From 573e8bce25d63d096169d92fe73f6c5710786c74 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Wed, 24 Dec 2025 18:30:15 +0000
Subject: [PATCH] Auto-update RSS feed

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 <?xml version="1.0" encoding="UTF-8"?>
-<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/"><channel><title>My Customized Papers (Auto-Filtered)</title><link>https://github.com/your_username/your_repo</link><description>Aggregated research papers based on keywords</description><language>en-US</language><lastBuildDate>Wed, 24 Dec 2025 12:41:48 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] Turing Pattern Engineering Enables Kinetically Ultrastable yet Ductile Metallic Glasses</title><link>https://arxiv.org/abs/2512.20196</link><description>arXiv:2512.20196v1 Announce Type: new 
+<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/"><channel><title>My Customized Papers (Auto-Filtered)</title><link>https://github.com/your_username/your_repo</link><description>Aggregated research papers based on keywords</description><language>en-US</language><lastBuildDate>Wed, 24 Dec 2025 18:30:15 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[ScienceDirect Publication: Artificial Intelligence Chemistry] Integrating Machine Learning with Electrochemical Sensors for Intelligent Food Safety Monitoring</title><link>https://www.sciencedirect.com/science/article/pii/S2949747725000223?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 23 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Artificial Intelligence Chemistry&lt;/p&gt;&lt;p&gt;Author(s): Aaryashree, Arti Devi&lt;/p&gt;</description><author>ScienceDirect Publication: Artificial Intelligence Chemistry</author><pubDate>Wed, 24 Dec 2025 18:29:56 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2949747725000223</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>Wed, 24 Dec 2025 18:29:19 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125004453</guid></item><item><title>[cond-mat updates on arXiv.org] Turing Pattern Engineering Enables Kinetically Ultrastable yet Ductile Metallic Glasses</title><link>https://arxiv.org/abs/2512.20196</link><description>arXiv:2512.20196v1 Announce Type: new 
 Abstract: Enhancing the kinetic stability of glasses often necessitates deepening thermodynamic stability, which typically compromises ductility due to increased structural rigidity. Decoupling these properties remains a critical challenge for functional applications. Here, we demonstrate that pattern engineering in metallic glasses (MGs) enables unprecedented kinetic ultrastability while retaining thermodynamic metastability and intrinsic plasticity. Through atomistic simulations guided by machine-learning interatomic potentials and replica-exchange molecular dynamics, we reveal that clustering oxygen contents, driven by reaction-diffusion-coupled pattern dynamics, act as localized pinning sites. These motifs drastically slow structural relaxation, yielding kinetic stability comparable to crystal-like ultrastable glasses while retaining an energetic as-cast state. Remarkably, the thermodynamically metastable state preserves heterogeneous atomic mobility, allowing strain delocalization under mechanical stress. By tailoring oxygen modulation via geometric patterning, we achieve an approximately 200 K increase in the onset temperature of the glass transition (Tonset) while maintaining fracture toughness akin to conventional MGs. This work establishes a paradigm of kinetic stabilization without thermodynamic compromise, offering a roadmap to additively manufacture bulk amorphous materials with combined hyperstability and plasticity.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 24 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.20196v1</guid></item><item><title>[cond-mat updates on arXiv.org] Composition-Based Machine Learning for Screening Superconducting Ternary Hydrides from a Curated Dataset</title><link>https://arxiv.org/abs/2512.20228</link><description>arXiv:2512.20228v1 Announce Type: new 
 Abstract: We present an ensemble machine-learning approach for composition-based, structure-agnostic screening of candidate superconductors among ternary hydrides under high pressure. Hydrogen-rich hydrides are known to exhibit high superconducting transition temperatures, and ternary or multinary hydrides can stabilize superconducting phases at reduced pressures through chemical compression. To systematically explore this vast compositional space, we construct an ensemble of 30 XGBoost regression models trained on a curated dataset of approximately 2000 binary and ternary hydride entries. The model ensemble is used to screen a broad set of A-B-H compositions at pressures of 100, 200, and 300 GPa, with screening outcomes evaluated statistically based on prediction consistency across ensemble members. This analysis highlights several high-scoring compositional systems, including Ca-Ti-H, Li-K-H, and Na-Mg-H, which were not explicitly included in the training dataset. In addition, feature-importance analysis indicates that elemental properties such as ionization energy and atomic radius contribute significantly to the learned composition-level trends in superconducting transition temperature. Overall, these results demonstrate the utility of ensemble-based machine learning as a primary screening tool for identifying promising regions of chemical space in superconducting hydrides.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 24 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.20228v1</guid></item><item><title>[cond-mat updates on arXiv.org] Benchmarking Universal Interatomic Potentials on Elemental Systems</title><link>https://arxiv.org/abs/2512.20230</link><description>arXiv:2512.20230v1 Announce Type: new 
 Abstract: The rapid emergence of universal Machine Learning Interatomic Potentials (uMLIPs) has transformed materials modeling. However, a comprehensive understanding of their generalization behavior across configurational space remains an open challenge. In this work, we introduce a benchmarking framework to evaluate both the equilibrium and far-from-equilibrium performance of state-of-the-art uMLIPs, including three MACE-based models, MatterSim, and PET-MAD. Our assessment utilizes Equation-of-State (EOS) tests to evaluate near-equilibrium properties, such as bulk moduli and equilibrium volumes, alongside extensive Minima Hopping (MH) structural searches to probe the global Potential Energy Surface (PES). Here, we assess universality within the fundamental limit of unary (elemental) systems, which serve as a necessary baseline for broader chemical generalization and provide a framework that can be systematically extended to multicomponent materials. We find that while most models exhibit high accuracy in reproducing equilibrium volumes for transition metals, significant performance gaps emerge in alkali and alkaline earth metal groups. Crucially, our MH results reveal a decoupling between search efficiency and structural fidelity, highlighting that smoother learned PESs do not necessarily yield more accurate energetic landscapes.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 24 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.20230v1</guid></item><item><title>[cond-mat updates on arXiv.org] Iterative learning scheme for crystal structure prediction with anharmonic lattice dynamics</title><link>https://arxiv.org/abs/2512.20424</link><description>arXiv:2512.20424v1 Announce Type: new 
@@ -8,7 +8,7 @@ Abstract: One of the primary applications of classical Boltzmann machines is gen
 Abstract: Recent experiments in twisted bilayer MoTe$_2$ (tMoTe$_2$) have uncovered a rich landscape of correlated phases. In this work, we investigate the reentrant integer quantum anomalous Hall (RIQAH) states reported by F. Xu, arXiv.2504.06972 which display a notable mismatch between the Hall conductivity measured via transport and that inferred from the Streda formula. We argue that this discrepancy can be explained if the RIQAH state is a quantum Hall bubble or Wigner crystal phase, analogous to similar well-established phenomena in two-dimensional (2D) GaAs quantum wells. While this explains the RIQAH state at filling $\nu = -0.63$, F. Xu et al. report that the other RIQAH state at $\nu = -0.7$ has a smaller slope, necessitating a different interpretation. We propose and substantiate with analysis of the experimental data that this discrepancy arises due to a nearby resistive peak masking the true slope. Furthermore, we identify this resistive peak as a signature of a phase transition near $\nu = -0.75$, possibly driven by a van Hove singularity. The anomalous Hall response and Landau fan evolution across this transition suggest a change in Fermi-surface topology and a metallic phase with a non quantized Hall response. These observations offer new insights into the nature of the RIQAH states and raise the possibility that the nearby superconducting phase may have a valley-imbalanced metal parent state.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 24 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2506.10965v4</guid></item><item><title>[cond-mat updates on arXiv.org] Surprisingly High Redundancy in Electronic Structure Data</title><link>https://arxiv.org/abs/2507.09001</link><description>arXiv:2507.09001v2 Announce Type: replace 
