From 5f00329a7501efdac93a90e13d92110ad2a635f7 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Thu, 8 Jan 2026 18:28:46 +0000
Subject: [PATCH] Auto-update RSS feed

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-<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/"><channel><title>My Customized Papers</title><link>https://github.com/your_username/your_repo</link><description>Aggregated research papers</description><language>en-US</language><lastBuildDate>Thu, 08 Jan 2026 12:44:30 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[ScienceDirect Publication: Solid State Ionics] Enhanced ionic conductivity and dielectric performance of CaB₂O₄-doped 2-hydroxyethyl cellulose polymer electrolytes for electrical double layer capacitor applications</title><link>https://www.sciencedirect.com/science/article/pii/S0167273826000019?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 March 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Solid State Ionics, Volume 436&lt;/p&gt;&lt;p&gt;Author(s): Ranaa M. Almarshedy, Siti Rohana Majid, Ninie Suhana Abdul Manan&lt;/p&gt;</description><author>ScienceDirect Publication: Solid State Ionics</author><pubDate>Thu, 08 Jan 2026 12:44:16 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0167273826000019</guid></item><item><title>[ScienceDirect Publication: Solid State Ionics] One – Step synthesis of glass ceramic Li&lt;sub&gt;6&lt;/sub&gt;PS&lt;sub&gt;5&lt;/sub&gt;Cl&lt;sub&gt;1-x&lt;/sub&gt;I&lt;sub&gt;x&lt;/sub&gt; solid electrolytes for all-solid-state batteries</title><link>https://www.sciencedirect.com/science/article/pii/S0167273825003352?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 March 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Solid State Ionics, Volume 436&lt;/p&gt;&lt;p&gt;Author(s): Nurcemal Atmaca, Mahir Uenal, Hansen Chang, Oliver Clemens&lt;/p&gt;</description><author>ScienceDirect Publication: Solid State Ionics</author><pubDate>Thu, 08 Jan 2026 12:44:16 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0167273825003352</guid></item><item><title>[Wiley: Small Methods: Table of Contents] Interfacial Stability and Design Strategies for Halide Solid Electrolytes in High‐Voltage All‐Solid‐State Sodium‐Ion Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smtd.202502179?af=R</link><description>Small Methods, EarlyView.</description><author>Wiley: Small Methods: Table of Contents</author><pubDate>Thu, 08 Jan 2026 06:35:51 GMT</pubDate><guid isPermaLink="true">10.1002/smtd.202502179</guid></item><item><title>[cond-mat updates on arXiv.org] Physically Consistent Machine Learning for Melting Temperature Prediction of Refractory High-Entropy Alloys</title><link>https://arxiv.org/abs/2601.03801</link><description>arXiv:2601.03801v1 Announce Type: new 
+<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/"><channel><title>My Customized Papers</title><link>https://github.com/your_username/your_repo</link><description>Aggregated research papers</description><language>en-US</language><lastBuildDate>Thu, 08 Jan 2026 18:28:46 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[ScienceDirect Publication: Journal of Energy Storage] Polydopamine coating on garnet-type solid electrolyte for enhancing interfacial compatibility in solid-state lithium metal batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25048753?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 28 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 148&lt;/p&gt;&lt;p&gt;Author(s): Lifeng Guan, Lian Wu, Xinyuan Li, Xuanshuo Zhang, Xiuqing Hao, Jinxiu Wen, Wei Zeng&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Thu, 08 Jan 2026 18:28:37 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25048753</guid></item><item><title>[ScienceDirect Publication: Science Bulletin] Machine learning-based diagnosis of uterine myomas and sarcomas using tumor-educated platelet transcriptomics: a retrospective multicenter study</title><link>https://www.sciencedirect.com/science/article/pii/S2095927325011600?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 15 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Science Bulletin, Volume 71, Issue 1&lt;/p&gt;&lt;p&gt;Author(s): Xudong Liu, Roujie Huang, Hua Yang, Yu Dong, Lei Li, Zhe Li, Jia Zeng, Qingxia Zhang, Yun Liu, Lei Zhang, Yidi Ma, Lin Zhang, Weijie Tian, Yan You, Yaqian Li, Tianshu Sun, Xiaoyue Zhao, Wei Liu, Le Dang, Zhibo Zhang&lt;/p&gt;</description><author>ScienceDirect Publication: Science Bulletin</author><pubDate>Thu, 08 Jan 2026 18:28:36 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2095927325011600</guid></item><item><title>[ScienceDirect Publication: Solid State Ionics] Enhanced ionic conductivity and dielectric performance of CaB₂O₄-doped 2-hydroxyethyl cellulose polymer electrolytes for electrical double layer capacitor applications</title><link>https://www.sciencedirect.com/science/article/pii/S0167273826000019?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 March 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Solid State Ionics, Volume 436&lt;/p&gt;&lt;p&gt;Author(s): Ranaa M. Almarshedy, Siti Rohana Majid, Ninie Suhana Abdul Manan&lt;/p&gt;</description><author>ScienceDirect Publication: Solid State Ionics</author><pubDate>Thu, 08 Jan 2026 12:44:16 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0167273826000019</guid></item><item><title>[ScienceDirect Publication: Solid State Ionics] One – Step synthesis of glass ceramic Li&lt;sub&gt;6&lt;/sub&gt;PS&lt;sub&gt;5&lt;/sub&gt;Cl&lt;sub&gt;1-x&lt;/sub&gt;I&lt;sub&gt;x&lt;/sub&gt; solid electrolytes for all-solid-state batteries</title><link>https://www.sciencedirect.com/science/article/pii/S0167273825003352?