diff --git a/filtered_feed.xml b/filtered_feed.xml
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+++ b/filtered_feed.xml
@@ -1,5 +1,5 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/"><channel><title>My Customized Papers</title><link>https://github.com/your_username/your_repo</link><description>Aggregated research papers</description><language>en-US</language><lastBuildDate>Fri, 09 Jan 2026 06:33:40 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] Fluctuation conductivity in ultraclean multicomponent superconductors</title><link>https://arxiv.org/abs/2601.04308</link><description>arXiv:2601.04308v1 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>Fri, 09 Jan 2026 12:43:24 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] Fluctuation conductivity in ultraclean multicomponent superconductors</title><link>https://arxiv.org/abs/2601.04308</link><description>arXiv:2601.04308v1 Announce Type: new 
 Abstract: We consider the intrinsic fluctuation conductivity in metals with multiply sheeted Fermi surfaces approaching a superconducting critical point. Restricting our attention to extreme type-II multicomponent superconductors motivates focusing on the ultraclean limit. Using functional-integral techniques, we derive the Gaussian fluctuation action from which we obtain the gauge-invariant electromagnetic linear response kernel. This allows us to compute the optical conductivity tensor. We identify essential conditions required for a nonzero longitudinal conductivity at finite frequencies in a disorder-free and translationally invariant system. Specifically, this is neither related to impurity scattering nor electron-phonon interaction, but derives indirectly from the multicomponent character of the incipient superconducting order and the parent metallic state. Under these conditions, the enhancement of the DC conductivity due to fluctuations close to the critical point follows the same critical behaviour as in the diffusive limit.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.04308v1</guid></item><item><title>[cond-mat updates on arXiv.org] Towards understanding the defect properties in the multivalent A-site Na$_{0.5}$Bi$_{0.5}$TiO$_3$-based perovskite ceramics</title><link>https://arxiv.org/abs/2601.04725</link><description>arXiv:2601.04725v1 Announce Type: new 
 Abstract: A defect model involving cation and anion vacancies and anti-site defects is proposed that accounts for the non-stoichiometry of multi-valent $A$-site Na$_{0.5}$Bi$_{0.5}$TiO$_3$ based perovskite oxides with $ABO_3$ composition. A series of samples with varying $A$-site non-stoichiometry and $A$:$B$ ratios were prepared to investigate their electrical conductivity. The oxygen partial pressure and temperature dependent conductivities where studied with direct current (dc) and alternating current (ac) techniques, enabling to separate between ionic and electronic conduction. The Na-excess samples, regardless of the $A$:$B$ ratio, exhibit dominant ionic conductivity and $p$-type electronic conduction, with the highest total conductivity reaching $4 \times 10^{-4}$ S/cm at 450$^\circ$C. In contrast, the Bi-excess samples display more insulating characteristics and $n$-type electronic conductivity, with conductivity values within the 10$^{-8}$ S/cm range at 450$^\circ$C. These conductivity results strongly support the proposed defect model, which offers a straightforward description of defect chemistry in NBT-based ceramics and serves as a valuable guide for optimizing sample processing to achieve tailored properties.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.04725v1</guid></item><item><title>[cond-mat updates on arXiv.org] Scalable Dielectric Tensor Predictions for Inorganic Materials using Equivariant Graph Neural Networks</title><link>https://arxiv.org/abs/2601.04755</link><description>arXiv:2601.04755v1 Announce Type: new 
 Abstract: Accurate prediction of dielectric tensors is essential for accelerating the discovery of next-generation inorganic dielectric materials. Existing machine learning approaches, such as equivariant graph neural networks, typically rely on specially-designed network architectures to enforce O(3) equivariance. However, to preserve equivariance, these specially-designed models restrict the update of equivariant features during message passing to linear transformations or gated equivariant nonlinearities. The inability to implicitly characterize more complex nonlinear structures may reduce the predictive accuracy of the model. In this study, we introduce a frame-averaging-based approach to achieve equivariant dielectric tensor prediction. We propose GoeCTP, an O(3)-equivariant framework that predicts dielectric tensors without imposing any structural restrictions on the backbone network. We benchmark its performance against several state-of-the-art models and further employ it for large-scale virtual screening of thermodynamically stable materials from the Materials Project database. GoeCTP successfully identifies various promising candidates, such as Zr(InBr$_3$)$_2$ (band gap $E_g = 2.41$ eV, dielectric constant $\overline{\varepsilon} = 194.72$) and SeI$_2$ (anisotropy ratio $\alpha_r = 96.763$), demonstrating its accuracy and efficiency in accelerating the discovery of advanced inorganic dielectric materials.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.04755v1</guid></item><item><title>[cond-mat updates on arXiv.org] Lateral Graphene-Metallene Interfaces at the Nanoscale</title><link>https://arxiv.org/abs/2601.04838</link><description>arXiv:2601.04838v1 Announce Type: new 