 Abstract: Accurate prediction of electronic structure underpins advances in chemistry, materials science, and condensed matter physics. In recent years, Machine Learning (ML) has enabled the development of powerful surrogate models that can enable the prediction of the ground state electron density and related properties at a fraction of the computational cost of conventional first principles simulations. Such ML models typically rely on massive datasets generated through expensive Kohn-Sham Density Functional Theory calculations. A key reason for relying on such large datasets is the lack of prior knowledge about which portions of the data are essential, and which are redundant. This study reveals significant redundancies in electronic structure datasets across various material systems, including molecules, simple metals, and chemically complex alloys -- challenging the notion that extensive datasets are essential for accurate ML-based electronic structure predictions. We demonstrate that even random pruning can substantially reduce dataset size with minimal loss in predictive accuracy. Furthermore, a state-of-the-art coverage-based pruning strategy that selects data across all learning difficulties, retains chemical accuracy and model generalizability using up to 100-fold less data, while reducing training time by threefold or greater. By contrast, widely used importance-based pruning methods, which eliminate easy-to-learn data, can catastrophically fail at higher pruning factors due to significant reduction in data coverage. This heretofore unexplored high redundancy in electronic structure data holds the potential to identify a minimal, essential dataset representative of each material class.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 24 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2507.09001v2</guid></item><item><title>[cond-mat updates on arXiv.org] Domain Wall-mediated Interfacial Ferroelectric Switching</title><link>https://arxiv.org/abs/2508.11997</link><description>arXiv:2508.11997v2 Announce Type: replace 
 Abstract: Interfacial ferroelectricity offers a promising platform for ultrafast, low-power memory devices. While previous studies have demonstrated the importance of domain wall in polarization switching, the coexistence of various domain wall types and their impact on polarization stability lacks fundamental understanding. By integrating first-principles calculations, machine learning methods, and experimental validations, we show that domain walls connect opposite polarization states and respond to out-of-plane electric field through polarization vector deviation, leading to inhomogeneous interlayer sliding and domain-wall migration. This mechanism bears clear resemblance to that in traditional ferroelectrics. Notably, different domain wall types result in distinct switching behaviors, which play a crucial role in determining the reversibility of polarization switching. We then propose strategies beyond ideal conditions to achieve non-volatile ferroelectric switching, which are supported by our experimental observations. These insights shed light on the microscopic switching mechanism in hexagonal interfacial ferroelectrics, offering guidance for future nanoelectronics applications.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 24 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2508.11997v2</guid></item><item><title>[cond-mat updates on arXiv.org] Local Order Average-Atom Interatomic Potentials</title><link>https://arxiv.org/abs/2510.06459</link><description>arXiv:2510.06459v4 Announce Type: replace 
-Abstract: This article describes an extension to the effective Average Atom (AA) method for random alloys to account for local ordering (short-range order) effects by utilizing information from partial radial distribution functions. The new Local-Order Average Atom (LOAA) method is rigorously derived based on statistical mechanics arguments and validated for non-stoichiometric binary 2D hexagonal crystals and 3D FeNiCr and NiAl alloys whose ground state is obtained through Monte Carlo sampling. Material properties for these alloys, and phase transformations for the NiAl system, computed from static and dynamic atomistic simulations using standard interatomic potentials (IPs) exhibit a strong dependence on local ordering that is captured by simulations with effective LOAA IPs, but not the original AA method. The advantage of LOAA is that it requires smaller system sizes to achieve statistically converged results and therefore enables the simulation of complex materials, such as high-entropy alloys, at a fraction of the computational cost of standard IPs.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 24 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2510.06459v4</guid></item><item><title>[ScienceDirect Publication: eScience] Achieving high-voltage polymer-based all-solid-state batteries based on thermodynamic and kinetic degradation insights</title><link>https://www.sciencedirect.com/science/article/pii/S2667141725000631?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; eScience, Volume 6, Issue 1&lt;/p&gt;&lt;p&gt;Author(s): Xiaoyan Yu, Yun Su, Hang Su, Ruizhi Liu, Jingyi Qiu, Xiayu Zhu, Rui Wen, Hao Zhang, Xiaohui Rong, Yong-Sheng Hu, Gaoping Cao&lt;/p&gt;</description><author>ScienceDirect Publication: eScience</author><pubDate>Wed, 24 Dec 2025 01:38:36 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2667141725000631</guid></item><item><title>[ChemRxiv] Chemical Reactivity Mapping: Exploring Chemical Reaction based on Digitalization of Small Molecules through Mayr’s Parameter</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-1r6gz?rft_dat=source%3Ddrss</link><description>A new strategy for exploring novel reactions, utilizing Mayr’s reactivity parameters, is developed. We create a highly accurate prediction model of reactivity parameters using machine learning. Validating the reactivity scales in the reactions of isoquinoline and predicting the reactivity in commercially available chemicals lead to a new approach for exploring chemical reactions. Incorporating Mayr's reactivity parameters in both chemical reactivity mapping and chemical reaction space contributes to expedite the exploitation of reactions in small molecule syntheses.</description><author>ChemRxiv</author><pubDate>Wed, 24 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-1r6gz?rft_dat=source%3Ddrss</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Defect‐Driven Ionic Trap Construction and Interface Modulation for Rapid Li+ Kinetics in Composite Solid Electrolytes</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202519541?af=R</link><description>Advanced Materials, EarlyView.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Tue, 23 Dec 2025 08:43:35 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202519541</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] A Smart Self‐Immobilization Magnetic Resonance Contrast Agent for Delayed Tumor Imaging In Vivo</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202516998?af=R</link><description>Angewandte Chemie International Edition, Volume 64, Issue 52, December 22, 2025.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Tue, 23 Dec 2025 08:21:09 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202516998</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Iron/NHC‐Catalyzed Regio‐ and Stereoselective 1,6‐Additions of Aliphatic Grignard Reagents to α,β,γ,δ‐Unsaturated Carbonyl Compounds: Asymmetric Variants with Chiral NHCs</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202518346?af=R</link><description>Angewandte Chemie International Edition, Volume 64, Issue 52, December 22, 2025.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Tue, 23 Dec 2025 08:21:09 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202518346</guid></item><item><title>[Wiley: Small: Table of Contents] Supersaturation‐Driven Co‐Precipitation Enables Scalable Wet‐Chemical Synthesis of High‐Purity Na3InCl6 Solid Electrolyte for Sodium‐Ion Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202509165?af=R</link><description>Small, Volume 21, Issue 51, December 23, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Tue, 23 Dec 2025 07:06:10 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202509165</guid></item><item><title>[Wiley: Small: Table of Contents] Synergistic Co‐Optimization Strategy for Electron‐Ion Transport Kinetics in all‐Solid‐State Sulfurized Polyacrylonitrile Cathodes</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202507810?af=R</link><description>Small, Volume 21, Issue 51, December 23, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Tue, 23 Dec 2025 07:06:10 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202507810</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] Defect-reconstructed carbon nitride nanosheets for sacrificial agent-free H&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;2&lt;/sub&gt; photosynthesis coupled with biomass-derived polyols valorization</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725007006?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Catalysis, Volume 454&lt;/p&gt;&lt;p&gt;Author(s): Yanmei Zheng, Qiang Xu, Jianghong Ouyang, Ziwei Hang, Jingde Li, Xinli Guo, Zupeng Chen&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Tue, 23 Dec 2025 06:33:51 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725007006</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] A novel kind of activating agents for preparation of high specific surface area porous carbon towards zinc‑iodine batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25045542?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 145&lt;/p&gt;&lt;p&gt;Author(s): Yun Zhou, Boyang Liu, Xuejin Chen, Junchen Chen, Chenglong Li, Xulai Xiao&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 23 Dec 2025 06:33:34 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25045542</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] A molecular simulation study to the effect of T313 bonding agent and crystal defects on the performance of ammonium perchlorate oxidizer</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625006913?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 30 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 262&lt;/p&gt;&lt;p&gt;Author(s): Bin Yuan, Rui Zhu, Jianfa Chen, Kuai He&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 06:33:32 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625006913</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] Mobility and sintering of silica-supported platinum clusters via reactive neural network potentials</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725005998?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Catalysis, Volume 453&lt;/p&gt;&lt;p&gt;Author(s): Tereza Benešová, Kristýna Pokorná, Andreas Erlebach, Christopher J. Heard&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Tue, 23 Dec 2025 05:56:09 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725005998</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] Machine learning–assisted discovery of chromium bis(2-pyridyl)amine catalysts for ethylene tri-/tetramerization</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725006797?