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 1 March 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Solid State Ionics, Volume 436&lt;/p&gt;&lt;p&gt;Author(s): Nurcemal Atmaca, Mahir Uenal, Hansen Chang, Oliver Clemens&lt;/p&gt;</description><author>ScienceDirect Publication: Solid State Ionics</author><pubDate>Thu, 08 Jan 2026 12:44:16 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0167273825003352</guid></item><item><title>[Wiley: Small Methods: Table of Contents] Interfacial Stability and Design Strategies for Halide Solid Electrolytes in High‐Voltage All‐Solid‐State Sodium‐Ion Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smtd.202502179?af=R</link><description>Small Methods, EarlyView.</description><author>Wiley: Small Methods: Table of Contents</author><pubDate>Thu, 08 Jan 2026 06:35:51 GMT</pubDate><guid isPermaLink="true">10.1002/smtd.202502179</guid></item><item><title>[cond-mat updates on arXiv.org] Physically Consistent Machine Learning for Melting Temperature Prediction of Refractory High-Entropy Alloys</title><link>https://arxiv.org/abs/2601.03801</link><description>arXiv:2601.03801v1 Announce Type: new 
 Abstract: Predicting the melting temperature (Tm) of multi-component and high-entropy alloys (HEAs) is critical for high-temperature applications but computationally expensive using traditional CALPHAD or DFT methods. In this work, we develop a gradient-boosted decision tree (XGBoost) model to predict Tm for complex alloys based on elemental properties. To ensure physical consistency, we address the issue of data leakage by excluding temperature-dependent thermodynamic descriptors (such as Gibbs free energy of mixing) and instead rely on physically motivated elemental features. The optimized model achieves a coefficient of determination (R2) of 0.948 and a Mean Squared Error (MSE) of 9928 which is about 5% relative error for HEAs on a validation set of approximately 1300 compositions. Crucially, we validate the model using the Valence Electron Concentration (VEC) rule. Without explicit constraints during training, the model successfully captures the known stability transition between BCC and FCC phases at a VEC of approximately 6.87. These results demonstrate that data-driven models, when properly feature-engineered, can capture fundamental metallurgical principles for rapid alloy screening.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 08 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.03801v1</guid></item><item><title>[cond-mat updates on arXiv.org] Material exploration through active learning -- METAL</title><link>https://arxiv.org/abs/2601.03933</link><description>arXiv:2601.03933v1 Announce Type: new 
 Abstract: The discovery and design of new materials are paramount in the development of green technologies. High entropy oxides represent one such group that has only been tentatively explored, mainly due to the inherent problem of navigating vast compositional spaces. Thanks to the emergence of machine learning, however, suitable tools are now readily available. Here, the task of finding oxygen carriers for chemical looping processes has been tackled by leveraging active learning-based strategies combined with first-principles calculations. High efficiency and efficacy have, moreover, been achieved by exploiting the power of recently developed machine learning interatomic potentials. Firstly, the proposed approaches were validated based on an established computational framework for identifying high entropy perovskites that can be used in chemical looping air separation and dry reforming. Chief among the insights thus gained was the identification of the best performing strategies, in the form of greedy or Thompson-based sampling based on uncertainty estimates obtained from Gaussian processes. Building on this newfound knowledge, the concept was applied to a more complex problem, namely the discovery of high entropy oxygen carriers for chemical looping oxygen uncoupling. This resulted in both qualitative as well as quantitative outcomes, including lists of specific materials with high oxygen transfer capacities and configurational entropies. Specifically, the best candidates were based on the known oxygen carrier CaMnO3 but also contained a variety of additional species, of which some, e.g., Ti; Co; Cu; and Ti, were expected while others were not, e.g., Y and Sm. The results suggest that adopting active learning approaches is critical in materials discovery, given that these methods are already shifting research practice and soon will be the norm.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 08 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.03933v1</guid></item><item><title>[cond-mat updates on arXiv.org] Transport properties in a model of confined granular mixtures at moderate densities</title><link>https://arxiv.org/abs/2601.04026</link><description>arXiv:2601.04026v1 Announce Type: new 
 Abstract: This work derives the Navier--Stokes hydrodynamic equations for a model of a confined, quasi-two-dimensional, $s$-component mixture of inelastic, smooth, hard spheres. Using the inelastic version of the revised Enskog theory, macroscopic balance equations for mass, momentum, and energy are obtained, and constitutive equations for the fluxes are determined through a first-order Chapman--Enskog expansion. As for elastic collisions, the transport coefficients are given in terms of the solutions of a set of coupled linear integral equations. Approximate solutions to these equations for diffusion transport coefficients and shear viscosity are achieved by assuming steady-state conditions and considering leading terms in a Sonine polynomial expansion. These transport coefficients are expressed in terms of the coefficients of restitution, concentration, the masses and diameters of the mixture's components, and the system's density. The results apply to moderate densities and are not limited to particular values of the coefficients of restitution, concentration, mass, and/or diameter ratios. As an application, the thermal diffusion factor is evaluated to analyze segregation driven by temperature gradients and gravity, providing criteria that distinguish whether larger particles accumulate near the hotter or colder boundaries.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 08 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.04026v1</guid></item><item><title>[cond-mat updates on arXiv.org] libMobility: A Python library for hydrodynamics at the Smoluchowski level</title><link>https://arxiv.org/abs/2510.02135</link><description>arXiv:2510.02135v2 Announce Type: replace 
