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-<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/"><channel><title>My Customized Papers (Auto-Filtered)</title><link>https://github.com/your_username/your_repo</link><description>Aggregated research papers based on keywords</description><language>en-US</language><lastBuildDate>Sat, 27 Dec 2025 01:29:15 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[ScienceDirect Publication: Computational Materials Science] An enhanced machine learning and computational screening framework for synthesizable single-phase high-entropy spinel oxides</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625008110?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 263&lt;/p&gt;&lt;p&gt;Author(s): Mahalaxmi Chandramohan, Hridhya Vinod, Meenal Deo&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Fri, 26 Dec 2025 18:29:22 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625008110</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Anomalous temperature evolution of lattice anharmonicity and thermal transport in orthorhombic SnSe</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325003608?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 26 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics&lt;/p&gt;&lt;p&gt;Author(s): Tianxiang Jiang, Wujie Qiu, Haijuan Zhang, Jifen Wang, Kunpeng Zhao, Huaqing Xie&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Fri, 26 Dec 2025 18:29:17 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325003608</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Machine learning aided bandgap and defect engineering of mixed halide perovskites for photovoltaic applications</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325003591?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 26 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics&lt;/p&gt;&lt;p&gt;Author(s): Ayush Kumar Pandey, Vivek Pandey, Abhishek Tewari&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Fri, 26 Dec 2025 18:29:17 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325003591</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Mendelian Randomization and Double Machine Learning Modeling Reveal Brain Imaging‐Derived Phenotypes as Functional Contributors to 18 Autoimmune Inflammatory Diseases</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202515675?af=R</link><description>Advanced Science, EarlyView.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Fri, 26 Dec 2025 06:23:13 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202515675</guid></item><item><title>[Wiley: Carbon Energy: Table of Contents] Data‐Driven Design of Scalable Perovskite Film Fabrication via Machine Learning–Guided Processing</title><link>https://onlinelibrary.wiley.com/doi/10.1002/cey2.70164?af=R</link><description>Carbon Energy, EarlyView.</description><author>Wiley: Carbon Energy: Table of Contents</author><pubDate>Fri, 26 Dec 2025 06:22:51 GMT</pubDate><guid isPermaLink="true">10.1002/cey2.70164</guid></item><item><title>[Nature Communications] Inferring fine-grained migration patterns across the United States</title><link>https://www.nature.com/articles/s41467-025-68019-2</link><description>&lt;p&gt;Nature Communications, Published online: 26 December 2025; &lt;a href="https://www.nature.com/articles/s41467-025-68019-2"&gt;doi:10.1038/s41467-025-68019-2&lt;/a&gt;&lt;/p&gt;This study releases a very high-resolution migration dataset that reveals trends that shape daily life: rising moves into high-income neighborhoods, racial gaps in upward mobility, and wildfire-driven moves.</description><author>Nature Communications</author><pubDate>Fri, 26 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41467-025-68019-2</guid></item><item><title>[Communications Materials] Machine learning interatomic potential reveals hydrogen embrittlement origins at general grain boundaries in α-iron</title><link>https://www.nature.com/articles/s43246-025-01042-4</link><description>&lt;p&gt;Communications Materials, Published online: 26 December 2025; &lt;a href="https://www.nature.com/articles/s43246-025-01042-4"&gt;doi:10.1038/s43246-025-01042-4&lt;/a&gt;&lt;/p&gt;Hydrogen embrittlement is an issue that alloys used in the energy sector must overcome. Here, a machine learning interatomic potential for iron-hydrogen is reported, with large-scale molecular dynamics simulations revealing that hydrogen can suppress &gt;111 &lt; /2 dislocation emission at grain boundaries.</description><author>Communications Materials</author><pubDate>Fri, 26 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s43246-025-01042-4</guid></item><item><title>[iScience] Entropy-based byte patching transformer for self-supervised pretraining of SMILES data</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02816-0?rss=yes</link><description>Efficient molecular representations are critical for improving the performance and generalization of large language models in chemical learning. Transformer-based architectures have advanced molecular representation learning, yet capturing localized and hierarchical chemical structures remains challenging. We introduce the SMILES Byte-Patch Transformer (SMiBPT), an adaptive model that dynamically segments SMILES and DeepSMILES strings into chemically meaningful substructures through entropy-based byte patching.</description><author>iScience</author><pubDate>Fri, 26 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02816-0?rss=yes</guid></item><item><title>[ScienceDirect Publication: Solid State Ionics] Anion engineering enhances the electrochemical performance of Li&lt;sub&gt;7&lt;/sub&gt;P&lt;sub&gt;3&lt;/sub&gt;S&lt;sub&gt;11&lt;/sub&gt; solid electrolyte</title><link>https://www.sciencedirect.com/science/article/pii/S0167273825003236?