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Catalysis, Volume 454&lt;/p&gt;&lt;p&gt;Author(s): Youcai Zhu, Yue Mu, Xiaoke Shi, Shu Yang, Li Sun, Zhen Liu&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Tue, 23 Dec 2025 05:56:09 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725006797</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] The influence of the organic residue and the solvent in the Schlenk equilibrium for Grignard reagents in THF. A molecular dynamics study with machine learning potentials</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725006852?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Catalysis, Volume 454&lt;/p&gt;&lt;p&gt;Author(s): Marco Bortoli, Sigbjørn Løland Bore, Odile Eisenstein, Michele Cascella&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Tue, 23 Dec 2025 05:56:09 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725006852</guid></item><item><title>[ScienceDirect Publication: Acta Materialia] DuctGPT: A Generative Transformer for Forward Screening of Ductile Refractory Multi-Principal Element Alloys</title><link>https://www.sciencedirect.com/science/article/pii/S135964542501050X?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Acta Materialia, Volume 304&lt;/p&gt;&lt;p&gt;Author(s): Sai Pranav Reddy Guduru, Mkpe O. Kekung, Ryan T. Ott, Sougata Roy, Prashant Singh&lt;/p&gt;</description><author>ScienceDirect Publication: Acta Materialia</author><pubDate>Tue, 23 Dec 2025 05:56:09 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S135964542501050X</guid></item><item><title>[ScienceDirect Publication: Acta Materialia] On-the-fly machine learning of interatomic potentials for elastic property modeling in Al–Mg–Zr solid solutions</title><link>https://www.sciencedirect.com/science/article/pii/S1359645425011310?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 15 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Acta Materialia, Volume 305&lt;/p&gt;&lt;p&gt;Author(s): Lukas Volkmer, Leonardo Medrano Sandonas, Philip Grimm, Julia Kristin Hufenbach, Gianaurelio Cuniberti&lt;/p&gt;</description><author>ScienceDirect Publication: Acta Materialia</author><pubDate>Tue, 23 Dec 2025 05:56:09 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1359645425011310</guid></item><item><title>[ScienceDirect Publication: Journal of Materiomics] Data-augmented machine learning models for oxynitride glasses &lt;em&gt;via&lt;/em&gt; Wasserstein generative adversarial network with gradient penalty and content constraint</title><link>https://www.sciencedirect.com/science/article/pii/S2352847825001017?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 8 August 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Materiomics&lt;/p&gt;&lt;p&gt;Author(s): Jing Tian, Yuan Li, Min Guan, Jijie Zheng, Jingyuan Chu, Yong Liu, Gaorong Han&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Materiomics</author><pubDate>Tue, 23 Dec 2025 05:55:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352847825001017</guid></item><item><title>[ScienceDirect Publication: Journal of Materiomics] Machine learning assisted &lt;em&gt;τ&lt;/em&gt;&lt;sub&gt;f&lt;/sub&gt; value prediction of ABO&lt;sub&gt;3&lt;/sub&gt;-type microwave dielectric ceramics</title><link>https://www.sciencedirect.com/science/article/pii/S2352847825001078?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 8 August 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Materiomics&lt;/p&gt;&lt;p&gt;Author(s): Mingyue Yang, Liangyu Mo, Jincheng Qin, Faqiang Zhang, Mingsheng Ma, Yongxiang Li, Zhifu Liu&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Materiomics</author><pubDate>Tue, 23 Dec 2025 05:55:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352847825001078</guid></item><item><title>[ScienceDirect Publication: Journal of Materiomics] Domain knowledge-assisted materials data anomaly detection towards constructing high-performance machine learning models</title><link>https://www.sciencedirect.com/science/article/pii/S2352847825000565?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: November 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Materiomics, Volume 11, Issue 6&lt;/p&gt;&lt;p&gt;Author(s): Yue Liu, Shuchang Ma, Zhengwei Yang, Duo Wu, Yali Zhao, Maxim Avdeev, Siqi Shi&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Materiomics</author><pubDate>Tue, 23 Dec 2025 05:55:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352847825000565</guid></item><item><title>[ScienceDirect Publication: Journal of Materiomics] PTCDA/CuS cathode enabling stable sulfide-based all-solid-state batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352847825000814?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Materiomics, Volume 12, Issue 1&lt;/p&gt;&lt;p&gt;Author(s): Zhixing Wan, Shuo Wang, Yahao Mu, Ruihua Zhou, Hang Liu, Tingwu Jin, Di Wu, Jianlong Xia, Ce-Wen Nan&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Materiomics</author><pubDate>Tue, 23 Dec 2025 05:55:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352847825000814</guid></item><item><title>[ScienceDirect Publication: Current Opinion in Solid State and Materials Science] Solid electrolyte-driven suppression of H2–H3 phase transition in Ni-rich cathodes for stable high-voltage cycling</title><link>https://www.sciencedirect.com/science/article/pii/S1359028625000324?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Current Opinion in Solid State and Materials Science, Volume 39&lt;/p&gt;&lt;p&gt;Author(s): Hao Chen, Hsiao-Hsuan Wu, Chia-Chen Li&lt;/p&gt;</description><author>ScienceDirect Publication: Current Opinion in Solid State and Materials Science</author><pubDate>Tue, 23 Dec 2025 05:55:53 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1359028625000324</guid></item><item><title>[ScienceDirect Publication: Current Opinion in Solid State and Materials Science] State-of-the-art review of additive friction stir deposition: microstructural evolution, machine learning applications, and future directions</title><link>https://www.sciencedirect.com/science/article/pii/S1359028625000300?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Current Opinion in Solid State and Materials Science, Volume 40&lt;/p&gt;&lt;p&gt;Author(s): Ashish Kumar, Lei Shi, Virendra Pratap Singh, Sudipta Mohapatra, Long Li, Chuansong Wu, Sergey Mironov, Amitava De&lt;/p&gt;</description><author>ScienceDirect Publication: Current Opinion in Solid State and Materials Science</author><pubDate>Tue, 23 Dec 2025 05:55:53 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1359028625000300</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Transfer learning-enhanced hybrid deep neural network model for accurate lithium-ion batteries health estimation in electric vehicles</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25045451?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 145&lt;/p&gt;&lt;p&gt;Author(s): Ibrahim AL-Wesabi, Hassan M. 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Al-Shamma'a&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 23 Dec 2025 05:55:53 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25045451</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] UV-curable montmorillonite-enhanced gel polymer electrolyte for dendrite-free, enhanced ion transport, and flexible Zn metal batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25046353?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 145&lt;/p&gt;&lt;p&gt;Author(s): Eunyoung Jung, Gaeun Lee, Byeongjun Kim, Chanwoo Park, Yujin Nam, Jong-Seong Bae, Ji Hyeon Kim, Yong Nam Ahn, Jaehyun Hur&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 23 Dec 2025 05:55:53 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25046353</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Mechanically reinforced graphene heterostructure anodes co-optimizing ultrafast ion diffusion and high storage capacity for Li/Na-ion batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25046298?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 145&lt;/p&gt;&lt;p&gt;Author(s): Houda Khattab, Hamza Bekkali, Abdelilah Benyoussef, Abdallah El Kenz, Omar Mounkachi&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 23 Dec 2025 05:55:53 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25046298</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Thermal diffusivity and conductivity of sulfide and oxide solid electrolytes: Effects of densification and microstructural evolution</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25046407?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 145&lt;/p&gt;&lt;p&gt;Author(s): Hayoung Lee, Yuto Seki, Atsuro Okumura, Manabu Kodama&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 23 Dec 2025 05:55:53 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25046407</guid></item><item><title>[ScienceDirect Publication: Solid State Ionics] Crossover from insulating into solid electrolyte behavior in bulk CaSO&lt;sub&gt;4&lt;/sub&gt;⋅0.5H&lt;sub&gt;2&lt;/sub&gt;O material due to ion exchange processes induced by high-temperature treatment with orthophosphoric acid</title><link>https://www.sciencedirect.com/science/article/pii/S0167273825003170?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Solid State Ionics, Volume 434&lt;/p&gt;&lt;p&gt;Author(s): Ivan Nikulin, Tatiana Nikulicheva, Vitaly Vyazmin, Oleg Ivanov, Nikita Anosov, Olga Telpova&lt;/p&gt;</description><author>ScienceDirect Publication: Solid State Ionics</author><pubDate>Tue, 23 Dec 2025 05:55:51 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0167273825003170</guid></item><item><title>[ScienceDirect Publication: Solid State Ionics] First-principles elucidation of the effects of Al-doping on Li-ion diffusion in LiCoO&lt;sub&gt;2&lt;/sub&gt;</title><link>https://www.sciencedirect.com/science/article/pii/S0167273825003224?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Solid State Ionics, Volume 434&lt;/p&gt;&lt;p&gt;Author(s): Jordan A. 