From 717c3d5ea716af677639e36569b7508f91d1f11e Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Thu, 8 Jan 2026 06:34:15 +0000
Subject: [PATCH] Auto-update RSS feed

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-<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, 08 Jan 2026 01:43:04 GMT</lastBuildDate><generator>rfeed v1.1.1</generator><docs>https://github.com/svpino/rfeed/blob/master/README.md</docs><item><title>[ScienceDirect Publication: Journal of Energy Storage] Optimization of porous electrode configuration for organic redox flow battery by machine learning based on back propagation neural network based on firefly</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25047668?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): Fengming Chu, Yongzhuo Wang, Xi Liu, Tong Liu&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Wed, 07 Jan 2026 18:32:48 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25047668</guid></item><item><title>[ScienceDirect Publication: Progress in Materials Science] Advanced simulations from DFT to machine learning for solid-state hydrogen storage: fundamentals, progresses, challenges and perspectives</title><link>https://www.sciencedirect.com/science/article/pii/S0079642525002336?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 6 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Progress in Materials Science&lt;/p&gt;&lt;p&gt;Author(s): Shuling Chen, Mei Yang, Shaoyang Shen, Liuzhang Ouyang&lt;/p&gt;</description><author>ScienceDirect Publication: Progress in Materials Science</author><pubDate>Wed, 07 Jan 2026 18:32:44 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0079642525002336</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Outside Back Cover: Rhodopsin‐Mimicking Reversible Photo‐Switchable Chloride Channels Based on Azobenzene‐Appended Semiaza‐Bambusurils for Light‐Controlled Ion Transport and Cancer Cell Apoptosis</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.2025-m0501054600?af=R</link><description>Angewandte Chemie International Edition, EarlyView.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Wed, 07 Jan 2026 05:23:27 GMT</pubDate><guid isPermaLink="true">10.1002/anie.2025-m0501054600</guid></item><item><title>[Wiley: ENERGY &amp; ENVIRONMENTAL MATERIALS: Table of Contents] Thermodynamic Mechanisms of Co‐S Bond Anchoring in Few‐Layered 1T‐MoS2 for Enhanced Capacitive Performance via Spin State Regulation and Ion Diffusion Kinetics</title><link>https://onlinelibrary.wiley.com/doi/10.1002/eem2.70218?af=R</link><description>ENERGY &amp;amp;ENVIRONMENTAL MATERIALS, EarlyView.</description><author>Wiley: ENERGY &amp; ENVIRONMENTAL MATERIALS: Table of Contents</author><pubDate>Wed, 07 Jan 2026 05:20:14 GMT</pubDate><guid isPermaLink="true">10.1002/eem2.70218</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Customizing Ion Transport by Anionphilic Nanofiber‐Polymer Electrolyte for Stable Zinc Metal Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202519057?af=R</link><description>Advanced Materials, EarlyView.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Wed, 07 Jan 2026 05:17:00 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202519057</guid></item><item><title>[cond-mat updates on arXiv.org] A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse design</title><link>https://arxiv.org/abs/2601.02424</link><description>arXiv:2601.02424v1 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>Thu, 08 Jan 2026 06:34:15 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] 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 
+Abstract: Effective hydrodynamic modeling is crucial for accurately predicting fluid-particle interactions in diverse fields such as biophysics and materials science. Developing and implementing hydrodynamic algorithms is challenging due to the complexity of fluid dynamics, necessitating efficient management of large-scale computations and sophisticated boundary conditions. Furthermore, adapting these algorithms for use on massively parallel architectures like GPUs adds an additional layer of complexity. This paper presents the libMobility software library, which offers a suite of CUDA-enabled solvers for simulating hydrodynamic interactions in particulate systems at the Rotne-Prager-Yamakawa (RPY) level. The library facilitates precise simulations of particle displacements influenced by external forces and torques, including both the deterministic and stochastic components. Notable features of libMobility include its ability to handle linear and angular displacements, thermal fluctuations, and various domain geometries effectively. With an interface in Python, libMobility provides comprehensive tools for researchers in computational fluid dynamics and related fields to simulate particle mobility efficiently. This article details the technical architecture, functionality, and wide-ranging applications of libMobility. libMobility is available at https://github.com/stochasticHydroTools/libMobility.</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:2510.02135v2</guid></item><item><title>[cond-mat updates on arXiv.org] Kinetic Flux Equations for Ion Exchange in Silicate Glasses</title><link>https://arxiv.org/abs/2601.03207</link><description>arXiv:2601.03207v2 Announce Type: replace 