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 15 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Solid State Ionics, Volume 435&lt;/p&gt;&lt;p&gt;Author(s): D. Narsimulu, Ramkumar Balasubramaniam, Kwang-Sun Ryu&lt;/p&gt;</description><author>ScienceDirect Publication: Solid State Ionics</author><pubDate>Thu, 25 Dec 2025 18:28:52 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0167273825003236</guid></item><item><title>[ScienceDirect Publication: Science Bulletin] Machine learning for spectral precision: a new horizon in radiative cooling material design</title><link>https://www.sciencedirect.com/science/article/pii/S2095927325011235?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 30 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Science Bulletin, Volume 70, Issue 24&lt;/p&gt;&lt;p&gt;Author(s): Xinpeng Hu, Mingxiang Liu, Xuemei Fu, Guangming Tao, Xiang Lu, Jinping Qu&lt;/p&gt;</description><author>ScienceDirect Publication: Science Bulletin</author><pubDate>Thu, 25 Dec 2025 18:28:50 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2095927325011235</guid></item><item><title>[cond-mat updates on arXiv.org] Towards predictive atomistic simulations of SiC crystal growth</title><link>https://arxiv.org/abs/2512.20804</link><description>arXiv:2512.20804v1 Announce Type: new 
+<rss version="2.0" xmlns:dc="http://purl.org/dc/elements/1.1/"><channel><title>My Customized Papers (Auto-Filtered)</title><link>https://github.com/your_username/your_repo</link><description>Aggregated research papers based on keywords</description><language>en-US</language><lastBuildDate>Sat, 27 Dec 2025 06:30:40 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[ScienceDirect Publication: Computational Materials Science] An enhanced machine learning and computational screening framework for synthesizable single-phase high-entropy spinel oxides</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625008110?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Computational Materials Science, Volume 263&lt;/p&gt;&lt;p&gt;Author(s): Mahalaxmi Chandramohan, Hridhya Vinod, Meenal Deo&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Fri, 26 Dec 2025 18:29:22 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625008110</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Anomalous temperature evolution of lattice anharmonicity and thermal transport in orthorhombic SnSe</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325003608?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 26 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics&lt;/p&gt;&lt;p&gt;Author(s): Tianxiang Jiang, Wujie Qiu, Haijuan Zhang, Jifen Wang, Kunpeng Zhao, Huaqing Xie&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Fri, 26 Dec 2025 18:29:17 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325003608</guid></item><item><title>[ScienceDirect Publication: Materials Today Physics] Machine learning aided bandgap and defect engineering of mixed halide perovskites for photovoltaic applications</title><link>https://www.sciencedirect.com/science/article/pii/S2542529325003591?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 26 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics&lt;/p&gt;&lt;p&gt;Author(s): Ayush Kumar Pandey, Vivek Pandey, Abhishek Tewari&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Fri, 26 Dec 2025 18:29:17 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529325003591</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Mendelian Randomization and Double Machine Learning Modeling Reveal Brain Imaging‐Derived Phenotypes as Functional Contributors to 18 Autoimmune Inflammatory Diseases</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202515675?af=R</link><description>Advanced Science, EarlyView.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Fri, 26 Dec 2025 06:23:13 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202515675</guid></item><item><title>[Wiley: Carbon Energy: Table of Contents] Data‐Driven Design of Scalable Perovskite Film Fabrication via Machine Learning–Guided Processing</title><link>https://onlinelibrary.wiley.com/doi/10.1002/cey2.70164?af=R</link><description>Carbon Energy, EarlyView.</description><author>Wiley: Carbon Energy: Table of Contents</author><pubDate>Fri, 26 Dec 2025 06:22:51 GMT</pubDate><guid isPermaLink="true">10.1002/cey2.70164</guid></item><item><title>[Nature Communications] Inferring fine-grained migration patterns across the United States</title><link>https://www.nature.com/articles/s41467-025-68019-2</link><description>&lt;p&gt;Nature Communications, Published online: 26 December 2025; &lt;a href="https://www.nature.com/articles/s41467-025-68019-2"&gt;doi:10.1038/s41467-025-68019-2&lt;/a&gt;&lt;/p&gt;This study releases a very high-resolution migration dataset that reveals trends that shape daily life: rising moves into high-income neighborhoods, racial gaps in upward mobility, and wildfire-driven moves.</description><author>Nature Communications</author><pubDate>Fri, 26 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41467-025-68019-2</guid></item><item><title>[Communications Materials] Machine learning interatomic potential reveals hydrogen embrittlement origins at general grain boundaries in α-iron</title><link>https://www.nature.com/articles/s43246-025-01042-4</link><description>&lt;p&gt;Communications Materials, Published online: 26 December 2025; &lt;a href="https://www.nature.com/articles/s43246-025-01042-4"&gt;doi:10.1038/s43246-025-01042-4&lt;/a&gt;&lt;/p&gt;Hydrogen embrittlement is an issue that alloys used in the energy sector must overcome. Here, a machine learning interatomic potential for iron-hydrogen is reported, with large-scale molecular dynamics simulations revealing that hydrogen can suppress &gt;111 &lt; /2 dislocation emission at grain boundaries.