 Abstract: Effective hydrodynamic modeling is crucial for accurately predicting fluid-particle interactions in diverse fields such as biophysics and materials science. Developing and implementing hydrodynamic algorithms is challenging due to the complexity of fluid dynamics, necessitating efficient management of large-scale computations and sophisticated boundary conditions. Furthermore, adapting these algorithms for use on massively parallel architectures like GPUs adds an additional layer of complexity. This paper presents the libMobility software library, which offers a suite of CUDA-enabled solvers for simulating hydrodynamic interactions in particulate systems at the Rotne-Prager-Yamakawa (RPY) level. The library facilitates precise simulations of particle displacements influenced by external forces and torques, including both the deterministic and stochastic components. Notable features of libMobility include its ability to handle linear and angular displacements, thermal fluctuations, and various domain geometries effectively. With an interface in Python, libMobility provides comprehensive tools for researchers in computational fluid dynamics and related fields to simulate particle mobility efficiently. This article details the technical architecture, functionality, and wide-ranging applications of libMobility. libMobility is available at https://github.com/stochasticHydroTools/libMobility.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 08 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2510.02135v2</guid></item><item><title>[cond-mat updates on arXiv.org] Kinetic Flux Equations for Ion Exchange in Silicate Glasses</title><link>https://arxiv.org/abs/2601.03207</link><description>arXiv:2601.03207v2 Announce Type: replace 
 Abstract: Ion exchange kinetic flux equations have been extensively investigated since the mid-twentieth century and continue to provide a fundamental framework for describing mass transport phenomena in solid materials. Despite the maturity of this field, inconsistencies remain in the literature concerning the definition, dimensional consistency, and physical interpretation of the parameters involved. A rigorous and unified treatment of these equations is therefore essential to ensure the reproducibility and comparability of theoretical and experimental studies. The present study aims to establish a coherent and systematic development of ion exchange kinetic flux equations, with particular emphasis on the consistent definition and dimensional formulation of the relevant physical quantities. Beyond refining the theoretical foundations, this study extends the classical formulation by incorporating the influence of mechanical stress on ion transport and considering cross-term interactions within the framework of linear irreversible thermodynamics. These developments provide a more comprehensive description of ion exchange kinetics, particularly as applied to silicate glasses, where coupling between chemical and mechanical effects plays a crucial role in determining transport behavior and performance.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 08 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.03207v2</guid></item><item><title>[cond-mat updates on arXiv.org] Agentic Exploration of Physics Models</title><link>https://arxiv.org/abs/2509.24978</link><description>arXiv:2509.24978v4 Announce Type: replace-cross 
 Abstract: The process of scientific discovery relies on an interplay of observations, analysis, and hypothesis generation. Machine learning is increasingly being adopted to address individual aspects of this process. However, it remains an open challenge to fully automate the heuristic, iterative loop required to discover the laws of an unknown system by exploring it through experiments and analysis, without tailoring the approach to the specifics of a given task. Here, we introduce SciExplorer, an agent that leverages large language model tool-use capabilities to enable exploration of systems without any domain-specific blueprints, and apply it to physical systems that are initially unknown to the agent. We test SciExplorer on a broad set of models spanning mechanical dynamical systems, wave evolution, and quantum many-body physics. Despite using a minimal set of tools, primarily based on code execution, we observe impressive performance on tasks such as recovering equations of motion from observed dynamics and inferring Hamiltonians from expectation values. The demonstrated effectiveness of this setup opens the door towards similar scientific exploration in other domains, without the need for finetuning or task-specific instructions.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 08 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2509.24978v4</guid></item><item><title>[cond-mat updates on arXiv.org] Masgent: An AI-assisted Materials Simulation Agent</title><link>https://arxiv.org/abs/2512.23010</link><description>arXiv:2512.23010v2 Announce Type: replace-cross 