+Abstract: Ion exchange kinetic flux equations have been extensively investigated since the mid-twentieth century and continue to provide a fundamental framework for describing mass transport phenomena in solid materials. Despite the maturity of this field, inconsistencies remain in the literature concerning the definition, dimensional consistency, and physical interpretation of the parameters involved. A rigorous and unified treatment of these equations is therefore essential to ensure the reproducibility and comparability of theoretical and experimental studies. The present study aims to establish a coherent and systematic development of ion exchange kinetic flux equations, with particular emphasis on the consistent definition and dimensional formulation of the relevant physical quantities. Beyond refining the theoretical foundations, this study extends the classical formulation by incorporating the influence of mechanical stress on ion transport and considering cross-term interactions within the framework of linear irreversible thermodynamics. These developments provide a more comprehensive description of ion exchange kinetics, particularly as applied to silicate glasses, where coupling between chemical and mechanical effects plays a crucial role in determining transport behavior and performance.</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:2601.03207v2</guid></item><item><title>[cond-mat updates on arXiv.org] Agentic Exploration of Physics Models</title><link>https://arxiv.org/abs/2509.24978</link><description>arXiv:2509.24978v4 Announce Type: replace-cross 
+Abstract: The process of scientific discovery relies on an interplay of observations, analysis, and hypothesis generation. Machine learning is increasingly being adopted to address individual aspects of this process. However, it remains an open challenge to fully automate the heuristic, iterative loop required to discover the laws of an unknown system by exploring it through experiments and analysis, without tailoring the approach to the specifics of a given task. Here, we introduce SciExplorer, an agent that leverages large language model tool-use capabilities to enable exploration of systems without any domain-specific blueprints, and apply it to physical systems that are initially unknown to the agent. We test SciExplorer on a broad set of models spanning mechanical dynamical systems, wave evolution, and quantum many-body physics. Despite using a minimal set of tools, primarily based on code execution, we observe impressive performance on tasks such as recovering equations of motion from observed dynamics and inferring Hamiltonians from expectation values. The demonstrated effectiveness of this setup opens the door towards similar scientific exploration in other domains, without the need for finetuning or task-specific instructions.</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:2509.24978v4</guid></item><item><title>[cond-mat updates on arXiv.org] Masgent: An AI-assisted Materials Simulation Agent</title><link>https://arxiv.org/abs/2512.23010</link><description>arXiv:2512.23010v2 Announce Type: replace-cross 
+Abstract: Density functional theory (DFT) and machine learning potentials (MLPs) are essential for predicting and understanding materials properties, yet preparing, executing, and analyzing these simulations typically requires extensive scripting, multi-step procedures, and significant high-performance computing (HPC) expertise. These challenges hinder reproducibility and slow down discovery. Here, we introduce Masgent, an AI-assisted materials simulation agent that unifies structure manipulation, automated VASP input generation, DFT workflow construction and analysis, fast MLP-based simulations, and lightweight machine learning (ML) utilities within a single platform. Powered by large language models (LLMs), Masgent enables researchers to perform complex simulation tasks through natural-language interaction, eliminating most manual scripting and reducing setup time from hours to seconds. By standardizing protocols and integrating advanced simulation and data-driven tools, Masgent lowers the barrier to performing state-of-the-art computational methodologies, enabling faster hypothesis testing, pre-screening, and exploratory research for both new and experienced practitioners.</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:2512.23010v2</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Optimization of porous electrode configuration for organic redox flow battery by machine learning based on back propagation neural network based on firefly</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25047668?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): Fengming Chu, Yongzhuo Wang, Xi Liu, Tong Liu&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Wed, 07 Jan 2026 18:32:48 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25047668</guid></item><item><title>[ScienceDirect Publication: Progress in Materials Science] Advanced simulations from DFT to machine learning for solid-state hydrogen storage: fundamentals, progresses, challenges and perspectives</title><link>https://www.sciencedirect.com/science/article/pii/S0079642525002336?