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<?xml version="1.0" encoding="UTF-8"?> <?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>Thu, 15 Jan 2026 18:36:06 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] Engineering dense superionic Li₁₊&lt;em&gt;ₓ&lt;/em&gt;Al&lt;em&gt;ₓ&lt;/em&gt;Ti₂₋&lt;em&gt;ₓ&lt;/em&gt;(PO₄)₃ solid electrolytes for safer solid-state Li-ion batteries: Impact of sintering temperature and Al&lt;sup&gt;3+&lt;/sup&gt; doping</title><link>https://www.sciencedirect.com/science/article/pii/S0167273826000044?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): Sumit Khatua, K. Ramakrushna Achary, K. Sasikumar, Lakshmi Hrushita Korlapati, L.N. Patro&lt;/p&gt;</description><author>ScienceDirect Publication: Solid State Ionics</author><pubDate>Thu, 15 Jan 2026 18:35:51 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0167273826000044</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Magnetic–Current Coupling Matched with Pore Geometry Boosts Ion Transport in LiFePO&lt;sub&gt;4&lt;/sub&gt; Cathodes</title><link>https://www.sciencedirect.com/science/article/pii/S2211285526000169?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 14 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy&lt;/p&gt;&lt;p&gt;Author(s): Yue Li, Jiabao Sun, Jianxin Deng, Rui Zhang, Ning Wang, Xingai Wang, Lei Wang, Qiyu Wang, Haichang Zhang, Fei Ding&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Thu, 15 Jan 2026 18:35:42 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285526000169</guid></item><item><title>[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Predicting Activation Energy of Hydrocarbon Dehydrogenation on Au(111) via Machine Learning</title><link>http://dx.doi.org/10.1021/acs.jpclett.5c03941</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpclett.5c03941/asset/images/medium/jz5c03941_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.5c03941&lt;/div&gt;</description><author>The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)</author><pubDate>Thu, 15 Jan 2026 12:50:31 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpclett.5c03941</guid></item><item><title>[cond-mat updates on arXiv.org] Emergent chiral Higgs mode in $\pi$-flux frustrated lattices</title><link>https://arxiv.org/abs/2601.08925</link><description>arXiv:2601.08925v1 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, 16 Jan 2026 01:43:57 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] Engineering dense superionic Li₁₊&lt;em&gt;ₓ&lt;/em&gt;Al&lt;em&gt;ₓ&lt;/em&gt;Ti₂₋&lt;em&gt;ₓ&lt;/em&gt;(PO₄)₃ solid electrolytes for safer solid-state Li-ion batteries: Impact of sintering temperature and Al&lt;sup&gt;3+&lt;/sup&gt; doping</title><link>https://www.sciencedirect.com/science/article/pii/S0167273826000044?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): Sumit Khatua, K. Ramakrushna Achary, K. Sasikumar, Lakshmi Hrushita Korlapati, L.N. Patro&lt;/p&gt;</description><author>ScienceDirect Publication: Solid State Ionics</author><pubDate>Thu, 15 Jan 2026 18:35:51 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0167273826000044</guid></item><item><title>[ScienceDirect Publication: Nano Energy] Magnetic–Current Coupling Matched with Pore Geometry Boosts Ion Transport in LiFePO&lt;sub&gt;4&lt;/sub&gt; Cathodes</title><link>https://www.sciencedirect.com/science/article/pii/S2211285526000169?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 14 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Nano Energy&lt;/p&gt;&lt;p&gt;Author(s): Yue Li, Jiabao Sun, Jianxin Deng, Rui Zhang, Ning Wang, Xingai Wang, Lei Wang, Qiyu Wang, Haichang Zhang, Fei Ding&lt;/p&gt;</description><author>ScienceDirect Publication: Nano Energy</author><pubDate>Thu, 15 Jan 2026 18:35:42 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2211285526000169</guid></item><item><title>[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Predicting Activation Energy of Hydrocarbon Dehydrogenation on Au(111) via Machine Learning</title><link>http://dx.doi.org/10.1021/acs.jpclett.5c03941</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/acs.jpclett.5c03941/asset/images/medium/jz5c03941_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.5c03941&lt;/div&gt;</description><author>The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)</author><pubDate>Thu, 15 Jan 2026 12:50:31 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/acs.jpclett.5c03941</guid></item><item><title>[cond-mat updates on arXiv.org] Emergent chiral Higgs mode in $\pi$-flux frustrated lattices</title><link>https://arxiv.org/abs/2601.08925</link><description>arXiv:2601.08925v1 Announce Type: new