-Abstract: Density functional theory (DFT) and machine learning potentials (MLPs) are essential for predicting and understanding materials properties, yet preparing, executing, and analyzing these simulations typically requires extensive scripting, multi-step procedures, and significant high-performance computing (HPC) expertise. These challenges hinder reproducibility and slow down discovery. Here, we introduce Masgent, an AI-assisted materials simulation agent that unifies structure manipulation, automated VASP input generation, DFT workflow construction and analysis, fast MLP-based simulations, and lightweight machine learning (ML) utilities within a single platform. Powered by large language models (LLMs), Masgent enables researchers to perform complex simulation tasks through natural-language interaction, eliminating most manual scripting and reducing setup time from hours to seconds. By standardizing protocols and integrating advanced simulation and data-driven tools, Masgent lowers the barrier to performing state-of-the-art computational methodologies, enabling faster hypothesis testing, pre-screening, and exploratory research for both new and experienced practitioners.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 08 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.23010v2</guid></item><item><title>[ChemRxiv] The unique example of approximation of the electronic term of diatomic molecules by Morse potential. HF, DF, TF.</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-z97tt?rft_dat=source%3Ddrss</link><description>The Morse approximations M1(r) and M2(r) of the 1Σ+ ground state potential curves of three hydrofluoride isotopologues are analyzed. Qualitative differences between HF and heavy isotopologues were found. The result of the HF approximation is a function mainly described by the characteristics of a simple term. For HF, the anharmonicity M1(r) is lower than for M2(r), ωехе&lt;ωехе' and De&gt;D', therefore the curve M1(r) lies above U(r). The M2(r) model is constructed from the values of ωе and De, using the equation De=ωе2/4ωехе, so that its potential curve lies below U(r). For DF and TF, the anharmonicity of M1(r) is greater than of M2(r), ωехе&gt;ωехе' and De&lt;D', therefore, the curve M1(r) lies below U(r) and is outside its potential well with possible intersections. For DF and TF this results in the emergence of inversion of anharmonicity. The shape of U(r) for all three molecules is well described by the Morse formula, their parameters are close to each other, and the differences between HF and heavy isotopologues are small. The differences between the extrapolated and true De values for HF, DF, TF are 290, -230, -460 cm-1, and the differences between the values of anharmonicities ωехе and ωехе' are -0.51, 0.22, 0.30 cm-1, i.e. within 0.5‒1%. In the plot of the differences δ(r)≡U(r)‒M(r), the curves M2(r) of the three isotopologues are in the upper half-plane. The curve δ(r) M1(r) HF, which has not experienced an inversion, is also located there due to the small differences ∆ωехe and ∆De. It has the Morse potential shape, similar to M2(r), and its amplitude is lower than that for M2(r). For DF and TF, the amplitude of δ(r) for M1(r) is greater than δ(r) for M2(r), and their maxima coincide. Inversion of anharmonicity changes only the model term, therefore, a section of the δ(r) curves distorted by the Herzberg anomaly remains in the negative half-plane, because of the broadening of the U(r) term in the lower part of the potential well. This area occupies about half of its depth.</description><author>ChemRxiv</author><pubDate>Thu, 08 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-z97tt?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Machine-Learning-Accelerated Simulations of Vibrational Activation for Controlled Photoisomerization in a Molecular Motor</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-wlm7r?rft_dat=source%3Ddrss</link><description>The precise manipulation of photochemical reactions across broad configurational spaces requires sophisticated design of external control fields. Using the photoisomerization of a molecular motor as a prototype, this study integrates enhanced sampling and active learning to construct accurate machine-learned multi-state potential energy surfaces (PESs). By combining active-learning trajectories with enhanced sampling, our approach efficiently covers substantial reaction regions, enabling trajectory propagation extending to tens of picoseconds at a low computational cost within the machine learning framework. Furthermore, local control theory (LCT) is employed to selectively activate specific vibrational motions, leading to accelerated access to reactive regions, enhanced nonadiabatic transitions, and significantly improved selectivity toward the dominant photoproduct. This combined strategy of machine-learning potentials and LCT offers an efficient and generalizable framework for controlling excited-state dynamics in complex systems.</description><author>ChemRxiv</author><pubDate>Thu, 08 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-wlm7r?rft_dat=source%3Ddrss</guid></item><item><title>[RSC - Chem. Sci. latest articles] Dynamic Protein Structures in Solution: Decoding the Amide I Band with 2D-IR Spectral Libraries and Machine Learning</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC09973K</link><description>&lt;div&gt;&lt;i&gt;&lt;b&gt;Chem. Sci.&lt;/b&gt;&lt;/i&gt;, 2026, Accepted Manuscript&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5SC09973K, Edge Article&lt;/div&gt;&lt;div&gt;&lt;img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /&gt; Open Access&lt;/div&gt;&lt;div&gt;&lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt; &lt;img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Amy Farmer, Kelly Brown, Sophie E.T. Kendall-Price, Partha Malakar, Gregory M Greetham, Neil Hunt&lt;br /&gt;The dynamic three-dimensional structures of proteins dictate their function, but accessing structures in solution at physiological temperatures is challenging. Ultrafast 2D-IR spectroscopy of the protein amide I band produces a...