@@ -15,7 +15,7 @@ Abstract: Large language models suffer from "hallucinations"-logical inconsisten
 Abstract: Machine learning (ML) strategies are opening the door to faster computer simulations, allowing us to simulate more realistic colloidal systems. Since the interactions in colloidal systems are often highly many-body, stemming from e.g. depletion and steric interactions, one of the challenges for these algorithms is capturing the many-body nature of these interactions. In this paper, we introduce a new ML-based strategy for fitting many-body interactions in colloidal systems where the many-body interaction is highly local. To this end, we develop Voronoi-based descriptors for capturing the local environment and fit the effective potential using a simple neural network. To test this algorithm, we consider a simple two-dimensional model for a colloid-polymer mixture, where the colloid-colloid interactions and colloid-polymer interactions are hard-disk like, while the polymers themselves interact as ideal gas particles. We find that a Voronoi-based description is sufficient to accurately capture the many-body nature of this system. Moreover, we find that the Pearson correlation function alone is insufficient to determine the predictive power of the network emphasizing the importance of additional metrics when assessing the quality of ML-based potentials.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2502.19044v2</guid></item><item><title>[cond-mat updates on arXiv.org] Characterizing the cage state of glassy systems and its sensitivity to frozen boundaries</title><link>https://arxiv.org/abs/2507.16339</link><description>arXiv:2507.16339v2 Announce Type: replace 
 Abstract: Understanding the role that structure plays in the dynamical arrest observed in glassy systems remains an open challenge. Over the last decade, machine learning (ML) strategies have emerged as an important tool for probing this structure-dynamics relationship, particularly for predicting heterogeneous glassy dynamics from local structure. A recent advancement is the introduction of the cage state, a structural quantity that captures the average positions of particles while rearrangements are forbidden. During the caging regime, linear models trained on the cage state have been shown to outperform more complex ML methods trained on initial configurations only. In this paper, we explore the properties associated with the cage state in more detail to better understand why it serves as such an effective predictor for the dynamics. Specifically, we examine how the cage state in a binary hard-sphere mixture is influenced by both packing fraction and boundary conditions. Our results reveal that, as the system approaches the glassy regime, the cage state becomes increasingly influenced by long-range structural effects. This influence is evident both in its predictive power for particle dynamics and in the internal structure of the cage state, suggesting that the CS might be associated with some form of an amorphous growing structural length scale.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2507.16339v2</guid></item><item><title>[cond-mat updates on arXiv.org] Li+/H+ exchange in solid-state oxide Li-ion conductors</title><link>https://arxiv.org/abs/2509.13477</link><description>arXiv:2509.13477v2 Announce Type: replace 
 Abstract: Understanding the moisture stability of oxide Li-ion conductors is important for their practical applications in solid-state batteries. Unlike sulfide or halide conductors, oxide conductors generally better resist degradation when in contact with water, but can still undergo topotactic \ch{Li+}/\ch{H+} exchange (LHX). Here, we combine density functional theory (DFT) calculations with a machine-learning interatomic potential model to investigate the thermodynamic driving force of the LHX reaction for two representative oxide Li-ion conductor families: garnets and NASICONs. Li-stuffed garnets exhibit a strong driving force for proton exchange due to their high Li chemical potential. In contrast, NASICONs demonstrate a higher resistance against proton exchange due to the lower Li chemical potential and the lower O-H bond covalency for polyanion-bonded oxygens. Our findings reveal a critical trade-off: Li stuffing enhances conductivity but increases moisture susceptibility. This study underscores the importance of designing Li-ion conductors that possess both high conductivity and high stability in practical environments.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2509.13477v2</guid></item><item><title>[cond-mat updates on arXiv.org] A universal machine learning model for the electronic density of states</title><link>https://arxiv.org/abs/2508.17418</link><description>arXiv:2508.17418v2 Announce Type: replace-cross 