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Artificial Intelligence Chemistry, Volume 3, Issue 2&lt;/p&gt;&lt;p&gt;Author(s): Hajime Torii, Mikito Tsujimoto, Yukichi Kitamura&lt;/p&gt;</description><author>ScienceDirect Publication: Artificial Intelligence Chemistry</author><pubDate>Tue, 23 Dec 2025 05:55:51 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2949747725000156</guid></item><item><title>[ScienceDirect Publication: Artificial Intelligence Chemistry] Comparative study of machine learning methods for accurate prediction of logP and pKb</title><link>https://www.sciencedirect.com/science/article/pii/S2949747725000181?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: June 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Artificial Intelligence Chemistry, Volume 4, Issue 1&lt;/p&gt;&lt;p&gt;Author(s): Juda Baikété, Alhadji Malloum, Jeanet Conradie&lt;/p&gt;</description><author>ScienceDirect Publication: Artificial Intelligence Chemistry</author><pubDate>Tue, 23 Dec 2025 05:55:51 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2949747725000181</guid></item><item><title>[ScienceDirect Publication: Artificial Intelligence Chemistry] Statistical comparison and uncertainty analysis of graph neural networks and machine learning models for molecular property prediction in drug discovery</title><link>https://www.sciencedirect.com/science/article/pii/S294974772500020X?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: June 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Artificial Intelligence Chemistry, Volume 4, Issue 1&lt;/p&gt;&lt;p&gt;Author(s): Sarmad Waleed, Shams ul Islam, Muhammad Saleem, Ali Ahmed&lt;/p&gt;</description><author>ScienceDirect Publication: Artificial Intelligence Chemistry</author><pubDate>Tue, 23 Dec 2025 05:55:51 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S294974772500020X</guid></item><item><title>[ScienceDirect Publication: eScience] &lt;em&gt;In situ&lt;/em&gt; engineered organic–inorganic bilayer solid electrolyte interphase enabling lithium metal batteries over 15,000-hour lifespan</title><link>https://www.sciencedirect.com/science/article/pii/S2667141725001090?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 27 September 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; eScience&lt;/p&gt;&lt;p&gt;Author(s): Bin Zhang, Ke Yue, Ke Wang, Guohao Zhao, Wenbin Huang, Tao Li, Jianmin Luo, Ying Xu, Xinyong Tao&lt;/p&gt;</description><author>ScienceDirect Publication: eScience</author><pubDate>Tue, 23 Dec 2025 05:55:50 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2667141725001090</guid></item><item><title>[ScienceDirect Publication: eScience] Machine learning-driven discovery of high-performance Co/Sr-free air electrodes for protonic ceramic electrolysis cells</title><link>https://www.sciencedirect.com/science/article/pii/S2667141725001168?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 24 October 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; eScience&lt;/p&gt;&lt;p&gt;Author(s): Ning Wang, Qingwen Su, Huanxin Xiang, Biyao Zhou, Jiaxin Huang, Fangyuan Zheng, Ruijie Zhu, Yoshitaka Aoki, Ling Meng, Feng Jiao, Baoyin Yuan, Chunmei Tang, Siyu Ye&lt;/p&gt;</description><author>ScienceDirect Publication: eScience</author><pubDate>Tue, 23 Dec 2025 05:55:50 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2667141725001168</guid></item><item><title>[ScienceDirect Publication: eScience] Oxygen-induced lithium inter-cage conduction for enhanced performance in all-solid-state batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2667141725001326?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 18 November 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; eScience&lt;/p&gt;&lt;p&gt;Author(s): Yongsun Park, Myeongcho Jang, Jiyu Kim, Joon Ha Chang, Hae-Yong Kim, Seung-Deok Seo, Gwangseok Oh, Min Jae You, Sunho Ko, Jung Ho Kim, Kyung-Wan Nam, Seungho Yu, Sang-Cheol Nam, Ohmin Kwon&lt;/p&gt;</description><author>ScienceDirect Publication: eScience</author><pubDate>Tue, 23 Dec 2025 05:55:50 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2667141725001326</guid></item><item><title>[ScienceDirect Publication: Science Bulletin] Machine learning-guided design of high-performance Mg-based thermoelectrics: insights into thermal expansion effects</title><link>https://www.sciencedirect.com/science/article/pii/S2095927325008163?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 30 November 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Science Bulletin, Volume 70, Issue 22&lt;/p&gt;&lt;p&gt;Author(s): Da Wan, Shulin Bai, Sirui Fan, Bixuan Li, Xiaoya Huang, Hongyuan Zhao, Ziqi Wang, Zhen Li, Yu Liu, Peng Kang, Lei Zheng, Li-Dong Zhao, Huibin Xu&lt;/p&gt;</description><author>ScienceDirect Publication: Science Bulletin</author><pubDate>Tue, 23 Dec 2025 05:55:50 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2095927325008163</guid></item><item><title>[ScienceDirect Publication: Science Bulletin] Reconfigurable bipolar and nonlinear photoresponse in 2D VSe&lt;sub&gt;2&lt;/sub&gt;/WSe&lt;sub&gt;2&lt;/sub&gt; photodetectors for high-performance optical neural networks</title><link>https://www.sciencedirect.com/science/article/pii/S2095927325012575?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 15 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Science Bulletin&lt;/p&gt;&lt;p&gt;Author(s): Zhengui Zhao, Yuan Cheng, Jiacheng Sun, Pengyu Zhang, Tonglu Wang, Jianshi Tang, Yuyan Wang, Junying Zhang&lt;/p&gt;</description><author>ScienceDirect Publication: Science Bulletin</author><pubDate>Tue, 23 Dec 2025 05:55:50 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2095927325012575</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>Tue, 23 Dec 2025 05:55:50 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2095927325010965</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. 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Singh Raman, Alankar Alankar&lt;/p&gt;</description><author>ScienceDirect Publication: Progress in Materials Science</author><pubDate>Tue, 23 Dec 2025 05:55:47 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0079642525002166</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Deformation mechanisms of multiphase AlCoCuFeNiTi high-entropy alloys revealed by machine learning-assisted molecular dynamics</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325002561?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics, Volume 59&lt;/p&gt;&lt;p&gt;Author(s): Yu-Sheng Lu, Kenta Yamanaka, Hiroya Ishii, Thi-Xuyen Bui, Yi-Ju Hsieh, Te-Hua Fang&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 05:55:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325002561</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Modeling strategies for hydrogen reduction of high-purity metals: From DFT to ReaxFF and machine learning</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325002597?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics, Volume 59&lt;/p&gt;&lt;p&gt;Author(s): Zhimeng Shao, Bowen Gao, Zhifang Hu, Honglin Jiang, Qidong Zhang, Zhihe Dou, Yanxi Yin&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 05:55:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325002597</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Physics-informed neural networks with hard-encoded angle-dependent boundary conditions for phonon Boltzmann transport equation</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325002780?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics, Volume 59&lt;/p&gt;&lt;p&gt;Author(s): Jiahang Zhou, Ruiyang Li, Wenjie Shang, Yi Liu, J.P. Panda, Pan Du, Xin-Yang Liu, Jasmine Liang, Ben-Chi Ma, Jian-Xun Wang, Tengfei Luo&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 05:55:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325002780</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Machine-learning potentials for quantum and anharmonic effects in superconducting &lt;math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg" class="math"&gt;&lt;mrow&gt;&lt;mi mathvariant="bold-italic"&gt;F&lt;/mi&gt;&lt;mi mathvariant="bold-italic"&gt;m&lt;/mi&gt;&lt;mover accent="true"&gt;&lt;mn mathvariant="bold"&gt;3&lt;/mn&gt;&lt;mo&gt;‾&lt;/mo&gt;&lt;/mover&gt;&lt;mi mathvariant="bold-italic"&gt;m&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt; LaBeH&lt;sub&gt;8&lt;/sub&gt;</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325002950?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics, Volume 59&lt;/p&gt;&lt;p&gt;Author(s): Guiyan Dong, Tian Cui, Zihao Huo, Zhengtao Liu, Wenxuan Chen, Pugeng Hou, Yue-Wen Fang, Defang Duan&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 05:55:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325002950</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] A computational framework for interface design using lattice matching, machine learning potentials, and active learning: A case study on LaCoO&lt;sub&gt;3&lt;/sub&gt;/La&lt;sub&gt;2&lt;/sub&gt;NiO&lt;sub&gt;4&lt;/sub&gt;</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325002962?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics, Volume 59&lt;/p&gt;&lt;p&gt;Author(s): Guangchen Liu, Songge Yang, Yu Zhong&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 05:55:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325002962</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Thermal expansion prediction in oxide glasses via graph neural networks with temperature-encoded virtual nodes</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325002998?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics, Volume 59&lt;/p&gt;&lt;p&gt;Author(s): Huang Ming, Xiong Jingxian, Peng Yongqian, Mao Haijun, Liu Zhuofeng, Li Wei, Wang Fenglin, Zhang Weijun, Chen Xingyu&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 05:55:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325002998</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Tackling dataset curation challenges towards reliable machine learning: a case study on thermoelectric materials</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325003049?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics, Volume 59&lt;/p&gt;&lt;p&gt;Author(s): Shoeb Athar, Adrien Mecibah, Philippe Jund&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 05:55:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325003049</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Research progress of machine learning in flexible strain sensors in the context of material intelligence</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325002883?