, OMSF aligns incentives across academic and industrial stakeholders. This framework enables a synergistic ecosystem where projects interoperate to accelerate innovation, eliminate duplication, and ensure long-term software sustainability, thereby creating durable foundations that elevate the entire molecular modeling community.</description><author>ChemRxiv</author><pubDate>Sat, 03 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-6n5lz?rft_dat=source%3Ddrss</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Tracing Lithophilic Sites: In Situ Nanovisualization of Their Migration and Degradation in All-Solid-State Lithium Batteries</title><link>http://dx.doi.org/10.1021/jacs.5c19144</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c19144/asset/images/medium/ja5c19144_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.5c19144&lt;/div&gt;</description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Fri, 02 Jan 2026 13:23:31 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c19144</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Metal−Organic Framework Ion Conductor‐Based Polymer Solid Electrolytes for Long‐Cycle Lithium Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202511014?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 1, 2 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Fri, 02 Jan 2026 11:53:16 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202511014</guid></item><item><title>[Wiley: Small: Table of Contents] Regulating Interface Chemistry to Construct a Stable Solid Electrolyte Interphase for Long‐Life Zinc Metal Anodes</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202511310?af=R</link><description>Small, Volume 22, Issue 1, 2 January 2026.</description><author>Wiley: Small: Table of Contents</author><pubDate>Fri, 02 Jan 2026 11:26:58 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202511310</guid></item><item><title>[Recent Articles in Phys. Rev. Lett.] Common Sublattice-Pure Van Hove Singularities in the Kagome Superconductors $A{\mathrm{V}}_{3}{\mathrm{Sb}}_{5}$ ($A=\mathrm{K}$, Rb, Cs)</title><link>http://link.aps.org/doi/10.1103/njg9-jpkh</link><description>Author(s): Yujie Lan, Yuhao Lei, Congcong Le, Brenden R. Ortiz, Nicholas C. Plumb, Milan Radovic, Xianxin Wu, Ming Shi, Stephen D. Wilson, and Yong Hu&lt;br /&gt;&lt;p&gt;Kagome materials offer a versatile platform for exploring correlated and topological quantum states, where Van Hove singularities (VHSs) play a pivotal role in driving electronic instabilities, exhibiting distinct behaviors depending on electron filling and interaction settings. In the recently disc…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. Lett. 136, 016401] Published Fri Jan 02, 2026</description><author>Recent Articles in Phys. Rev. Lett.</author><pubDate>Fri, 02 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/njg9-jpkh</guid></item><item><title>[Recent Articles in Phys. Rev. Lett.] Half-Quantized Chiral Edge Current in a $C=1/2$ Parity Anomaly State</title><link>http://link.aps.org/doi/10.1103/vxcb-rwbl</link><description>Author(s): Deyi Zhuo, Bomin Zhang, Humian Zhou, Han Tay, Xiaoda Liu, Zhiyuan Xi, Chui-Zhen Chen, and Cui-Zu Chang&lt;br /&gt;&lt;p&gt;A single massive Dirac surface band is predicted to exhibit a half-quantized Hall conductance, a hallmark of the $C=1/2$ parity anomaly state in quantum field theory. Experimental signatures of the $C=1/2$ parity anomaly state have been observed in semimagnetic topological insulator (TI) bilayers, y…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. Lett. 136, 016601] Published Fri Jan 02, 2026</description><author>Recent Articles in Phys. Rev. Lett.</author><pubDate>Fri, 02 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/vxcb-rwbl</guid></item><item><title>[Wiley: ENERGY &amp; ENVIRONMENTAL MATERIALS: Table of Contents] Synergistic Enhancement of Modified‐PVDF Humidity Sensitivity via Chemical Adsorption‐Ionic Conductivity and its Application in Intelligent Powered Air‐Purifying Respirator</title><link>https://onlinelibrary.wiley.com/doi/10.1002/eem2.70119?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 09:41:25 GMT</pubDate><guid isPermaLink="true">10.1002/eem2.70119</guid></item><item><title>[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] In Situ Electric-Field Guided Assembly of Ordered Bilayer Solid Electrolyte Interphase (SEI) Enables High-Current Zinc Metal Anodes</title><link>http://dx.doi.org/10.1021/acs.jpclett.5c03386</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpclett.5c03386/asset/images/medium/jz5c03386_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.5c03386&lt;/div&gt;</description><author>The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)</author><pubDate>Fri, 02 Jan 2026 09:07:52 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpclett.5c03386</guid></item><item><title>[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] A neutralizing APOA5 monoclonal antibody reduces amounts of lipoprotein lipase in capillaries and triggers hypertriglyceridemia</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2528664123?