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: Available online 6 January 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Progress in Materials Science&lt;/p&gt;&lt;p&gt;Author(s): Shuling Chen, Mei Yang, Shaoyang Shen, Liuzhang Ouyang&lt;/p&gt;</description><author>ScienceDirect Publication: Progress in Materials Science</author><pubDate>Wed, 07 Jan 2026 18:32:44 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0079642525002336</guid></item><item><title>[Wiley: Angewandte Chemie International Edition: Table of Contents] Outside Back Cover: Rhodopsin‐Mimicking Reversible Photo‐Switchable Chloride Channels Based on Azobenzene‐Appended Semiaza‐Bambusurils for Light‐Controlled Ion Transport and Cancer Cell Apoptosis</title><link>https://onlinelibrary.wiley.com/doi/10.1002/anie.2025-m0501054600?af=R</link><description>Angewandte Chemie International Edition, EarlyView.</description><author>Wiley: Angewandte Chemie International Edition: Table of Contents</author><pubDate>Wed, 07 Jan 2026 05:23:27 GMT</pubDate><guid isPermaLink="true">10.1002/anie.2025-m0501054600</guid></item><item><title>[Wiley: ENERGY &amp; ENVIRONMENTAL MATERIALS: Table of Contents] Thermodynamic Mechanisms of Co‐S Bond Anchoring in Few‐Layered 1T‐MoS2 for Enhanced Capacitive Performance via Spin State Regulation and Ion Diffusion Kinetics</title><link>https://onlinelibrary.wiley.com/doi/10.1002/eem2.70218?af=R</link><description>ENERGY &amp;amp;ENVIRONMENTAL MATERIALS, EarlyView.</description><author>Wiley: ENERGY &amp; ENVIRONMENTAL MATERIALS: Table of Contents</author><pubDate>Wed, 07 Jan 2026 05:20:14 GMT</pubDate><guid isPermaLink="true">10.1002/eem2.70218</guid></item><item><title>[Wiley: Advanced Materials: Table of Contents] Customizing Ion Transport by Anionphilic Nanofiber‐Polymer Electrolyte for Stable Zinc Metal Batteries</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adma.202519057?af=R</link><description>Advanced Materials, EarlyView.</description><author>Wiley: Advanced Materials: Table of Contents</author><pubDate>Wed, 07 Jan 2026 05:17:00 GMT</pubDate><guid isPermaLink="true">10.1002/adma.202519057</guid></item><item><title>[cond-mat updates on arXiv.org] A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse design</title><link>https://arxiv.org/abs/2601.02424</link><description>arXiv:2601.02424v1 Announce Type: new 
 Abstract: The synthesis of nanocrystals has been highly dependent on trial-and-error, due to the complex correlation between synthesis parameters and physicochemical properties. Although deep learning offers a potential methodology to achieve generative inverse design, it is still hindered by the scarcity of high-quality datasets that align nanocrystal synthesis routes with their properties. Here, we present the construction of a large-scale, aligned Nanocrystal Synthesis-Property (NSP) database and demonstrate its capability for generative inverse design. To extract structured synthesis routes and their corresponding product properties from literature, we develop NanoExtractor, a large language model (LLM) enhanced by well-designed augmentation strategies. NanoExtractor is validated against human experts, achieving a weighted average score of 88% on the test set, significantly outperforming chemistry-specialized (3%) and general-purpose LLMs (38%). The resulting NSP database contains nearly 160,000 aligned entries and serves as training data for our NanoDesigner, an LLM for inverse synthesis design. The generative capability of NanoDesigner is validated through the successful design of viable synthesis routes for both well-established PbSe nanocrystals and rarely reported MgF2 nanocrystals. Notably, the model recommends a counter-intuitive, non-stoichiometric precursor ratio (1:1) for MgF2 nanocrystals, which is experimentally confirmed as critical for suppressing byproducts. Our work bridges the gap between unstructured literature and data-driven synthesis, and also establishes a powerful human-AI collaborative paradigm for accelerating nanocrystal discovery.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.02424v1</guid></item><item><title>[cond-mat updates on arXiv.org] Protein-Water Energy Transfer via Anharmonic Low-Frequency Vibrations</title><link>https://arxiv.org/abs/2601.02699</link><description>arXiv:2601.02699v1 Announce Type: new 