Abstract: Neutral-atom quantum simulators provide a powerful platform for realizing strongly correlated phases, enabling access to dynamical signatures of quasiparticles and symmetry breaking processes. Motivated by recent observations of quantum phases in flux-frustrated ladders with non-vanishing ground state currents, we investigate interacting bosons on the dimerized BBH lattice in two dimensions-originally introduced in the context of higher-order topology. After mapping out the phase diagram, which includes vortex superfluid (V-SF), vortex Mott insulator (V-MI), and featureless Mott insulator (MI) phases, we focus on the integer filling case. There, the MI/V-SF transition simultaneously breaks the $\mathbb Z_2^{T}$ and U(1) symmetries, where $\mathbb Z_2^{T}$ corresponds to time-reversal symmetry (TRS). Using a slave-boson description, we resolve the excitation spectrum across the transition and uncover a chiral Higgs mode whose mass softens at criticality, providing a dynamical hallmark of emergent chirality that we numerically probe via quench dynamics. Our results establish an experimentally realistic setting for probing unconventional TRS-broken phases and quasiparticles with intrinsic chirality in strongly interacting quantum matter.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 15 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.08925v1</guid></item><item><title>[cond-mat updates on arXiv.org] Machine Learning-Driven Creep Law Discovery Across Alloy Compositional Space</title><link>https://arxiv.org/abs/2601.08970</link><description>arXiv:2601.08970v1 Announce Type: new Abstract: Neutral-atom quantum simulators provide a powerful platform for realizing strongly correlated phases, enabling access to dynamical signatures of quasiparticles and symmetry breaking processes. Motivated by recent observations of quantum phases in flux-frustrated ladders with non-vanishing ground state currents, we investigate interacting bosons on the dimerized BBH lattice in two dimensions-originally introduced in the context of higher-order topology. After mapping out the phase diagram, which includes vortex superfluid (V-SF), vortex Mott insulator (V-MI), and featureless Mott insulator (MI) phases, we focus on the integer filling case. There, the MI/V-SF transition simultaneously breaks the $\mathbb Z_2^{T}$ and U(1) symmetries, where $\mathbb Z_2^{T}$ corresponds to time-reversal symmetry (TRS). Using a slave-boson description, we resolve the excitation spectrum across the transition and uncover a chiral Higgs mode whose mass softens at criticality, providing a dynamical hallmark of emergent chirality that we numerically probe via quench dynamics. Our results establish an experimentally realistic setting for probing unconventional TRS-broken phases and quasiparticles with intrinsic chirality in strongly interacting quantum matter.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 15 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.08925v1</guid></item><item><title>[cond-mat updates on arXiv.org] Machine Learning-Driven Creep Law Discovery Across Alloy Compositional Space</title><link>https://arxiv.org/abs/2601.08970</link><description>arXiv:2601.08970v1 Announce Type: new
Abstract: Hihg-temperature creep characterization of structural alloys traditionally relies on serial uniaxial tests, which are highly inefficient for exploring the large search space of alloy compositions and for material discovery. Here, we introduce a machine-learning-assisted, high-throughput framework for creep law identification based on a dimple array bulge instrument (DABI) configuration, which enables parallel creep testing of 25 dimples, each fabricated from a different alloy, in a single experiment. Full-field surface displacements of dimples undergoing time-dependent creep-induced bulging under inert gas pressure are measured by 3D digital image correlation. We train a recurrent neural network (RNN) as a surrogate model, mapping creep parameters and loading conditions to the time-dependent deformation response of DABI. Coupling this surrogate with a particle swarm optimization scheme enables rapid and global inverse identification with sparsity regularization of creep parameters from experiment displacement-time histories. In addition, we propose a phenomenological creep law with a time-dependent stress exponent that captures the sigmoidal primary creep observed in wrought INCONEL 625 and extracts its temperature dependence from DABI test at multiple temperatures. Furthermore, we employ a general creep law combining several conventional forms together with regularized inversion to identify the creep laws for 47 additional Fe-, Ni-, and Co-rich alloys and to automatically select the dominant functional form for each alloy. This workflow combined with DABI experiment provides a quantitative, high-throughput creep characterization platform that is compatible with data mining, composition-property modeling, and nonlinear structural optimization with creep behavior across a large alloy design space.