diff --git a/filtered_feed.xml b/filtered_feed.xml
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@@ -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, 16 Jan 2026 12:43:37 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[ScienceDirect Publication: Materials Today Physics] Accelerated discovery of MM’XT&lt;math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e159" altimg="si8.svg" class="math"&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt; MXenes for catalysis, electronics, and energy storage using supervised machine learning</title><link>https://www.sciencedirect.com/science/article/pii/S2542529326000131?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 15 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics&lt;/p&gt;&lt;p&gt;Author(s): Umair Haider, Gul Rahman, Imran Shakir, M.S. Al-Buriahi, Norah Alomayrah, Imen Kebaili&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Fri, 16 Jan 2026 12:43:18 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529326000131</guid></item><item><title>[Wiley: Small: Table of Contents] Machine Learning‐Accelerated Specific Surface Prediction Strategy in Janus‐Based Z‐Scheme Heterostructures for Efficient Photocatalytic Water Splitting</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202509069?af=R</link><description>Small, Volume 22, Issue 4, 16 January 2026.</description><author>Wiley: Small: Table of Contents</author><pubDate>Fri, 16 Jan 2026 08:21:14 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202509069</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Machine Learning‐Guided Design of L10‐PtCo Intermetallic Catalysts: Zn‐Mediated Atomic Ordering</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202505211?af=R</link><description>Advanced Energy Materials, EarlyView.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Fri, 16 Jan 2026 05:15:00 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202505211</guid></item><item><title>[cond-mat updates on arXiv.org] Performance of AI agents based on reasoning language models on ALD process optimization tasks</title><link>https://arxiv.org/abs/2601.09980</link><description>arXiv:2601.09980v1 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 18:32:31 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[ScienceDirect Publication: Materials Today Physics] Accelerated discovery of MM’XT&lt;math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e159" altimg="si8.svg" class="math"&gt;&lt;msub&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt; MXenes for catalysis, electronics, and energy storage using supervised machine learning</title><link>https://www.sciencedirect.com/science/article/pii/S2542529326000131?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 15 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Materials Today Physics&lt;/p&gt;&lt;p&gt;Author(s): Umair Haider, Gul Rahman, Imran Shakir, M.S. Al-Buriahi, Norah Alomayrah, Imen Kebaili&lt;/p&gt;</description><author>ScienceDirect Publication: Materials Today Physics</author><pubDate>Fri, 16 Jan 2026 12:43:18 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2542529326000131</guid></item><item><title>[Wiley: Small: Table of Contents] Machine Learning‐Accelerated Specific Surface Prediction Strategy in Janus‐Based Z‐Scheme Heterostructures for Efficient Photocatalytic Water Splitting</title><link>https://onlinelibrary.wiley.com/doi/10.1002/smll.202509069?af=R</link><description>Small, Volume 22, Issue 4, 16 January 2026.</description><author>Wiley: Small: Table of Contents</author><pubDate>Fri, 16 Jan 2026 08:21:14 GMT</pubDate><guid isPermaLink="true">10.1002/smll.202509069</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Machine Learning‐Guided Design of L10‐PtCo Intermetallic Catalysts: Zn‐Mediated Atomic Ordering</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202505211?af=R</link><description>Advanced Energy Materials, EarlyView.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Fri, 16 Jan 2026 05:15:00 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202505211</guid></item><item><title>[cond-mat updates on arXiv.org] Performance of AI agents based on reasoning language models on ALD process optimization tasks</title><link>https://arxiv.org/abs/2601.09980</link><description>arXiv:2601.09980v1 Announce Type: new 