&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Chem. Sci. latest articles</author><pubDate>Thu, 08 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC09973K</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Optimization of porous electrode configuration for organic redox flow battery by machine learning based on back propagation neural network based on firefly</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25047668?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 28 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 148&lt;/p&gt;&lt;p&gt;Author(s): Fengming Chu, Yongzhuo Wang, Xi Liu, Tong Liu&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Wed, 07 Jan 2026 18:32:48 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25047668</guid></item><item><title>[ScienceDirect Publication: Progress in Materials Science] Advanced simulations from DFT to machine learning for solid-state hydrogen storage: fundamentals, progresses, challenges and perspectives</title><link>https://www.sciencedirect.com/science/article/pii/S0079642525002336?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 6 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Progress in Materials Science&lt;/p&gt;&lt;p&gt;Author(s): Shuling Chen, Mei Yang, Shaoyang Shen, Liuzhang Ouyang&lt;/p&gt;</description><author>ScienceDirect Publication: Progress in Materials Science</author><pubDate>Wed, 07 Jan 2026 18:32:44 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0079642525002336</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Dynamic Li‐S Coordination Boosted Superionic Conduction in Cubic LiBS2 Solid Electrolyte</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202527133?af=R</link><description>Advanced Functional Materials, EarlyView.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Wed, 07 Jan 2026 15:35:52 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202527133</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Sulfonated Cellulose Acetate Nanofibers Induced Zincophilic‐Hydrophobic Interface to Regulate Ion Transport for Long‐Lifespan Zinc‐Iodine Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202522067?af=R</link><description>Advanced Science, EarlyView.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Wed, 07 Jan 2026 15:22:47 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202522067</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Outside Back Cover: Rhodopsin‐Mimicking Reversible Photo‐Switchable Chloride Channels Based on Azobenzene‐Appended Semiaza‐Bambusurils for Light‐Controlled Ion Transport and Cancer Cell Apoptosis</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.2025-m0501054600?af=R</link><description>Angewandte Chemie International Edition, EarlyView.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Wed, 07 Jan 2026 05:23:27 GMT</pubDate><guid isPermaLink="true">10.1002/anie.2025-m0501054600</guid></item><item><title>[Wiley: ENERGY &amp; ENVIRONMENTAL MATERIALS: Table of Contents] Thermodynamic Mechanisms of Co‐S Bond Anchoring in Few‐Layered 1T‐MoS2 for Enhanced Capacitive Performance via Spin State Regulation and Ion Diffusion Kinetics</title><link>https://onlinelibrary.wiley.com/doi/10.1002/eem2.70218?af=R</link><description>ENERGY &amp;amp;ENVIRONMENTAL MATERIALS, EarlyView.</description><author>Wiley: ENERGY &amp; ENVIRONMENTAL MATERIALS: Table of Contents</author><pubDate>Wed, 07 Jan 2026 05:20:14 GMT</pubDate><guid isPermaLink="true">10.1002/eem2.70218</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Customizing Ion Transport by Anionphilic Nanofiber‐Polymer Electrolyte for Stable Zinc Metal Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202519057?af=R</link><description>Advanced Materials, EarlyView.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Wed, 07 Jan 2026 05:17:00 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202519057</guid></item><item><title>[cond-mat updates on arXiv.org] A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse design</title><link>https://arxiv.org/abs/2601.02424</link><description>arXiv:2601.02424v1 Announce Type: new 
+Abstract: Density functional theory (DFT) and machine learning potentials (MLPs) are essential for predicting and understanding materials properties, yet preparing, executing, and analyzing these simulations typically requires extensive scripting, multi-step procedures, and significant high-performance computing (HPC) expertise. These challenges hinder reproducibility and slow down discovery. Here, we introduce Masgent, an AI-assisted materials simulation agent that unifies structure manipulation, automated VASP input generation, DFT workflow construction and analysis, fast MLP-based simulations, and lightweight machine learning (ML) utilities within a single platform. Powered by large language models (LLMs), Masgent enables researchers to perform complex simulation tasks through natural-language interaction, eliminating most manual scripting and reducing setup time from hours to seconds. By standardizing protocols and integrating advanced simulation and data-driven tools, Masgent lowers the barrier to performing state-of-the-art computational methodologies, enabling faster hypothesis testing, pre-screening, and exploratory research for both new and experienced practitioners.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 08 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.23010v2</guid></item><item><title>[ChemRxiv] The unique example of approximation of the electronic term of diatomic molecules by Morse potential. HF, DF, TF.