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics, Volume 59&lt;/p&gt;&lt;p&gt;Author(s): Jie Li, Zhe Li, Yan Lu, Gang Ye, Yan Hong, Li Niu, Jian Fang&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 05:55:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325002883</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] A physics-informed machine learning framework for unified prediction of superconducting transition temperatures</title><link>https://www.sciencedirect.com/science/article/pii/S254252932500327X?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics, Volume 60&lt;/p&gt;&lt;p&gt;Author(s): Ehsan Alibagheri, Mohammad Sandoghchi, Alireza Seyfi, Mohammad Khazaei, S. 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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>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009292</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Calendar aging of sulfide all-solid-state batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009358?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Yujing Wu, Ziqi Zhang, Dengxu Wu, Fuqiang Xu, Mu Zhou, Hong Li, Liquan Chen, Fan Wu&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009358</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Energy-efficient, high-accuracy sensing in loose-fitting textile sensor matrix for LLM-enabled human-robot collaboration</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009425?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Pengfei Deng, Yang Meng, Qilong Cheng, Yuanqiu Tan, Zhihong Chen, Tian Li&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009425</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Lithium superionic solid electrolyte: Phosphorus-free sulfide glass of LiSbGe&lt;sub&gt;(4-x)/4&lt;/sub&gt;S&lt;sub&gt;4-x&lt;/sub&gt;Cl&lt;sub&gt;x&lt;/sub&gt;</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009620?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Yuna Kim, Woojung Lee, Jiyun Han, Yeong Mu Seo, Dokyung Kim, Young Joo Lee, Byung Gon Kim, Munseok S. Chae, Sung Jin Kim, In Young Kim&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009620</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Advancing high-safety and low-cost all-solid-state batteries with polyanion cathodes: Challenges and recent progress</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009978?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 148&lt;/p&gt;&lt;p&gt;Author(s): Ali Yaghtin, Atiyeh Nekahi, Jeremy I.G. Dawkins, Xia Li, Karim Zaghib, Sixu Deng&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009978</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Enabling high-accuracy lithium-ion battery status prediction via machine learning-integrated perovskite sensors</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009851?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 148&lt;/p&gt;&lt;p&gt;Author(s): Jiaxin Sun, Xianjie Xu, Zhefu Mu, Zijun Huang, Guo Chen, Xinkai Qi, Hongwei Liu, Lei Zhu, Xiuquan Gu, Xinjian He, Sheng Huang&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009851</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Phase boundary engineering in micro-sized Sn/SnSb anode enabling superior sodium storage: Synergistic stress relief and fast ion transport</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525010249?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 148&lt;/p&gt;&lt;p&gt;Author(s): Yuhong Liang, Chengcheng He, Zhengyang Zhao, Longqing Zhang, Rui Sun, Qian Ning, Huibing He, Yang Ren, Jing Xu, Qiang Zhang, Yajie Song, Xucai Yin&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525010249</guid></item><item><title>[ScienceDirect Publication: Matter] Machine learning-driven ligand engineering decodes and controls structural distortions in 2D perovskites</title><link>https://www.sciencedirect.com/science/article/pii/S2590238525005259?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 10 October 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Matter&lt;/p&gt;&lt;p&gt;Author(s): Zhipeng Miao, Yahui Han, Qi Pan, Yipei Wang, Haibin Wang, Yunhang Xie, Jie Yu, Yapeng Shi, Rui Zhang, Yanlin Song, Pengwei Li&lt;/p&gt;</description><author>ScienceDirect Publication: Matter</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2590238525005259</guid></item><item><title>[ScienceDirect Publication: Matter] SpectroGen: A physically informed generative artificial intelligence for accelerated cross-modality spectroscopic materials characterization</title><link>https://www.sciencedirect.com/science/article/pii/S2590238525004771?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 14 October 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Matter&lt;/p&gt;&lt;p&gt;Author(s): Yanmin Zhu, Loza F. Tadesse&lt;/p&gt;</description><author>ScienceDirect Publication: Matter</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2590238525004771</guid></item><item><title>[ScienceDirect Publication: Matter] Precisely deciphering solid electrolyte interphase</title><link>https://www.sciencedirect.com/science/article/pii/S2590238525004114?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 5 November 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Matter, Volume 8, Issue 11&lt;/p&gt;&lt;p&gt;Author(s): Enhui Wang, Shaohua Ge, Wenbin Li, Beibei Fu, Fangyi Zhou, Weihua Chen&lt;/p&gt;</description><author>ScienceDirect Publication: Matter</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2590238525004114</guid></item><item><title>[ScienceDirect Publication: Matter] Rapid scalable plasma processing of thin-film Li–La–Zr–O solid-state electrolytes</title><link>https://www.sciencedirect.com/science/article/pii/S2590238525005119?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 5 November 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Matter, Volume 8, Issue 11&lt;/p&gt;&lt;p&gt;Author(s): Gabriel Badillo Crane, Thomas W. Colburn, Sarah E. Holmes, Justus Just, Yi Cui, Reinhold H. Dauskardt&lt;/p&gt;</description><author>ScienceDirect Publication: Matter</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2590238525005119</guid></item><item><title>[ScienceDirect Publication: Joule] Impact of metallic interlayers at the lithium-Li&lt;sub&gt;6&lt;/sub&gt;PS&lt;sub&gt;5&lt;/sub&gt;Cl solid electrolyte interface</title><link>https://www.sciencedirect.com/science/article/pii/S2542435125003563?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 19 November 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Joule, Volume 9, Issue 11&lt;/p&gt;&lt;p&gt;Author(s): Souhardh Kotakadi, Jack Aspinall, Matthew Burton, Yi Liang, Yuichi Aihara, Mauro Pasta&lt;/p&gt;</description><author>ScienceDirect Publication: Joule</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125003563</guid></item><item><title>[ScienceDirect Publication: Joule] Li–Si compound anodes enabling high-performance all-solid-state Li-ion batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2542435125003769?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 17 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Joule, Volume 9, Issue 12&lt;/p&gt;&lt;p&gt;Author(s): Do-Hyeon Kim, Young-Han Lee, Jeong-Myeong Yoon, Pugalenthiyar Thondaiman, Byung Chul Kim, In-Chul Choi, Jeong-Hee Choi, Ki-Joon Jeon, Cheol-Min Park&lt;/p&gt;</description><author>ScienceDirect Publication: Joule</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125003769</guid></item><item><title>[ScienceDirect Publication: Joule] Boosting ionic conductivity of fluoride electrolytes by polyanion coordination chemistry enabling 5 V-Class all-solid-state batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2542435125004143?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 19 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Joule&lt;/p&gt;&lt;p&gt;Author(s): Huaimin Jin, Xingyu Wang, Simeng Zhang, Xiangzhen Zhu, Chong Liu, Junyi Yue, Jie Qu, Bei Wu, Xu Han, Yueyue Wang, Yang Xu, Han Wu, Liyu Zhou, Mingying Zhang, Hao Lai, Shuo Wang, Jiangwen Liang, Xueliang Sun, Xiaona Li&lt;/p&gt;</description><author>ScienceDirect Publication: Joule</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125004143</guid></item><item><title>[cond-mat updates on arXiv.org] CrystalFormer-CSP: Thinking Fast and Slow for Crystal Structure Prediction</title><link>https://arxiv.org/abs/2512.18251</link><description>arXiv:2512.18251v1 Announce Type: new 
+Abstract: This article describes an extension to the effective Average Atom (AA) method for random alloys to account for local ordering (short-range order) effects by utilizing information from partial radial distribution functions. The new Local-Order Average Atom (LOAA) method is rigorously derived based on statistical mechanics arguments and validated for non-stoichiometric binary 2D hexagonal crystals and 3D FeNiCr and NiAl alloys whose ground state is obtained through Monte Carlo sampling. Material properties for these alloys, and phase transformations for the NiAl system, computed from static and dynamic atomistic simulations using standard interatomic potentials (IPs) exhibit a strong dependence on local ordering that is captured by simulations with effective LOAA IPs, but not the original AA method. The advantage of LOAA is that it requires smaller system sizes to achieve statistically converged results and therefore enables the simulation of complex materials, such as high-entropy alloys, at a fraction of the computational cost of standard IPs.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 24 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2510.06459v4</guid></item><item><title>[ScienceDirect Publication: eScience] Achieving high-voltage polymer-based all-solid-state batteries based on thermodynamic and kinetic degradation insights</title><link>https://www.sciencedirect.com/science/article/pii/S2667141725000631?