 Abstract: In this work we explore the performance and behavior of reasoning large language models to autonomously optimize atomic layer deposition (ALD) processes. In the ALD process optimization task, an agent built on top of a reasoning LLM has to find optimal dose times for an ALD precursor and a coreactant without any prior knowledge on the process, including whether it is actually self-limited. The agent is meant to interact iteratively with an ALD reactor in a fully unsupervised way. We evaluate this agent using a simple model of an ALD tool that incorporates ALD processes with different self-limited surface reaction pathways as well as a non self-limited component. Our results show that agents based on reasoning models like OpenAI's o3 and GPT5 consistently succeeded at completing this optimization task. However, we observed significant run-to-run variability due to the non deterministic nature of the model's response. In order to understand the logic followed by the reasoning model, the agent uses a two step process in which the model first generates an open response detailing the reasoning process. This response is then transformed into a structured output. An analysis of these reasoning traces showed that the logic of the model was sound and that its reasoning was based on the notions of self-limited process and saturation expected in the case of ALD. However, the agent can sometimes be misled by its own prior choices when exploring the optimization space.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 16 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.09980v1</guid></item><item><title>[cond-mat updates on arXiv.org] Advanced Manufacturing with Renewable and Bio-based Materials: AI/ML workflows and Process Optimization</title><link>https://arxiv.org/abs/2601.10382</link><description>arXiv:2601.10382v1 Announce Type: new 
 Abstract: Advanced manufacturing with new bio-derived materials can be achieved faster and more economically with first-principle-based artificial intelligence and machine learning (AI/ML)-derived models and process optimization. Not only is this motivated by increased industry profitability, but it can also be optimized to reduce waste generation, energy consumption, and gas emissions through additive manufacturing (AM) and AI/ML-directed self-driving laboratory (SDL) process optimization. From this perspective, the benefits of using 3D printing technology to manufacture durable, sustainable materials will enable high-value reuse and promote a better circular economy. Using AI/ML workflows at different levels, it is possible to optimize the synthesis and adaptation of new bio-derived materials with self-correcting 3D printing methods, and in-situ characterization. Working with training data and hypotheses derived from Large Language Models (LLMs) and algorithms, including ML-optimized simulation, it is possible to demonstrate more field convergence. The combination of SDL and AI/ML Workflows can be the norm for improved use of biobased and renewable materials towards advanced manufacturing. This should result in faster and better structure, composition, processing, and properties (SCPP) correlation. More agentic AI tasks, as well as supervised or unsupervised learning, can be incorporated to improve optimization protocols continuously. Deep Learning (DL), Reinforcement Learning (RL), and Deep Reinforcement Learning (DRL) with Deep Neural Networks (DNNs) can be applied to more generative AI directions in both AM and SDL, with bio-based materials.</description><author>cond-mat updates on arXiv.org</author><pubDate>Fri, 16 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.10382v1</guid></item><item><title>[cond-mat updates on arXiv.org] A Generalizable Framework for Building Executable Domain-Specific LLMs under Data Scarcity: Demonstration on Semiconductor TCAD Simulation</title><link>https://arxiv.org/abs/2601.10128</link><description>arXiv:2601.10128v1 Announce Type: cross 
 Abstract: Scientific and engineering verticals often suffer from data scarcity and strict executability requirements: models must generate not only fluent text, but also syntactically valid, tool-compilable scripts. We present a schema-first alignment framework for building compact, executable domain-specific LLMs in low-resource settings. The framework integrates three core components: (i) large-scale synthetic QA data generation from expert documentation to instill foundational domain knowledge; (ii) a code-centric IR-&gt;DPO workflow that converts verified tool decks into interpretable intermediate representations (IR), performs equivalence-preserving diversification, and constructs preference pairs to directly optimize instruction compliance and code executability; and (iii) a controlled evaluation of Retrieval-Augmented Generation (RAG), showing that while RAG benefits general LLMs, it can marginally degrade the performance of already domain-aligned models.