</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-z97tt?rft_dat=source%3Ddrss</link><description>The Morse approximations M1(r) and M2(r) of the 1Σ+ ground state potential curves of three hydrofluoride isotopologues are analyzed. Qualitative differences between HF and heavy isotopologues were found. The result of the HF approximation is a function mainly described by the characteristics of a simple term. For HF, the anharmonicity M1(r) is lower than for M2(r), ωехе&lt;ωехе' and De&gt;D', therefore the curve M1(r) lies above U(r). The M2(r) model is constructed from the values of ωе and De, using the equation De=ωе2/4ωехе, so that its potential curve lies below U(r). For DF and TF, the anharmonicity of M1(r) is greater than of M2(r), ωехе&gt;ωехе' and De&lt;D', therefore, the curve M1(r) lies below U(r) and is outside its potential well with possible intersections. For DF and TF this results in the emergence of inversion of anharmonicity. The shape of U(r) for all three molecules is well described by the Morse formula, their parameters are close to each other, and the differences between HF and heavy isotopologues are small. The differences between the extrapolated and true De values for HF, DF, TF are 290, -230, -460 cm-1, and the differences between the values of anharmonicities ωехе and ωехе' are -0.51, 0.22, 0.30 cm-1, i.e. within 0.5‒1%. In the plot of the differences δ(r)≡U(r)‒M(r), the curves M2(r) of the three isotopologues are in the upper half-plane. The curve δ(r) M1(r) HF, which has not experienced an inversion, is also located there due to the small differences ∆ωехe and ∆De. It has the Morse potential shape, similar to M2(r), and its amplitude is lower than that for M2(r). For DF and TF, the amplitude of δ(r) for M1(r) is greater than δ(r) for M2(r), and their maxima coincide. Inversion of anharmonicity changes only the model term, therefore, a section of the δ(r) curves distorted by the Herzberg anomaly remains in the negative half-plane, because of the broadening of the U(r) term in the lower part of the potential well. This area occupies about half of its depth.</description><author>ChemRxiv</author><pubDate>Thu, 08 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-z97tt?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Machine-Learning-Accelerated Simulations of Vibrational Activation for Controlled Photoisomerization in a Molecular Motor</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-wlm7r?rft_dat=source%3Ddrss</link><description>The precise manipulation of photochemical reactions across broad configurational spaces requires sophisticated design of external control fields. Using the photoisomerization of a molecular motor as a prototype, this study integrates enhanced sampling and active learning to construct accurate machine-learned multi-state potential energy surfaces (PESs). By combining active-learning trajectories with enhanced sampling, our approach efficiently covers substantial reaction regions, enabling trajectory propagation extending to tens of picoseconds at a low computational cost within the machine learning framework. Furthermore, local control theory (LCT) is employed to selectively activate specific vibrational motions, leading to accelerated access to reactive regions, enhanced nonadiabatic transitions, and significantly improved selectivity toward the dominant photoproduct. This combined strategy of machine-learning potentials and LCT offers an efficient and generalizable framework for controlling excited-state dynamics in complex systems.</description><author>ChemRxiv</author><pubDate>Thu, 08 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-wlm7r?rft_dat=source%3Ddrss</guid></item><item><title>[RSC - Chem. Sci. latest articles] Dynamic Protein Structures in Solution: Decoding the Amide I Band with 2D-IR Spectral Libraries and Machine Learning</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC09973K</link><description>&lt;div&gt;&lt;i&gt;&lt;b&gt;Chem. Sci.&lt;/b&gt;&lt;/i&gt;, 2026, Accepted Manuscript&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5SC09973K, Edge Article&lt;/div&gt;&lt;div&gt;&lt;img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /&gt; Open Access&lt;/div&gt;&lt;div&gt;&lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt; &lt;img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Amy Farmer, Kelly Brown, Sophie E.T. Kendall-Price, Partha Malakar, Gregory M Greetham, Neil Hunt&lt;br /&gt;The dynamic three-dimensional structures of proteins dictate their function, but accessing structures in solution at physiological temperatures is challenging. Ultrafast 2D-IR spectroscopy of the protein amide I band produces a...&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Chem. Sci. latest articles</author><pubDate>Thu, 08 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC09973K</guid></item><item><title>[ChemRxiv] Atomistic insights into the chemical stability and ionic transport at Li-metal/Li-Argyrodite interfaces</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-5d9nq-v2?rft_dat=source%3Ddrss</link><description>All solid-state batteries (ASSBs) based on solid-state electrolytes (SSEs) are a novel Li-ion battery technology with the potential of enhanced safety, longer lifetimes, and increased energy density when coupled with the Li-metal anode. Li-Argyrodite (Li6PS5Cl) is a promising SSE with high ionic conductivity, produced using cheap and sustainable precursors, and therefore of interest to both academia and industry. Like many other sulfide-based SSEs, it is however unstable against Li-metal. Using ab-initio and machine-learning methods, we simulate three representative Li-metal/Li-Argyrodite interface models to investigate whether the exact surface termination affects the chemical stability and ion