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 15 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.08970v1</guid></item><item><title>[cond-mat updates on arXiv.org] Agentic AI and Machine Learning for Accelerated Materials Discovery and Applications</title><link>https://arxiv.org/abs/2601.09027</link><description>arXiv:2601.09027v1 Announce Type: new Abstract: Hihg-temperature creep characterization of structural alloys traditionally relies on serial uniaxial tests, which are highly inefficient for exploring the large search space of alloy compositions and for material discovery. Here, we introduce a machine-learning-assisted, high-throughput framework for creep law identification based on a dimple array bulge instrument (DABI) configuration, which enables parallel creep testing of 25 dimples, each fabricated from a different alloy, in a single experiment. Full-field surface displacements of dimples undergoing time-dependent creep-induced bulging under inert gas pressure are measured by 3D digital image correlation. We train a recurrent neural network (RNN) as a surrogate model, mapping creep parameters and loading conditions to the time-dependent deformation response of DABI. Coupling this surrogate with a particle swarm optimization scheme enables rapid and global inverse identification with sparsity regularization of creep parameters from experiment displacement-time histories. In addition, we propose a phenomenological creep law with a time-dependent stress exponent that captures the sigmoidal primary creep observed in wrought INCONEL 625 and extracts its temperature dependence from DABI test at multiple temperatures. Furthermore, we employ a general creep law combining several conventional forms together with regularized inversion to identify the creep laws for 47 additional Fe-, Ni-, and Co-rich alloys and to automatically select the dominant functional form for each alloy. This workflow combined with DABI experiment provides a quantitative, high-throughput creep characterization platform that is compatible with data mining, composition-property modeling, and nonlinear structural optimization with creep behavior across a large alloy design space.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 15 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.08970v1</guid></item><item><title>[cond-mat updates on arXiv.org] Agentic AI and Machine Learning for Accelerated Materials Discovery and Applications</title><link>https://arxiv.org/abs/2601.09027</link><description>arXiv:2601.09027v1 Announce Type: new
Abstract: Artificial Intelligence (AI), especially AI agents, is increasingly being applied to chemistry, healthcare, and manufacturing to enhance productivity. In this review, we discuss the progress of AI and agentic AI in areas related to, and beyond polymer materials and discovery chemistry. More specifically, the focus is on the need for efficient discovery, core concepts, and large language models. Consequently, applications are showcased in scenarios such as (1) flow chemistry, (2) biosensors, and (3) batteries.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 15 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.09027v1</guid></item><item><title>[cond-mat updates on arXiv.org] Data-Driven Exploration and Insights into Temperature-Dependent Phonons in Inorganic Materials</title><link>https://arxiv.org/abs/2601.09123</link><description>arXiv:2601.09123v1 Announce Type: new Abstract: Artificial Intelligence (AI), especially AI agents, is increasingly being applied to chemistry, healthcare, and manufacturing to enhance productivity. In this review, we discuss the progress of AI and agentic AI in areas related to, and beyond polymer materials and discovery chemistry. More specifically, the focus is on the need for efficient discovery, core concepts, and large language models. Consequently, applications are showcased in scenarios such as (1) flow chemistry, (2) biosensors, and (3) batteries.</description><author>cond-mat updates on arXiv.org</author><pubDate>Thu, 15 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.09027v1</guid></item><item><title>[cond-mat updates on arXiv.org] Data-Driven Exploration and Insights into Temperature-Dependent Phonons in Inorganic Materials</title><link>https://arxiv.org/abs/2601.09123</link><description>arXiv:2601.09123v1 Announce Type: new