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; eScience, Volume 6, Issue 1&lt;/p&gt;&lt;p&gt;Author(s): Xiaoyan Yu, Yun Su, Hang Su, Ruizhi Liu, Jingyi Qiu, Xiayu Zhu, Rui Wen, Hao Zhang, Xiaohui Rong, Yong-Sheng Hu, Gaoping Cao&lt;/p&gt;</description><author>ScienceDirect Publication: eScience</author><pubDate>Wed, 24 Dec 2025 01:38:36 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2667141725000631</guid></item><item><title>[ChemRxiv] Chemical Reactivity Mapping: Exploring Chemical Reaction based on Digitalization of Small Molecules through Mayr’s Parameter</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-1r6gz?rft_dat=source%3Ddrss</link><description>A new strategy for exploring novel reactions, utilizing Mayr’s reactivity parameters, is developed. We create a highly accurate prediction model of reactivity parameters using machine learning. Validating the reactivity scales in the reactions of isoquinoline and predicting the reactivity in commercially available chemicals lead to a new approach for exploring chemical reactions. Incorporating Mayr's reactivity parameters in both chemical reactivity mapping and chemical reaction space contributes to expedite the exploitation of reactions in small molecule syntheses.</description><author>ChemRxiv</author><pubDate>Wed, 24 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-1r6gz?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Guiding Ligand Selection in Copper-Catalyzed Cross Couplings via Principal Component Analysis</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-rqcts?rft_dat=source%3Ddrss</link><description>Copper-catalyzed Ullmann-type couplings are a promising alternative to Pd-catalyzed C-O and C-N bond formations. Recent discoveries of numerous second-generation ligands for Cu-mediated Ullmann-type cross-coupling reactions have broadened the scope to include less reactive aryl halides and nucleophiles and have also enabled lower copper and ligand loadings in these processes. However, in contrast to Pd chemistry, in-silico workflows for guiding systematic ligand selection from early-stage screening through optimization remain significantly less developed for Cu-catalyzed C-O and C-N bond coupling reactions. This report describes the application of Principal Component Analysis (PCA) for streamlining ligand selection in Cu-catalyzed Ullmann-type coupling reactions. It provides a “map” of more than 80 ligands used in Ullmann couplings and is designed to reveal reactivity trends and highlight areas of high and low yields, thereby facilitating prioritization of high-value experiments in both medicinal and process chemistry settings. Validation for C-N and C-O couplings is provided using literature datasets, along with the development and application of a PCA-guided screening set for Ullmann couplings.</description><author>ChemRxiv</author><pubDate>Wed, 24 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-rqcts?rft_dat=source%3Ddrss</guid></item><item><title>[Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)] [ASAP] Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials</title><link>http://dx.doi.org/10.1021/acs.jctc.5c01712</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jctc.5c01712/asset/images/medium/ct5c01712_0007.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;Journal of Chemical Theory and Computation&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acs.jctc.5c01712&lt;/div&gt;</description><author>Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)</author><pubDate>Tue, 23 Dec 2025 19:20:50 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jctc.5c01712</guid></item><item><title>[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Tuning ORR Activity of N-Doped Biphenylene-Based Single-Atom Catalysts via DFT and Machine Learning Synergy</title><link>http://dx.doi.org/10.1021/acs.jpcc.5c06757</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpcc.5c06757/asset/images/medium/jp5c06757_0007.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;The Journal of Physical Chemistry C&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acs.jpcc.5c06757&lt;/div&gt;</description><author>The Journal of Physical Chemistry C: Latest Articles (ACS Publications)</author><pubDate>Tue, 23 Dec 2025 18:21:52 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpcc.5c06757</guid></item><item><title>[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Concomitant Enhancement of the Reorientational Dynamics of the BH4– Anions and Mg2+ Ionic Conductivity in Mg(BH4)2·NH3 upon Ligand Incorporation</title><link>http://dx.doi.org/10.1021/acs.jpcc.5c07031</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpcc.5c07031/asset/images/medium/jp5c07031_0012.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;The Journal of Physical Chemistry C&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acs.jpcc.5c07031&lt;/div&gt;</description><author>The Journal of Physical Chemistry C: Latest Articles (ACS Publications)</author><pubDate>Tue, 23 Dec 2025 13:34:12 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpcc.5c07031</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Defect‐Driven Ionic Trap Construction and Interface Modulation for Rapid Li+ Kinetics in Composite Solid Electrolytes</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202519541?af=R</link><description>Advanced Materials, EarlyView.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Tue, 23 Dec 2025 08:43:35 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202519541</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] A Smart Self‐Immobilization Magnetic Resonance Contrast Agent for Delayed Tumor Imaging In Vivo</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202516998?af=R</link><description>Angewandte Chemie International Edition, Volume 64, Issue 52, December 22, 2025.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Tue, 23 Dec 2025 08:21:09 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202516998</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Iron/NHC‐Catalyzed Regio‐ and Stereoselective 1,6‐Additions of Aliphatic Grignard Reagents to α,β,γ,δ‐Unsaturated Carbonyl Compounds: Asymmetric Variants with Chiral NHCs</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.202518346?af=R</link><description>Angewandte Chemie International Edition, Volume 64, Issue 52, December 22, 2025.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Tue, 23 Dec 2025 08:21:09 GMT</pubDate><guid isPermaLink="true">10.1002/anie.202518346</guid></item><item><title>[Wiley: Small: Table of Contents] Supersaturation‐Driven Co‐Precipitation Enables Scalable Wet‐Chemical Synthesis of High‐Purity Na3InCl6 Solid Electrolyte for Sodium‐Ion Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202509165?af=R</link><description>Small, Volume 21, Issue 51, December 23, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Tue, 23 Dec 2025 07:06:10 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202509165</guid></item><item><title>[Wiley: Small: Table of Contents] Synergistic Co‐Optimization Strategy for Electron‐Ion Transport Kinetics in all‐Solid‐State Sulfurized Polyacrylonitrile Cathodes</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202507810?af=R</link><description>Small, Volume 21, Issue 51, December 23, 2025.</description><author>Wiley: Small: Table of Contents</author><pubDate>Tue, 23 Dec 2025 07:06:10 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202507810</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] Defect-reconstructed carbon nitride nanosheets for sacrificial agent-free H&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;2&lt;/sub&gt; photosynthesis coupled with biomass-derived polyols valorization</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725007006?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Catalysis, Volume 454&lt;/p&gt;&lt;p&gt;Author(s): Yanmei Zheng, Qiang Xu, Jianghong Ouyang, Ziwei Hang, Jingde Li, Xinli Guo, Zupeng Chen&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Tue, 23 Dec 2025 06:33:51 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725007006</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] A novel kind of activating agents for preparation of high specific surface area porous carbon towards zinc‑iodine batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25045542?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 145&lt;/p&gt;&lt;p&gt;Author(s): Yun Zhou, Boyang Liu, Xuejin Chen, Junchen Chen, Chenglong Li, Xulai Xiao&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 23 Dec 2025 06:33:34 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25045542</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] A molecular simulation study to the effect of T313 bonding agent and crystal defects on the performance of ammonium perchlorate oxidizer</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625006913?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 30 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 262&lt;/p&gt;&lt;p&gt;Author(s): Bin Yuan, Rui Zhu, Jianfa Chen, Kuai He&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 06:33:32 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625006913</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] Mobility and sintering of silica-supported platinum clusters via reactive neural network potentials</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725005998?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Catalysis, Volume 453&lt;/p&gt;&lt;p&gt;Author(s): Tereza Benešová, Kristýna Pokorná, Andreas Erlebach, Christopher J. Heard&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Tue, 23 Dec 2025 05:56:09 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725005998</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] Machine learning–assisted discovery of chromium bis(2-pyridyl)amine catalysts for ethylene tri-/tetramerization</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725006797?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Catalysis, Volume 454&lt;/p&gt;&lt;p&gt;Author(s): Youcai Zhu, Yue Mu, Xiaoke Shi, Shu Yang, Li Sun, Zhen Liu&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Tue, 23 Dec 2025 05:56:09 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725006797</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] The influence of the organic residue and the solvent in the Schlenk equilibrium for Grignard reagents in THF. A molecular dynamics study with machine learning potentials</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725006852?