transport capability. We present a systematic approach to create low-energy interfaces by screening 28 low Miller-index surface terminations of Li-argyrodite and coupling them with Li-metal. Custom-made machine-learned interatomic potentials trained on ab-initio data enable the simulation of large interface models with over 2000 atoms for 5 ns. We find that all three interfaces decompose into an amorphous solid-electrolyte interphase (SEI) layer, consisting of Li3P, Li2S and LiCl, which then crystallizes into an antifluorite phase Li2S{1-x-y}P{x}Cl{y}; {x,y = 0.14-0.15}. A two orders of magnitude decrease in Li-ion flux shows that the crystalline SEI layer is a sluggish ion conductor, similar to Li2S. While all three interfaces form the same crystalline SEI layer, the exact rates of the decomposition and crystallisation depend on the actual surface composition. These atomic-level insights could potentially be used to control the SEI formation in sulphide-based SSEs and others.</description><author>ChemRxiv</author><pubDate>Thu, 08 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-5d9nq-v2?rft_dat=source%3Ddrss</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Optimization of porous electrode configuration for organic redox flow battery by machine learning based on back propagation neural network based on firefly</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25047668?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 28 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 148&lt;/p&gt;&lt;p&gt;Author(s): Fengming Chu, Yongzhuo Wang, Xi Liu, Tong Liu&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Wed, 07 Jan 2026 18:32:48 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25047668</guid></item><item><title>[ScienceDirect Publication: Progress in Materials Science] Advanced simulations from DFT to machine learning for solid-state hydrogen storage: fundamentals, progresses, challenges and perspectives</title><link>https://www.sciencedirect.com/science/article/pii/S0079642525002336?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 6 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Progress in Materials Science&lt;/p&gt;&lt;p&gt;Author(s): Shuling Chen, Mei Yang, Shaoyang Shen, Liuzhang Ouyang&lt;/p&gt;</description><author>ScienceDirect Publication: Progress in Materials Science</author><pubDate>Wed, 07 Jan 2026 18:32:44 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0079642525002336</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Dynamic Li‐S Coordination Boosted Superionic Conduction in Cubic LiBS2 Solid Electrolyte</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202527133?af=R</link><description>Advanced Functional Materials, EarlyView.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Wed, 07 Jan 2026 15:35:52 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202527133</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Sulfonated Cellulose Acetate Nanofibers Induced Zincophilic‐Hydrophobic Interface to Regulate Ion Transport for Long‐Lifespan Zinc‐Iodine Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202522067?af=R</link><description>Advanced Science, EarlyView.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Wed, 07 Jan 2026 15:22:47 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202522067</guid></item><item><title>[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Machine Learning Prediction of Two-Dimensional Polymerization of Nitrogen in FeNx</title><link>http://dx.doi.org/10.1021/acs.jpclett.5c03557</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpclett.5c03557/asset/images/medium/jz5c03557_0007.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;The Journal of Physical Chemistry Letters&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/acs.jpclett.5c03557&lt;/div&gt;</description><author>The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)</author><pubDate>Wed, 07 Jan 2026 15:15:41 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpclett.5c03557</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Outside Back Cover: Rhodopsin‐Mimicking Reversible Photo‐Switchable Chloride Channels Based on Azobenzene‐Appended Semiaza‐Bambusurils for Light‐Controlled Ion Transport and Cancer Cell Apoptosis</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.2025-m0501054600?af=R</link><description>Angewandte Chemie International Edition, EarlyView.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Wed, 07 Jan 2026 05:23:27 GMT</pubDate><guid isPermaLink="true">10.1002/anie.2025-m0501054600</guid></item><item><title>[Wiley: ENERGY &amp; ENVIRONMENTAL MATERIALS: Table of Contents] Thermodynamic Mechanisms of Co‐S Bond Anchoring in Few‐Layered 1T‐MoS2 for Enhanced Capacitive Performance via Spin State Regulation and Ion Diffusion Kinetics</title><link>https://onlinelibrary.wiley.com/doi/10.1002/eem2.70218?af=R</link><description>ENERGY &amp;amp;ENVIRONMENTAL MATERIALS, EarlyView.</description><author>Wiley: ENERGY &amp; ENVIRONMENTAL MATERIALS: Table of Contents</author><pubDate>Wed, 07 Jan 2026 05:20:14 GMT</pubDate><guid isPermaLink="true">10.1002/eem2.70218</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Customizing Ion Transport by Anionphilic Nanofiber‐Polymer Electrolyte for Stable Zinc Metal Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202519057?af=R</link><description>Advanced Materials, EarlyView.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Wed, 07 Jan 2026 05:17:00 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202519057</guid></item><item><title>[cond-mat updates on arXiv.org] A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse design</title><link>https://arxiv.org/abs/2601.02424</link><description>arXiv:2601.02424v1 Announce Type: new 