@@ -28,7 +28,9 @@ a profound influence on the crystallinity of MOF-derived ZrO2 polymorph. Our fin
 the utility of small-data-driven predictive ML modeling and transfer learning for guiding the synthesis of
 advanced oxide materials providing a blueprint for accelerated discovery of MOF-derived nanomaterials.</description><author>ChemRxiv</author><pubDate>Fri, 16 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-7lnbc?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] The Role of Oxygen Excess on Fluoride Intercalation in Ruddlesden–Popper Electrodes for Fluoride Ion Batteries: The Case of LaSrMnO4</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-0hcf3?rft_dat=source%3Ddrss</link><description>Ruddlesden–Popper–type compounds are particularly attractive electrode materials for fluoride-ion batteries. Among them, LaSrMnO4 has received significant attention due to its high fluoride incorporation capability and lower environmental impact compared to nickel- and cobalt-based analogues. In this work, neutron diffraction data are used to provide an experimental visualization of fluoride-ion diffusion in this class of materials, through Maximum Entropy Method (MEM) and Bond Valence Site Energy (BVSE) analysis. Additionally, since oxygen excess is well known in Ruddlesden–Popper oxides but its impact on fluoride-ion transport has not been previously investigated, molecular dynamics (MD) simulations were employed to reveal how oxygen over-stoichiometry affects fluoride intercalation mechanisms and energetics, unveiling new migration pathways that hinder fluoride mobility. These findings have direct implications for fluoride-ion battery performance, highlighting the critical role of oxygen content in determining anion transport and the electrochemical performance of this class of materials.</description><author>ChemRxiv</author><pubDate>Fri, 16 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-0hcf3?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] From Cation Order to Disorder: Unlocking Ion Transport Pathways in Li-Zn-Zr-Cl Halospinels</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-zw0j8?rft_dat=source%3Ddrss</link><description>Lithium metal chloride halospinels of the general formula Li2MCl4 are a promising class of earth-abundant ion conductors for all-solid-state batteries. However, poor room-temperature ionic conductivity has historically limited their use in practical applications. Here, we substitute Zr4+ into Li2ZnCl4 along the series Li2−2x/3Zn1−xZr2x/3Cl4 (x = 0, 0.1, 0.3, 0.6, 0.9, and 1.0) to understand how cation disorder and vacancy tuning impacts ion transport in “normal” halospinels. Aliovalent Zr4+ substitution increases ionic conductivity by nearly five orders of magnitude, from 1.320(3) × 10−9 S cm−1 in Li2ZnCl4 to 6.74(1) × 10−5 S cm−1 for x = 0.6. Average and local structure characterization through synchrotron X-ray diffraction
 (SXRD) and neutron pair distribution function (nPDF) analysis reveal that Zr4+ redistributes the Zn2+ and Li+ sublattices into previously unoccupied interstitial sites that form new low-energy hopping pathways that facilitate ion transport. We rationalize the dramatic rearrangement of the cation local structure by considering the coordination
-preferences of the cations and the potential electrostatic penalties incurred by the higher-valent Zr4+ cations. This work delivers an atomistic understanding of substitution-induced cation disorder and ion transport properties in a new family of earth-abundant halospinels.</description><author>ChemRxiv</author><pubDate>Fri, 16 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-zw0j8?rft_dat=source%3Ddrss</guid></item><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>[Recent Articles in Phys. Rev. Lett.] Sub-Doppler Cooling of a Trapped Ion in a Phase-Stable Polarization Gradient</title><link>http://link.aps.org/doi/10.1103/fy3t-f1hz</link><description>Author(s): Ethan Clements, Felix W. Knollmann, Sabrina Corsetti, Zhaoyi Li, Ashton Hattori, Milica Notaros, Reuel Swint, Tal Sneh, May E. Kim, Aaron D. Leu, Patrick Callahan, Thomas Mahony, Gavin N. West, Cheryl Sorace-Agaskar, Dave Kharas, Robert McConnell, Colin D. Bruzewicz, Isaac L. Chuang, Jelena Notaros, and John Chiaverini&lt;br /&gt;&lt;p&gt;Trapped ions provide a highly controlled platform for quantum sensors, clocks, simulators, and computers, all of which depend on cooling ions close to their motional ground state. Existing methods like Doppler, resolved sideband, and dark resonance cooling balance trade-offs between the final temper…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. Lett. 136, 023201] Published Thu Jan 15, 2026</description><author>Recent Articles in Phys. Rev. Lett.</author><pubDate>Thu, 15 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/fy3t-f1hz</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 