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Catalysis, Volume 454&lt;/p&gt;&lt;p&gt;Author(s): Marco Bortoli, Sigbjørn Løland Bore, Odile Eisenstein, Michele Cascella&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Tue, 23 Dec 2025 05:56:09 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725006852</guid></item><item><title>[ScienceDirect Publication: Acta Materialia] DuctGPT: A Generative Transformer for Forward Screening of Ductile Refractory Multi-Principal Element Alloys</title><link>https://www.sciencedirect.com/science/article/pii/S135964542501050X?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Acta Materialia, Volume 304&lt;/p&gt;&lt;p&gt;Author(s): Sai Pranav Reddy Guduru, Mkpe O. Kekung, Ryan T. Ott, Sougata Roy, Prashant Singh&lt;/p&gt;</description><author>ScienceDirect Publication: Acta Materialia</author><pubDate>Tue, 23 Dec 2025 05:56:09 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S135964542501050X</guid></item><item><title>[ScienceDirect Publication: Acta Materialia] On-the-fly machine learning of interatomic potentials for elastic property modeling in Al–Mg–Zr solid solutions</title><link>https://www.sciencedirect.com/science/article/pii/S1359645425011310?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 15 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Acta Materialia, Volume 305&lt;/p&gt;&lt;p&gt;Author(s): Lukas Volkmer, Leonardo Medrano Sandonas, Philip Grimm, Julia Kristin Hufenbach, Gianaurelio Cuniberti&lt;/p&gt;</description><author>ScienceDirect Publication: Acta Materialia</author><pubDate>Tue, 23 Dec 2025 05:56:09 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1359645425011310</guid></item><item><title>[ScienceDirect Publication: Journal of Materiomics] Data-augmented machine learning models for oxynitride glasses &lt;em&gt;via&lt;/em&gt; 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Journal of Materiomics&lt;/p&gt;&lt;p&gt;Author(s): Mingyue Yang, Liangyu Mo, Jincheng Qin, Faqiang Zhang, Mingsheng Ma, Yongxiang Li, Zhifu Liu&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Materiomics</author><pubDate>Tue, 23 Dec 2025 05:55:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352847825001078</guid></item><item><title>[ScienceDirect Publication: Journal of Materiomics] Domain knowledge-assisted materials data anomaly detection towards constructing high-performance machine learning models</title><link>https://www.sciencedirect.com/science/article/pii/S2352847825000565?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: November 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Materiomics, Volume 11, Issue 6&lt;/p&gt;&lt;p&gt;Author(s): Yue Liu, Shuchang Ma, Zhengwei Yang, Duo Wu, Yali Zhao, Maxim Avdeev, Siqi Shi&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Materiomics</author><pubDate>Tue, 23 Dec 2025 05:55:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352847825000565</guid></item><item><title>[ScienceDirect Publication: Journal of Materiomics] PTCDA/CuS cathode enabling stable sulfide-based all-solid-state batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352847825000814?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Materiomics, Volume 12, Issue 1&lt;/p&gt;&lt;p&gt;Author(s): Zhixing Wan, Shuo Wang, Yahao Mu, Ruihua Zhou, Hang Liu, Tingwu Jin, Di Wu, Jianlong Xia, Ce-Wen Nan&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Materiomics</author><pubDate>Tue, 23 Dec 2025 05:55:54 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352847825000814</guid></item><item><title>[ScienceDirect Publication: Current Opinion in Solid State and Materials Science] Solid electrolyte-driven suppression of H2–H3 phase transition in Ni-rich cathodes for stable high-voltage cycling</title><link>https://www.sciencedirect.com/science/article/pii/S1359028625000324?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Current Opinion in Solid State and Materials Science, Volume 39&lt;/p&gt;&lt;p&gt;Author(s): Hao Chen, Hsiao-Hsuan Wu, Chia-Chen Li&lt;/p&gt;</description><author>ScienceDirect Publication: Current Opinion in Solid State and Materials Science</author><pubDate>Tue, 23 Dec 2025 05:55:53 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1359028625000324</guid></item><item><title>[ScienceDirect Publication: Current Opinion in Solid State and Materials Science] State-of-the-art review of additive friction stir deposition: microstructural evolution, machine learning applications, and future directions</title><link>https://www.sciencedirect.com/science/article/pii/S1359028625000300?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Current Opinion in Solid State and Materials Science, Volume 40&lt;/p&gt;&lt;p&gt;Author(s): Ashish Kumar, Lei Shi, Virendra Pratap Singh, Sudipta Mohapatra, Long Li, Chuansong Wu, Sergey Mironov, Amitava De&lt;/p&gt;</description><author>ScienceDirect Publication: Current Opinion in Solid State and Materials Science</author><pubDate>Tue, 23 Dec 2025 05:55:53 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1359028625000300</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Transfer learning-enhanced hybrid deep neural network model for accurate lithium-ion batteries health estimation in electric vehicles</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25045451?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 145&lt;/p&gt;&lt;p&gt;Author(s): Ibrahim AL-Wesabi, Hassan M. 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Barr, Scott P. Beckman, Brandon C. Wood, Liwen F. 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Crone&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 23 Dec 2025 05:55:51 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625007207</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Predicting hydrogen storage capacity of metal hydrides using novel imputation techniques and tree-based machine learning models</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625007335?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 30 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 262&lt;/p&gt;&lt;p&gt;Author(s): Zaid Allal, Hassan N. 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Fellman, J. Byggmästar, F. Granberg, F. Djurabekova, K. 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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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Mehdi Vaez Allaei&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 05:55:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S254252932500327X</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Revisiting thermoelectric transport in 122 Zintl phases: Anharmonic phonon renormalization and phonon localization effects</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325003359?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics, Volume 60&lt;/p&gt;&lt;p&gt;Author(s): Zhenguo Wang, Yinchang Zhao, Jun Ni, Zhenhong Dai&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Tue, 23 Dec 2025 05:55:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325003359</guid></item><item><title>[ScienceDirect Publication: Materials Today] A facile construction of LiF interlayer and F-doping &lt;em&gt;via&lt;/em&gt; 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Materials Today&lt;/p&gt;&lt;p&gt;Author(s): Hwa-Jung Kim, Jong Hoon Kim, Minseo Choi, Jung Hyun Kim, Hosun Shin, Ki Chang Kwon, Sun Hwa Park, Hyun Min Park, Seokhee Lee, Young Heon Kim, Hyeokjun Park, Seung-Wook Baek&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today</author><pubDate>Tue, 23 Dec 2025 05:55:46 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S1369702125005139</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Monoclinic Li&lt;sub&gt;2&lt;/sub&gt;ZrO&lt;sub&gt;3&lt;/sub&gt; with cationic vacancy–based ion transport channels enhanced composite polymer electrolytes for high-rate solid-state lithium metal batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009309?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Qianyi Xu, Yanru Wang, Xiang Feng, Timing Fang, Xueyan Li, Longzhou Zhang, Lijie Zhang, Daohao Li, Dongjiang Yang&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009309</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Sulfonated ether-free polybenzimidazole membrane with fast and selective ion transport enabling ultrahigh cycle stability in vanadium redox flow batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009292?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Hui Yan, Wei Wei, Xin Li, Qi-an Zhang, Ying Li, Ao Tang&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009292</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Calendar aging of sulfide all-solid-state batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009358?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Yujing Wu, Ziqi Zhang, Dengxu Wu, Fuqiang Xu, Mu Zhou, Hong Li, Liquan Chen, Fan Wu&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009358</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Energy-efficient, high-accuracy sensing in loose-fitting textile sensor matrix for LLM-enabled human-robot collaboration</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009425?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Pengfei Deng, Yang Meng, Qilong Cheng, Yuanqiu Tan, Zhihong Chen, Tian Li&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009425</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Lithium superionic solid electrolyte: Phosphorus-free sulfide glass of LiSbGe&lt;sub&gt;(4-x)/4&lt;/sub&gt;S&lt;sub&gt;4-x&lt;/sub&gt;Cl&lt;sub&gt;x&lt;/sub&gt;</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009620?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Yuna Kim, Woojung Lee, Jiyun Han, Yeong Mu Seo, Dokyung Kim, Young Joo Lee, Byung Gon Kim, Munseok S. Chae, Sung Jin Kim, In Young Kim&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009620</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Advancing high-safety and low-cost all-solid-state batteries with polyanion cathodes: Challenges and recent progress</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009978?