 Abstract: The synthesis of nanocrystals has been highly dependent on trial-and-error, due to the complex correlation between synthesis parameters and physicochemical properties. Although deep learning offers a potential methodology to achieve generative inverse design, it is still hindered by the scarcity of high-quality datasets that align nanocrystal synthesis routes with their properties. Here, we present the construction of a large-scale, aligned Nanocrystal Synthesis-Property (NSP) database and demonstrate its capability for generative inverse design. To extract structured synthesis routes and their corresponding product properties from literature, we develop NanoExtractor, a large language model (LLM) enhanced by well-designed augmentation strategies. NanoExtractor is validated against human experts, achieving a weighted average score of 88% on the test set, significantly outperforming chemistry-specialized (3%) and general-purpose LLMs (38%). The resulting NSP database contains nearly 160,000 aligned entries and serves as training data for our NanoDesigner, an LLM for inverse synthesis design. The generative capability of NanoDesigner is validated through the successful design of viable synthesis routes for both well-established PbSe nanocrystals and rarely reported MgF2 nanocrystals. Notably, the model recommends a counter-intuitive, non-stoichiometric precursor ratio (1:1) for MgF2 nanocrystals, which is experimentally confirmed as critical for suppressing byproducts. Our work bridges the gap between unstructured literature and data-driven synthesis, and also establishes a powerful human-AI collaborative paradigm for accelerating nanocrystal discovery.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.02424v1</guid></item><item><title>[cond-mat updates on arXiv.org] Protein-Water Energy Transfer via Anharmonic Low-Frequency Vibrations</title><link>https://arxiv.org/abs/2601.02699</link><description>arXiv:2601.02699v1 Announce Type: new 
 Abstract: Heat dissipation is ubiquitous in living systems, which constantly convert distinct forms of energy into each other. The transport of thermal energy in liquids and even within proteins is well understood but kinetic energy transfer across a heterogeneous molecular boundary provides additional challenges. Here, we use atomistic molecular dynamics simulations under steady-state conditions to analyze how a protein dissipates surplus thermal energy into the surrounding solvent. We specifically focus on collective degrees of freedom that govern the dynamics of the system from the diffusive regime to mid-infrared frequencies. Using a fully anharmonic analysis of molecular vibrations, we analyzed their vibrational spectra, temperatures, and heat transport efficiencies. We find that the most efficient energy transfer mechanisms are associated with solvent-mediated friction. However, this mechanism only applies to a small number of degrees of freedom of a protein. Instead, less efficient vibrational energy transfer in the far-infrared dominates heat transfer overall due to a large number of vibrations in this frequency range. A notable by-product of this work is a highly sensitive measure of deviations from energy equi-partition in equilibrium systems, which can be used to analyze non-ergodic properties.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.02699v1</guid></item><item><title>[cond-mat updates on arXiv.org] Interplay of Structure and Dynamics in Solid Polymer Electrolytes: a Molecular Dynamics Study of LiPF6/polypropylene carbonate</title><link>https://arxiv.org/abs/2601.02869</link><description>arXiv:2601.02869v1 Announce Type: new 
 Abstract: Solid-state batteries (SSB) are emerging as next-generation electrochemical energy storage devices. Achieving high energy density in SSB relies on solid polymer electrolytes (SPE) that are electrochemically stable against both lithium metal and high-potential positive electrodes, two conditions that are difficult to satisfy without chemical degradation. In this work, molecular dynamics simulations are employed to investigate the relationship between structure and dynamics in carbonate-based SPE composed of polypropylene carbonate and lithium hexafluorophosphate (LiPF$_6$), at salt concentrations ranging from 0.32 to 1.21 mol$/$kg. Structural properties are analyzed under ambient pressure at the experimentally relevant temperature $T = 353$ K. Since the slow dynamical processes governing ion transport in these systems are inaccessible to direct molecular dynamics, transport properties are simulated at elevated temperatures up to 900 K and extrapolated to $T = 353$ K using Arrhenius behavior. The results reveal strong ionic correlations, a limited fraction of free ions, and a predominance of negatively charged clusters, especially at high salt concentration. At high temperature, the self-diffusion coefficient of Li$^+$ exceeds that of PF$_6^-$ due to weaker Li$^+$-carbonate and ion-ion interactions. However, at $T = 353$ K, Li$^+$ mobility becomes lower than that of the anion, consistent with typical experimental observations in SPE. As expected, the ionic conductivity $\sigma$ increases with temperature, while at $T = 353$ K it exhibits a maximum for salt concentrations between 1.0 and 1.1 mol$/$kg. Overall, the estimated physico-chemical parameters highlight the key role of ion correlations in SPE and suggest strategies to optimize electrolyte performance. The Arrhenius extrapolation approach used here provides valuable insight into ion transport mechanisms in solid polymer electrolytes.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.02869v1</guid></item><item><title>[cond-mat updates on arXiv.org] DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations</title><link>https://arxiv.org/abs/2601.02938</link><description>arXiv:2601.02938v1 Announce Type: new