+preferences of the cations and the potential electrostatic penalties incurred by the higher-valent Zr4+ cations. This work delivers an atomistic understanding of substitution-induced cation disorder and ion transport properties in a new family of earth-abundant halospinels.</description><author>ChemRxiv</author><pubDate>Fri, 16 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-zw0j8?rft_dat=source%3Ddrss</guid></item><item><title>[RSC - Digital Discovery latest articles] OBELiX: A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State Electrolytes</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00441A</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/D5DD00441A, 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;Felix Therrien, Jamal Abou Haibeh, Divya  Sharma, Rhiannon  Hendley, Leah Wairimu Mungai, Sun  Sun, Alain Tchagang, Jiang  Su, Samuel  Huberman, Yoshua Bengio, Hongyu Guo, Alex Hernandez-Garcia, Homin Shin&lt;br /&gt;Solid-state electrolyte batteries are expected to replace liquid electrolyte lithium-ion batteries in the near future thanks to their higher theoretical energy density and improved safety. However, their adoption is currently...&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, 16 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2025/DD/D5DD00441A</guid></item><item><title>[ChemRxiv] Automated QSAR — how good is it in practice?</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-l1d11?rft_dat=source%3Ddrss</link><description>Over the past two decades, quantitative structure–activity relationship (QSAR) modeling has evolved substantially, driven by improved data accessibility, open-source descriptor generation, mature ma- chine learning libraries, and scalable cloud computing. Large-scale benchmarking studies using public datasets have demonstrated the feasibility of building predictive models across hundreds of endpoints. In parallel, automated machine learning (Auto-ML) approaches have emerged as a promising means to lower the barrier to QSAR model development, enabling competitive performance without extensive expert intervention. 
+Here, we describe the design and implementation of an automated QSAR modeling system inte- grated into the CDD Vault platform, referred to as CDD Vault Inference Models. The system auto- matically trains, evaluates, and deploys regression models whenever new assay data become available, without requiring users to select endpoints, descriptors, or learning algorithms. Using public datasets from ChEMBL, we developed a fully automated workflow for model training and continuous evaluation. Models are released when a conservative performance threshold is achieved. The system is currently focused on building regression models. To give users a handle on model uncertainty, we also provide conformal prediction intervals. 
+We discuss the implications of deploying fully automated QSAR models in a production environ- ment and outline future extensions. Together, this work demonstrates that automated, continuously updated QSAR modeling can provide practical and scalable decision support for drug discovery, par- ticularly in settings where dedicated modeling expertise is limited.</description><author>ChemRxiv</author><pubDate>Fri, 16 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-l1d11?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] pyEF: A Python Framework for QM and QM/MM Atom-Wise Electric Field Analysis</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-3cbg4?rft_dat=source%3Ddrss</link><description>We introduce pyEF, a software package for computing molecular electric fields, electrostatic interaction energies, and electrostatic potentials from quantum mechanical (QM) atom-centered multipole expansions with atom-wise decomposable contributions. We demonstrate the computational efficiency and accuracy of this QM-derived electric field evaluation tool through several tests. To assess the influence of the underlying QM method and charge partitioning scheme on these electrostatic quantities, we analyze over 250 configurations of an acetone solute molecule in five solvents of variable polarity. We find that electric field calculations are highly sensitive to the choice of charge partitioning method. Even among real-space charge schemes, acetone Stark tuning rates differ by up to a factor of two. Benchmarking computed solvent dipole