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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</title><link>https://github.com/your_username/your_repo</link><description>Aggregated research papers</description><language>en-US</language><lastBuildDate>Mon, 12 Jan 2026 18:32:41 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)] [ASAP] Multiscale Modeling of Solid Electrolyte Interphase Formation on Oxygen-Functionalized Graphite Anodes for Lithium-Ion Batteries</title><link>http://dx.doi.org/10.1021/acs.jctc.5c01561</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jctc.5c01561/asset/images/medium/ct5c01561_0011.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.5c01561&lt;/div&gt;</description><author>Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)</author><pubDate>Mon, 12 Jan 2026 20:10:43 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jctc.5c01561</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Machine learning guided prediction of solute segregation at coherent and semi-coherent metal/oxide interfaces</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625008237?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: October 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 264&lt;/p&gt;&lt;p&gt;Author(s): Yizhou Lu, Blas Pedro Uberuaga, Samrat Choudhury&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Mon, 12 Jan 2026 12:46:02 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625008237</guid></item><item><title>[cond-mat updates on arXiv.org] The effect of normal stress on stacking fault energy in face-centered cubic metals</title><link>https://arxiv.org/abs/2601.05453</link><description>arXiv:2601.05453v1 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>Tue, 13 Jan 2026 01:39:36 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)] [ASAP] Multiscale Modeling of Solid Electrolyte Interphase Formation on Oxygen-Functionalized Graphite Anodes for Lithium-Ion Batteries</title><link>http://dx.doi.org/10.1021/acs.jctc.5c01561</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jctc.5c01561/asset/images/medium/ct5c01561_0011.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.5c01561&lt;/div&gt;</description><author>Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)</author><pubDate>Mon, 12 Jan 2026 20:10:43 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jctc.5c01561</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Machine learning guided prediction of solute segregation at coherent and semi-coherent metal/oxide interfaces</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625008237?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: October 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 264&lt;/p&gt;&lt;p&gt;Author(s): Yizhou Lu, Blas Pedro Uberuaga, Samrat Choudhury&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Mon, 12 Jan 2026 12:46:02 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625008237</guid></item><item><title>[cond-mat updates on arXiv.org] The effect of normal stress on stacking fault energy in face-centered cubic metals</title><link>https://arxiv.org/abs/2601.05453</link><description>arXiv:2601.05453v1 Announce Type: new 
 Abstract: Plastic deformation and fracture of FCC metals involve the formation of stable or unstable stacking faults (SFs) on (111) plane. Examples include dislocation cross-slip and dislocation nucleation at interfaces and near crack tips. The stress component normal to (111) plane can strongly affect the SF energy when the stress magnitude reaches several to tens of GPa. We conduct a series of DFT calculations of SF energies in six FCC metals: Al, Ni, Cu, Ag, Au, and Pt. The results show that normal compression significantly increases the stable and unstable SF energies in all six metals, while normal tension decreases them. The SF formation is accompanied by inelastic expansion in the normal direction. The DFT calculations are compared with predictions of several representative classical and machine-learning interatomic potentials. Many potentials fail to capture the correct stress effect on the SF energy, often predicting trends opposite to the DFT calculations. Possible ways to improve the ability of potentials to represent the stress effect on SF energy are discussed.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 12 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.05453v1</guid></item><item><title>[cond-mat updates on arXiv.org] Autonomous Discovery of the Ising Model's Critical Parameters with Reinforcement Learning</title><link>https://arxiv.org/abs/2601.05577</link><description>arXiv:2601.05577v1 Announce Type: new 
 Abstract: Traditional methods for determining critical parameters are often influenced by human factors. This research introduces a physics-inspired adaptive reinforcement learning framework that enables agents to autonomously interact with physical environments, simultaneously identifying both the critical temperature and various types of critical exponents in the Ising model with precision. Interestingly, our algorithm exhibits search behavior reminiscent of phase transitions, efficiently converging to target parameters regardless of initial conditions. Experimental results demonstrate that this method significantly outperforms traditional approaches, particularly in environments with strong perturbations. This study not only incorporates physical concepts into machine learning to enhance algorithm interpretability but also establishes a new paradigm for scientific exploration, transitioning from manual analysis to autonomous AI discovery.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 12 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.05577v1</guid></item><item><title>[cond-mat updates on arXiv.org] Phase Frustration Induced Intrinsic Bose Glass in the Kitaev-Bose-Hubbard Model</title><link>https://arxiv.org/abs/2601.05781</link><description>arXiv:2601.05781v1 Announce Type: new 
 Abstract: We report an intrinsic "Bubble Phase" in the two-dimensional Kitaev-Bose-Hubbard model, driven purely by phase frustration between complex hopping and anisotropic pairing. By combining Inhomogeneous Gutzwiller Mean-Field Theory with a Bogoliubov-de Gennes stability analysis augmented by a novel Energy Penalty Method, we demonstrate that this phase spontaneously fragments into coherent islands, exhibiting the hallmark Bose glass signature of finite compressibility without global superfluidity. Notably, we propose a unified framework linking disorder-driven localization to deterministic phase frustration, identifying the Bubble Phase as a pristine, disorder-free archetype of the Bose glass. Our results provide a theoretical blueprint for realizing glassy dynamics in clean quantum simulators.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 12 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.05781v1</guid></item><item><title>[cond-mat updates on arXiv.org] A Critical Examination of Active Learning Workflows in Materials Science</title><link>https://arxiv.org/abs/2601.05946</link><description>arXiv:2601.05946v1 Announce Type: new