-Abstract: In the last few years several ``universal'' interatomic potentials have appeared, using machine-learning approaches to predict energy and forces of atomic configurations with arbitrary composition and structure, with an accuracy often comparable with that of the electronic-structure calculations they are trained on. Here we demonstrate that these generally-applicable models can also be built to predict explicitly the electronic structure of materials and molecules. We focus on the electronic density of states (DOS), and develop PET-MAD-DOS, a rotationally unconstrained transformer model built on the Point Edge Transformer (PET) architecture, and trained on the Massive Atomistic Diversity (MAD) dataset. We demonstrate our model's predictive abilities on samples from diverse external datasets, showing also that the DOS can be further manipulated to obtain accurate band gap predictions. A fast evaluation of the DOS is especially useful in combination with molecular simulations probing matter in finite-temperature thermodynamic conditions. To assess the accuracy of PET-MAD-DOS in this context, we evaluate the ensemble-averaged DOS and the electronic heat capacity of three technologically relevant systems: lithium thiophosphate (LPS), gallium arsenide (GaAs), and a high entropy alloy (HEA). By comparing with bespoke models, trained exclusively on system-specific datasets, we show that our universal model achieves semi-quantitative agreement for all these tasks. Furthermore, we demonstrate that fine-tuning can be performed using a small fraction of the bespoke data, yielding models that are comparable to, and sometimes better than, fully-trained bespoke models.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2508.17418v2</guid></item><item><title>[RSC - Digital Discovery latest articles] MOFReasoner: Think Like a Scientist-A Reasoning Large Language Model via Knowledge Distillation</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00429B</link><description>&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2025, Accepted Manuscript&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00429B, Paper&lt;/div&gt;&lt;div&gt;&lt;img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /&gt; Open Access&lt;/div&gt;&lt;div&gt;&lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt; &lt;img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Xuefeng Bai, Zhiling  Zheng, Xin Zhang, Hao-Tian  Wang, Rui Yang, Jian-Rong Li&lt;br /&gt;Large Language Models (LLMs) have potential in transforming chemical research. Nevertheless, their general-purpose design constrains scientific understanding and reasoning within specialized fields like chemistry. In this study, we introduce MOFReasoner,...&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Fri, 09 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00429B</guid></item><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>[Recent Articles in Phys. Rev. Lett.] Enhancing Gravitational-Wave Detection: A Machine Learning Pipeline Combination Approach with Robust Uncertainty Quantification</title><link>http://link.aps.org/doi/10.1103/yfb3-fgf2</link><description>Author(s): Gregory Ashton, Ann-Kristin Malz, and Nicolo Colombo&lt;br /&gt;&lt;p&gt;Gravitational-wave data from advanced-era interferometric detectors consists of background Gaussian noise, frequent transient artifacts, and rare astrophysical signals. Multiple search algorithms exist to detect the signals from compact binary coalescences, but their varying performance complicates …&lt;/p&gt;&lt;br /&gt;[Phys. Rev. Lett. 136, 011402] Published Thu Jan 08, 2026</description><author>Recent Articles in Phys. Rev. Lett.</author><pubDate>Thu, 08 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/yfb3-fgf2</guid></item><item><title>[Recent Articles in Phys. Rev. B] Universal band center model for the HER activity of nonmetal sites in transition metal dichalcogenides</title><link>http://link.aps.org/doi/10.1103/zhg5-hhpl</link><description>Author(s): Ruixin Xu, Shiqian Cao, Tingting Bo, Yanyu Liu, and Wei Zhou&lt;br /&gt;&lt;p&gt;In this work, the hydrogenation performances of nonmetal sites in the transition metal dichalcogenides with the stoichiometry of $M{\mathit{X}}_{2}$ are systematically investigated using the first principles calculations. The trained machine learning model demonstrates that the ${p}_{\mathrm{z}}$ ba…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 113, 035305] Published Thu Jan 08, 2026</description><author>Recent Articles in Phys. Rev. B</author><pubDate>Thu, 08 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/zhg5-hhpl</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: In the last few years several ``universal'' interatomic potentials have appeared, using machine-learning approaches to predict energy and forces of atomic configurations with arbitrary composition and structure, with an