moments against experimental bulk values, we conclude that the CM5, ADCH, and Hirshfeld-I charge schemes most reliably capture solvent electrostatics and therefore provide a more faithful foundation for computing electric fields. When constructed from these real-space charges, electric fields are nearly insensitive to basis set size and monotonically increase in magnitude with higher Fock exchange. We also demonstrate efficient convergence of QM electrostatics when more distant molecules are represented solely by MM point charges, reducing computational overhead. Leveraging these findings, we demonstrate the use of pyEF to deduce environmental effects on a transition metal complex from a Ga4L612- nanocage and quantify the dominant role of organic linkers in orchestrating electrostatic preorganization.</description><author>ChemRxiv</author><pubDate>Fri, 16 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-3cbg4?rft_dat=source%3Ddrss</guid></item><item><title>[Joule] “Active material-free” design to overcome mass-transport limitations for high-energy-density all-solid-state Li-S batteries</title><link>https://www.cell.com/joule/fulltext/S2542-4351(25)00420-9?rss=yes</link><description>Artificially preloading active materials cannot guarantee effective three-phase contact, where the ionic conductors, electronic conductors, and active materials meet, making it difficult to achieve efficient sulfur utilization. In our design, the cathode contains no preloaded active material. Instead, it provides abundant interfaces between ionic and electronic conductors, allowing an “invisible hand” to drive the in situ formation of active materials at these interfaces. This design inherently maintains both ionic and electronic pathways, enabling high performance.</description><author>Joule</author><pubDate>Fri, 16 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/joule/fulltext/S2542-4351(25)00420-9?rss=yes</guid></item><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>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Ion Diffusion and (Photo)redox Conductivity in a Covalent Organic Framework</title><link>http://dx.doi.org/10.1021/jacs.5c17763</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c17763/asset/images/medium/ja5c17763_0009.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;Journal of the American Chemical Society&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/jacs.5c17763&lt;/div&gt;</description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Thu, 15 Jan 2026 17:27:53 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c17763</guid></item><item><title>[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Li1+xTaOxF6–x Oxyfluoride Solid Electrolytes with Amorphization-Driven Enhancement of Ion Conduction Channels for 5 V All-Solid-State Batteries</title><link>http://dx.doi.org/10.1021/jacs.5c14825</link><description>&lt;p&gt;&lt;img alt="TOC Graphic" src="https://pubs.acs.org/cms/10.1021/jacs.5c14825/asset/images/medium/ja5c14825_0005.gif" /&gt;&lt;/p&gt;&lt;div&gt;&lt;cite&gt;Journal of the American Chemical Society&lt;/cite&gt;&lt;/div&gt;&lt;div&gt;DOI: 10.1021/jacs.5c14825&lt;/div&gt;</description><author>Journal of the American Chemical Society: Latest Articles (ACS Publications)</author><pubDate>Thu, 15 Jan 2026 15:20:54 GMT</pubDate><guid isPermaLink="true">http://dx.doi.org/10.1021/jacs.5c14825</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>[Recent Articles in Phys. Rev. Lett.] Sub-Doppler Cooling of a Trapped Ion in a Phase-Stable Polarization Gradient</title><link>http://link.aps.org/doi/10.1103/fy3t-f1hz</link><description>Author(s): Ethan Clements, Felix W. Knollmann, Sabrina Corsetti, Zhaoyi Li, Ashton Hattori, Milica Notaros, Reuel Swint, Tal Sneh, May E. Kim, Aaron D. Leu, Patrick Callahan, Thomas Mahony, Gavin N. West, Cheryl Sorace-Agaskar, Dave Kharas, Robert McConnell, Colin D. Bruzewicz, Isaac L. Chuang, Jelena Notaros, and John Chiaverini&lt;br /&gt;&lt;p&gt;Trapped ions provide a highly controlled platform for quantum sensors, clocks, simulators, and computers, all of which depend on cooling ions close to their motional ground state. Existing methods like Doppler, resolved sideband, and dark resonance cooling balance trade-offs between the final temper…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. Lett. 136, 023201] Published Thu Jan 15, 2026</description><author>Recent Articles in Phys. Rev. Lett.</author><pubDate>Thu, 15 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/fy3t-f1hz</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: 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