</description><author>Communications Materials</author><pubDate>Fri, 26 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s43246-025-01042-4</guid></item><item><title>[iScience] Entropy-based byte patching transformer for self-supervised pretraining of SMILES data</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02816-0?rss=yes</link><description>Efficient molecular representations are critical for improving the performance and generalization of large language models in chemical learning. Transformer-based architectures have advanced molecular representation learning, yet capturing localized and hierarchical chemical structures remains challenging. We introduce the SMILES Byte-Patch Transformer (SMiBPT), an adaptive model that dynamically segments SMILES and DeepSMILES strings into chemically meaningful substructures through entropy-based byte patching.</description><author>iScience</author><pubDate>Fri, 26 Dec 2025 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02816-0?rss=yes</guid></item><item><title>[ScienceDirect Publication: Solid State Ionics] Anion engineering enhances the electrochemical performance of Li&lt;sub&gt;7&lt;/sub&gt;P&lt;sub&gt;3&lt;/sub&gt;S&lt;sub&gt;11&lt;/sub&gt; solid electrolyte</title><link>https://www.sciencedirect.com/science/article/pii/S0167273825003236?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 15 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Solid State Ionics, Volume 435&lt;/p&gt;&lt;p&gt;Author(s): D. Narsimulu, Ramkumar Balasubramaniam, Kwang-Sun Ryu&lt;/p&gt;</description><author>ScienceDirect Publication: Solid State Ionics</author><pubDate>Thu, 25 Dec 2025 18:28:52 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0167273825003236</guid></item><item><title>[ScienceDirect Publication: Science Bulletin] Machine learning for spectral precision: a new horizon in radiative cooling material design</title><link>https://www.sciencedirect.com/science/article/pii/S2095927325011235?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 30 December 2025&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Science Bulletin, Volume 70, Issue 24&lt;/p&gt;&lt;p&gt;Author(s): Xinpeng Hu, Mingxiang Liu, Xuemei Fu, Guangming Tao, Xiang Lu, Jinping Qu&lt;/p&gt;</description><author>ScienceDirect Publication: Science Bulletin</author><pubDate>Thu, 25 Dec 2025 18:28:50 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2095927325011235</guid></item><item><title>[cond-mat updates on arXiv.org] Towards predictive atomistic simulations of SiC crystal growth</title><link>https://arxiv.org/abs/2512.20804</link><description>arXiv:2512.20804v1 Announce Type: new 
 Abstract: Simulations of SiC crystal growth using molecular dynamics (MD) have become popular in recent years. They, however, simulate very fast deposition rates, to reduce computational costs. Therefore, they are more akin to surface sputtering, leading to abnormal growth effects, including thick amorphous layers and large defect densities. A recently developed method, called the minimum energy atomic deposition (MEAD), tries to overcome this problem by depositing the atoms directly at the minimum energy positions, increasing the time scale.
   We apply the MEAD method to simulate SiC crystal growth on stepped C-terminated 4H substrates with 4{\deg} and 8{\deg} off-cut angle. We explore relevant calculations settings, such as amount of equilibration steps between depositions and influence of simulation cell sizes and bench mark different interatomic potentials. The carefully calibrated methodology is able to replicate the stable step-flow growth, which was so far not possible using conventional MD simulations. Furthermore, the simulated crystals are evaluated in terms of their dislocations, surface roughness and atom mobility. Our methodology paves the way for future high fidelity investigations of surface phenomena in crystal growth.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 25 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.20804v1</guid></item><item><title>[cond-mat updates on arXiv.org] Emergence of Friedel-like oscillations from Lorenz dynamics in walking droplets</title><link>https://arxiv.org/abs/2512.21049</link><description>arXiv:2512.21049v1 Announce Type: new 
 Abstract: Friedel oscillations are spatially decaying density modulations near localized defects and are a hallmark of quantum systems. Walking droplets provide a macroscopic platform for hydrodynamic quantum analogs, and Friedel-like oscillations were recently observed in droplet-defect scattering experiments through wave-mediated speed modulation [P.~J.~S\'aenz \textit{et al.}, \textit{Sci.\ Adv.} \textbf{6}, eay9234 (2020)]. Here we show that Friedel-like statistics can also arise from a purely local, dynamical mechanism, which we elucidate using a minimal Lorenz-like model of a walking droplet. In this model, a localized defect perturbs the particle's internal dynamical state, generating underdamped velocity oscillations that give rise to oscillatory ensemble position statistics. This attractor-driven, local mechanism opens new avenues for hydrodynamic quantum analogs based on active particles with internal degrees of freedom.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 25 Dec 2025 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.21049v1</guid></item><item><title>[cond-mat updates on arXiv.org] From cluster to nanocrystal: the continuous evolution and critical size of copper clusters revealed by machine learning</title><link>https://arxiv.org/abs/2512.21067</link><description>arXiv:2512.21067v1 Announce Type: new