accuracy often comparable with that of the electronic-structure calculations they are trained on. Here we demonstrate that these generally-applicable models can also be built to predict explicitly the electronic structure of materials and molecules. We focus on the electronic density of states (DOS), and develop PET-MAD-DOS, a rotationally unconstrained transformer model built on the Point Edge Transformer (PET) architecture, and trained on the Massive Atomistic Diversity (MAD) dataset. We demonstrate our model's predictive abilities on samples from diverse external datasets, showing also that the DOS can be further manipulated to obtain accurate band gap predictions. A fast evaluation of the DOS is especially useful in combination with molecular simulations probing matter in finite-temperature thermodynamic conditions. To assess the accuracy of PET-MAD-DOS in this context, we evaluate the ensemble-averaged DOS and the electronic heat capacity of three technologically relevant systems: lithium thiophosphate (LPS), gallium arsenide (GaAs), and a high entropy alloy (HEA). By comparing with bespoke models, trained exclusively on system-specific datasets, we show that our universal model achieves semi-quantitative agreement for all these tasks. Furthermore, we demonstrate that fine-tuning can be performed using a small fraction of the bespoke data, yielding models that are comparable to, and sometimes better than, fully-trained bespoke models.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 09 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2508.17418v2</guid></item><item><title>[RSC - Digital Discovery latest articles] MOFReasoner: Think Like a Scientist-A Reasoning Large Language Model via Knowledge Distillation</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00429B</link><description>&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2025, Accepted Manuscript&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00429B, Paper&lt;/div&gt;&lt;div&gt;&lt;img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /&gt; Open Access&lt;/div&gt;&lt;div&gt;&lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt; &lt;img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Xuefeng Bai, Zhiling  Zheng, Xin Zhang, Hao-Tian  Wang, Rui Yang, Jian-Rong Li&lt;br /&gt;Large Language Models (LLMs) have potential in transforming chemical research. Nevertheless, their general-purpose design constrains scientific understanding and reasoning within specialized fields like chemistry. In this study, we introduce MOFReasoner,...&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Digital Discovery latest articles</author><pubDate>Fri, 09 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00429B</guid></item><item><title>[npj Computational Materials] Scaling reliable interatomic potentials to complex nuclear alloys via pretrained atomic models</title><link>https://www.nature.com/articles/s41524-025-01950-6</link><description>&lt;p&gt;npj Computational Materials, Published online: 09 January 2026; &lt;a href="https://www.nature.com/articles/s41524-025-01950-6"&gt;doi:10.1038/s41524-025-01950-6&lt;/a&gt;&lt;/p&gt;Scaling reliable interatomic potentials to complex nuclear alloys via pretrained atomic models</description><author>npj Computational Materials</author><pubDate>Fri, 09 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41524-025-01950-6</guid></item><item><title>[ChemRxiv] Efficient Simulation of Optical Spectra via Machine Learning and Physical Decomposition of Environmental Effects</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-r7kfr?rft_dat=source%3Ddrss</link><description>Simulations of optical spectra can provide key insights to aid experimental interpretation of electronic excitation phenomena. For chromophores in the condensed phase, these spectra, which incorporate the coupling between electronic excitation and molecular and solvent nuclear motions, can be simulated using excitation energies obtained from molecular dynamics simulations of the chromophore and solvent. Here, we present a hybrid scheme that exploits machine learning and physically informed spectral densities to show that as few as 25 ground and excited state energetic gradient calculations can be used to construct models that accurately predict environment-influenced vibronic coupling in optical spectra. We demonstrate our approach for the green fluorescent protein chromophore in water and the cresyl violet chromophore in methanol. We show that our hybrid approach, employing a machine learning model for the high-frequency spectral density and an ab initio parameterized Debye spectral density for the low-frequency, results in systematic improvement of the optical absorption lineshape, leading to a simple machine learning scheme that can be used for simulation of spectral densities and optical spectra.</description><author>ChemRxiv</author><pubDate>Fri, 09 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-r7kfr?