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 123, Issue 1, January 2026. &lt;br /&gt;SignificanceApolipoprotein AV (APOA5) reduces plasma triglyceride levels by binding to angiopoietin-like protein 3/8 complex (ANGPTL3/8) and suppressing its ability to block lipoprotein lipase, but our understanding of important APOA5 sequences and how ...</description><author>Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents</author><pubDate>Fri, 02 Jan 2026 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2528664123?af=R</guid></item><item><title>[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Antibody responses to a highly conserved peptide in HCV E2 protein correlate with chronicity or spontaneous clearance of HCV infection</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2522340122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 123, Issue 1, January 2026. &lt;br /&gt;SignificanceChronicity is a hallmark of hepatitis C virus (HCV) infection, often leading to severe liver diseases such as cirrhosis and hepatocellular carcinoma. Although progression to chronicity or spontaneous clearance is believed to be immune mediated ...</description><author>Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents</author><pubDate>Fri, 02 Jan 2026 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2522340122?af=R</guid></item><item><title>[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Interpretable early warnings using machine learning in an online game-experiment</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2503493122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 123, Issue 1, January 2026. &lt;br /&gt;SignificanceCritical transitions can model abrupt regime shifts in socio-ecological systems. While generic early warning signals that apply across systems have been investigated, no universal signal exists. We therefore propose a data-driven and system-...</description><author>Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents</author><pubDate>Fri, 02 Jan 2026 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2503493122?af=R</guid></item><item><title>[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Machine learning reveals hidden dimensions of functional similarity in proteins</title><link>https://www.pnas.org/doi/abs/10.1073/pnas.2524802122?af=R</link><description>Proceedings of the National Academy of Sciences, Volume 123, Issue 1, January 2026. &lt;br /&gt;</description><author>Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents</author><pubDate>Fri, 02 Jan 2026 08:00:00 GMT</pubDate><guid isPermaLink="true">https://www.pnas.org/doi/abs/10.1073/pnas.2524802122?af=R</guid></item><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>[Joule] Seeing the unseen: Real-time tracking of battery cycling-to-failure via surface strain</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00453-2?rss=yes</link><description>This study proposes a strain-based approach to address passive failures in lithium-ion batteries, which present spontaneous safety risks often indistinguishable from routine degradation using conventional diagnostics. By establishing a strain-failure correlation, we introduce a slope-based threshold and a failure-proximity index to characterize degradation-to-failure transitions. Incorporating strain-informed machine learning, it effectively detects early failure onset and estimates proximity. This scalable approach is suitable for real-time, onboard monitoring, supporting safer and more reliable battery operation.</description><author>Joule</author><pubDate>Fri, 02 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00453-2?rss=yes</guid></item><item><title>[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Understanding and Mitigating Lithium Metal Anode Failure in All-Solid-State Batteries with Inorganic Solid Electrolytes</title><link>http://dx.doi.org/10.1021/acsenergylett.5c03333</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acsenergylett.5c03333/asset/images/medium/nz5c03333_0006.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;ACS Energy Letters&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acsenergylett.5c03333&lt;/div&gt;</description><author>ACS Energy Letters: Latest Articles (ACS Publications)</author><pubDate>Thu, 01 Jan 2026 18:39:05 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acsenergylett.5c03333</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. 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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. 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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