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 148&lt;/p&gt;&lt;p&gt;Author(s): Ali Yaghtin, Atiyeh Nekahi, Jeremy I.G. Dawkins, Xia Li, Karim Zaghib, Sixu Deng&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009978</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Enabling high-accuracy lithium-ion battery status prediction via machine learning-integrated perovskite sensors</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525009851?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 148&lt;/p&gt;&lt;p&gt;Author(s): Jiaxin Sun, Xianjie Xu, Zhefu Mu, Zijun Huang, Guo Chen, Xinkai Qi, Hongwei Liu, Lei Zhu, Xiuquan Gu, Xinjian He, Sheng Huang&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525009851</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Phase boundary engineering in micro-sized Sn/SnSb anode enabling superior sodium storage: Synergistic stress relief and fast ion transport</title><link>https://www.sciencedirect.com/science/article/pii/S2211285525010249?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy, Volume 148&lt;/p&gt;&lt;p&gt;Author(s): Yuhong Liang, Chengcheng He, Zhengyang Zhao, Longqing Zhang, Rui Sun, Qian Ning, Huibing He, Yang Ren, Jing Xu, Qiang Zhang, Yajie Song, Xucai Yin&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285525010249</guid></item><item><title>[ScienceDirect Publication: Matter] Machine learning-driven ligand engineering decodes and controls structural distortions in 2D perovskites</title><link>https://www.sciencedirect.com/science/article/pii/S2590238525005259?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 10 October 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Matter&lt;/p&gt;&lt;p&gt;Author(s): Zhipeng Miao, Yahui Han, Qi Pan, Yipei Wang, Haibin Wang, Yunhang Xie, Jie Yu, Yapeng Shi, Rui Zhang, Yanlin Song, Pengwei Li&lt;/p&gt;</description><author>ScienceDirect Publication: Matter</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2590238525005259</guid></item><item><title>[ScienceDirect Publication: Matter] SpectroGen: A physically informed generative artificial intelligence for accelerated cross-modality spectroscopic materials characterization</title><link>https://www.sciencedirect.com/science/article/pii/S2590238525004771?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 14 October 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Matter&lt;/p&gt;&lt;p&gt;Author(s): Yanmin Zhu, Loza F. Tadesse&lt;/p&gt;</description><author>ScienceDirect Publication: Matter</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2590238525004771</guid></item><item><title>[ScienceDirect Publication: Matter] Precisely deciphering solid electrolyte interphase</title><link>https://www.sciencedirect.com/science/article/pii/S2590238525004114?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 5 November 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Matter, Volume 8, Issue 11&lt;/p&gt;&lt;p&gt;Author(s): Enhui Wang, Shaohua Ge, Wenbin Li, Beibei Fu, Fangyi Zhou, Weihua Chen&lt;/p&gt;</description><author>ScienceDirect Publication: Matter</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2590238525004114</guid></item><item><title>[ScienceDirect Publication: Matter] Rapid scalable plasma processing of thin-film Li–La–Zr–O solid-state electrolytes</title><link>https://www.sciencedirect.com/science/article/pii/S2590238525005119?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 5 November 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Matter, Volume 8, Issue 11&lt;/p&gt;&lt;p&gt;Author(s): Gabriel Badillo Crane, Thomas W. Colburn, Sarah E. Holmes, Justus Just, Yi Cui, Reinhold H. Dauskardt&lt;/p&gt;</description><author>ScienceDirect Publication: Matter</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2590238525005119</guid></item><item><title>[ScienceDirect Publication: Joule] Impact of metallic interlayers at the lithium-Li&lt;sub&gt;6&lt;/sub&gt;PS&lt;sub&gt;5&lt;/sub&gt;Cl solid electrolyte interface</title><link>https://www.sciencedirect.com/science/article/pii/S2542435125003563?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 19 November 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Joule, Volume 9, Issue 11&lt;/p&gt;&lt;p&gt;Author(s): Souhardh Kotakadi, Jack Aspinall, Matthew Burton, Yi Liang, Yuichi Aihara, Mauro Pasta&lt;/p&gt;</description><author>ScienceDirect Publication: Joule</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125003563</guid></item><item><title>[ScienceDirect Publication: Joule] Li–Si compound anodes enabling high-performance all-solid-state Li-ion batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2542435125003769?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 17 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Joule, Volume 9, Issue 12&lt;/p&gt;&lt;p&gt;Author(s): Do-Hyeon Kim, Young-Han Lee, Jeong-Myeong Yoon, Pugalenthiyar Thondaiman, Byung Chul Kim, In-Chul Choi, Jeong-Hee Choi, Ki-Joon Jeon, Cheol-Min Park&lt;/p&gt;</description><author>ScienceDirect Publication: Joule</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125003769</guid></item><item><title>[ScienceDirect Publication: Joule] Boosting ionic conductivity of fluoride electrolytes by polyanion coordination chemistry enabling 5 V-Class all-solid-state batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2542435125004143?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 19 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Joule&lt;/p&gt;&lt;p&gt;Author(s): Huaimin Jin, Xingyu Wang, Simeng Zhang, Xiangzhen Zhu, Chong Liu, Junyi Yue, Jie Qu, Bei Wu, Xu Han, Yueyue Wang, Yang Xu, Han Wu, Liyu Zhou, Mingying Zhang, Hao Lai, Shuo Wang, Jiangwen Liang, Xueliang Sun, Xiaona Li&lt;/p&gt;</description><author>ScienceDirect Publication: Joule</author><pubDate>Tue, 23 Dec 2025 05:55:41 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542435125004143</guid></item><item><title>[cond-mat updates on arXiv.org] CrystalFormer-CSP: Thinking Fast and Slow for Crystal Structure Prediction</title><link>https://arxiv.org/abs/2512.18251</link><description>arXiv:2512.18251v1 Announce Type: new 
 Abstract: Crystal structure prediction is a fundamental problem in materials science. We present CrystalFormer-CSP, an efficient framework that unifies data-driven heuristic and physics-driven optimization approaches to predict stable crystal structures for given chemical compositions. The approach combines pretrained generative models for space-group-informed structure generation and a universal machine learning force field for energy minimization. Reinforcement fine-tuning can be employed to further boost the accuracy of the framework. We demonstrate the effectiveness of CrystalFormer-CSP on benchmark problems and showcase its usage via web interface and language model integration.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.18251v1</guid></item><item><title>[cond-mat updates on arXiv.org] Spin Reorientation Driven Renormalization of Spin-Phonon Coupling in Fe$_4$GeTe$_2$</title><link>https://arxiv.org/abs/2512.18544</link><description>arXiv:2512.18544v1 Announce Type: new 
 Abstract: Quasi-2D van der Waals ferromagnet Fe$_4$GeTe$_2$, featuring the simultaneous presence of high Curie temperature ($T_\mathrm{C}$ $\sim 270$ K) and a spin-reorientation transition at $T_\mathrm{SR}$ $\sim 110$ K, is a rare system where strong interplay of spin dynamics, lattice vibrations, and electronic structure leads to a wide range of interesting phenomena. Here, we investigate the lattice response of exfoliated Fe$_4$GeTe$_2$ nanoflakes using temperature-dependent Raman spectroscopy. Polarization-resolved measurements reveal that, while one Raman mode exhibits a purely out-of-plane character, the rest display mixed symmetry, reflecting interlayer vibrational nonuniformity and symmetry-driven mode degeneracies. Below $T_\mathrm{C}$, phonons harden, and the linewidth narrows, consistent with reduced anharmonicity, while across the spin reorientation transition at $T_\mathrm{SR}$ they display anomalous softening, linewidth broadening, and a peak in lifetime, which are signatures of strengthened spin-phonon coupling. Complementary DFT+DMFT calculations and atomistic spin dynamical simulations reveal temperature-dependent spin excitations whose energies overlap with the Raman-active phonons, providing a natural route for the observed magnon-phonon interaction. Together, these insights establish Fe$_4$GeTe$_2$ as a versatile platform for exploring intertwined spin, lattice, and electronic degrees of freedom, with relevance for dynamic spintronic and magneto-optic functionalities near technologically meaningful temperatures.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.18544v1</guid></item><item><title>[cond-mat updates on arXiv.org] Tackling dataset curation challenges towards reliable machine learning: a case study on thermoelectric materials</title><link>https://arxiv.org/abs/2512.18653</link><description>arXiv:2512.18653v1 Announce Type: new 
 Abstract: Machine Learning (ML) driven discovery of novel and efficient thermoelectric (TE) materials warrants experimental TE datasets of high volume, diversity, and quality. While the largest publicly available dataset, Starrydata2, has a high data volume, it contains inaccurate data due to the inherent limitations of Large Language Model (LLM)-assisted data curation, ambiguous nomenclature and complex formulas of materials in the literature. Another unaddressed issue is the inclusion of multi-source experimental data, with high standard deviations and without synthesis information. Using half-Heusler (hH) materials as an example, this work is aimed at first highlighting these errors and inconsistencies which cannot be filtered with conventional dataset curation workflows. We then propose a statistical round-robin error-based data filtering method to address these issues, a method that can be applied to filter any other material property. Lastly, a hybrid dataset creation workflow, involving data from Starrydata2 and manual extraction, is proposed and the resulting dataset is analyzed and compared against Starrydata2.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 23 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.18653v1</guid></item><item><title>[cond-mat updates on arXiv.org] Topological surface phonons modulate thermal transport in semiconductor thin films</title><link>https://arxiv.org/abs/2512.18757</link><description>arXiv:2512.18757v1 Announce Type: new