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] MolPic: Name/SMILES to Publication-Ready Molecular Figures</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-wjnk9?rft_dat=source%3Ddrss</link><description>Here, we present MolPic, an open-source Python-based software that can be used to generate high-resolution, publication-quality molecular figures directly from compound names or SMILES strings. MolPic supports single-molecule rendering, batch processing, and automated multi-panel 2D figure generation, which are suitable for manuscripts and presentations. MolPic generates a scalable vector graphics (SVG) image as the output. MolPic is fully compatible with Linux, macOS, and cloud-based environments such as Google Colab. The software is archived with a permanent DOI and is freely available to the scientific community.</description><author>ChemRxiv</author><pubDate>Fri, 09 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-wjnk9?rft_dat=source%3Ddrss</guid></item><item><title>[RSC - Chem. Sci. latest articles] Coupling abundant active sites and Ultra-short ion diffusion path: R-VO 2 /carbon nanotubes composite microspheres boosted high performance aqueous ammonium-ion batteries</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC08747C</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/D5SC08747C, Edge Article&lt;/div&gt;&lt;div&gt;&lt;img alt="Open Access" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/open_access_blue.png" /&gt; Open Access&lt;/div&gt;&lt;div&gt;&lt;a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt; &lt;img alt="Creative Commons Licence" border="none" src="http://sod-a.rsc-cdn.org/pubs.rsc-uat.org/content/NewImages/CCBY-NC.png" /&gt;&lt;/a&gt;&amp;nbsp; This article is licensed under a &lt;a href="http://creativecommons.org/licenses/by-nc/3.0/" rel="license" target="_blank" title="This link will open in a new browser window"&gt;Creative Commons Attribution-NonCommercial 3.0 Unported Licence.&lt;/a&gt;&lt;/div&gt;&lt;div&gt;Lin-bo Tang, Xian-Kai  Fan, Kaixiong Xiang, Wei Zhou, Weina Deng, Hai  Zhu, Liang Chen, Junchao Zheng, Han Chen&lt;br /&gt;Ammonium (NH4+) ions as charge carriers have exposed tremendous potentials in aqueous batteries because of the rich resources, ultrafast reaction kinetics, and negligible dendrite risks. However, the choices for cathode...&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>Fri, 09 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC08747C</guid></item><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>[Wiley: Advanced Science: Table of Contents] OBUSight: Clinically Aligned Generative AI for Ophthalmic Ultrasound Interpretation and Diagnosis</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202515864?af=R</link><description>Advanced Science, EarlyView.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 08 Jan 2026 13:20:36 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202515864</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Synergistic Effects of Solid Electrolyte Mild Sintering and Lithium Surface Passivation for Enhanced Lithium Metal Cycling in All‐Solid‐State Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202521791?af=R</link><description>Advanced Science, EarlyView.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Thu, 08 Jan 2026 13:11:10 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202521791</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. 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Rev. Lett.] Enhancing Gravitational-Wave Detection: A Machine Learning Pipeline Combination Approach with Robust Uncertainty Quantification</title><link>http://link.aps.org/doi/10.1103/yfb3-fgf2</link><description>Author(s): Gregory Ashton, Ann-Kristin Malz, and Nicolo Colombo&lt;br /&gt;&lt;p&gt;Gravitational-wave data from advanced-era interferometric detectors consists of background Gaussian noise, frequent transient artifacts, and rare astrophysical signals. Multiple search algorithms exist to detect the signals from compact binary coalescences, but their varying performance complicates …&lt;/p&gt;&lt;br /&gt;[Phys. Rev. Lett. 136, 011402] Published Thu Jan 08, 2026</description><author>Recent Articles in Phys. Rev. Lett.</author><pubDate>Thu, 08 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/yfb3-fgf2</guid></item><item><title>[Recent Articles in Phys. Rev. B] Universal band center model for the HER activity of nonmetal sites in transition metal dichalcogenides</title><link>http://link.aps.org/doi/10.1103/zhg5-hhpl</link><description>Author(s): Ruixin Xu, Shiqian Cao, Tingting Bo, Yanyu Liu, and Wei Zhou&lt;br /&gt;&lt;p&gt;In this work, the hydrogenation performances of nonmetal sites in the transition metal dichalcogenides with the stoichiometry of $M{\mathit{X}}_{2}$ are systematically investigated using the first principles calculations. The trained machine learning model demonstrates that the ${p}_{\mathrm{z}}$ ba…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 113, 035305] Published Thu Jan 08, 2026</description><author>Recent Articles in Phys. Rev. B</author><pubDate>Thu, 08 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/zhg5-hhpl</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