 Abstract: Heat dissipation is ubiquitous in living systems, which constantly convert distinct forms of energy into each other. The transport of thermal energy in liquids and even within proteins is well understood but kinetic energy transfer across a heterogeneous molecular boundary provides additional challenges. Here, we use atomistic molecular dynamics simulations under steady-state conditions to analyze how a protein dissipates surplus thermal energy into the surrounding solvent. We specifically focus on collective degrees of freedom that govern the dynamics of the system from the diffusive regime to mid-infrared frequencies. Using a fully anharmonic analysis of molecular vibrations, we analyzed their vibrational spectra, temperatures, and heat transport efficiencies. We find that the most efficient energy transfer mechanisms are associated with solvent-mediated friction. However, this mechanism only applies to a small number of degrees of freedom of a protein. Instead, less efficient vibrational energy transfer in the far-infrared dominates heat transfer overall due to a large number of vibrations in this frequency range. A notable by-product of this work is a highly sensitive measure of deviations from energy equi-partition in equilibrium systems, which can be used to analyze non-ergodic properties.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.02699v1</guid></item><item><title>[cond-mat updates on arXiv.org] Interplay of Structure and Dynamics in Solid Polymer Electrolytes: a Molecular Dynamics Study of LiPF6/polypropylene carbonate</title><link>https://arxiv.org/abs/2601.02869</link><description>arXiv:2601.02869v1 Announce Type: new 
 Abstract: Solid-state batteries (SSB) are emerging as next-generation electrochemical energy storage devices. Achieving high energy density in SSB relies on solid polymer electrolytes (SPE) that are electrochemically stable against both lithium metal and high-potential positive electrodes, two conditions that are difficult to satisfy without chemical degradation. In this work, molecular dynamics simulations are employed to investigate the relationship between structure and dynamics in carbonate-based SPE composed of polypropylene carbonate and lithium hexafluorophosphate (LiPF$_6$), at salt concentrations ranging from 0.32 to 1.21 mol$/$kg. Structural properties are analyzed under ambient pressure at the experimentally relevant temperature $T = 353$ K. Since the slow dynamical processes governing ion transport in these systems are inaccessible to direct molecular dynamics, transport properties are simulated at elevated temperatures up to 900 K and extrapolated to $T = 353$ K using Arrhenius behavior. The results reveal strong ionic correlations, a limited fraction of free ions, and a predominance of negatively charged clusters, especially at high salt concentration. At high temperature, the self-diffusion coefficient of Li$^+$ exceeds that of PF$_6^-$ due to weaker Li$^+$-carbonate and ion-ion interactions. However, at $T = 353$ K, Li$^+$ mobility becomes lower than that of the anion, consistent with typical experimental observations in SPE. As expected, the ionic conductivity $\sigma$ increases with temperature, while at $T = 353$ K it exhibits a maximum for salt concentrations between 1.0 and 1.1 mol$/$kg. Overall, the estimated physico-chemical parameters highlight the key role of ion correlations in SPE and suggest strategies to optimize electrolyte performance. The Arrhenius extrapolation approach used here provides valuable insight into ion transport mechanisms in solid polymer electrolytes.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.02869v1</guid></item><item><title>[cond-mat updates on arXiv.org] DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations</title><link>https://arxiv.org/abs/2601.02938</link><description>arXiv:2601.02938v1 Announce Type: new 
@@ -9,7 +16,7 @@ Abstract: Ion exchange kinetic flux equations have been extensively investigated
 Abstract: H-theorem provides a microscopic foundation of the Second Law of Thermodynamics and is therefore essential to establishing statistical physics, but at the same time, H-theorem has been subject to controversy that in part persists till this day. To better understand H-theorem and its relation to the arrow of time, we study the equilibration of randomly oriented and positioned hard disks with periodic boundary conditions. Using a model based on the DeepSets architecture, which imposes permutation invariance of the particle labels, we train a model to capture the irreversibility of the H-functional.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2508.14003v3</guid></item><item><title>[cond-mat updates on arXiv.org] Bloch oscillations of helicoidal spin-orbit coupled Bose-Einstein condensates in deep optical lattices</title><link>https://arxiv.org/abs/2509.14873</link><description>arXiv:2509.14873v2 Announce Type: replace 
 Abstract: We consider helicoidal spin-orbit coupled Bose-Einstein condensates in deep optical lattice and study the dynamics of Bloch oscillation. We show that the variation of helicoidal gauge potential with spin-orbit coupling is different in zero-momentum and plane-wave phases. The characteristics of Bloch oscillation are different in the two phases. In the zero-momentum phase, the Bloch oscillation is harmonic while it is anharmonic in the plane-wave phase. The amplitude of Bloch oscillation is found to be affected by the helicoidal gauge potential and spin-orbit coupling. We examine that the decay of Bloch oscillation caused by mean-field interaction can be managed by helicoidal spin-orbit coupling.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2509.14873v2</guid></item><item><title>[cond-mat updates on arXiv.org] Tuning Separator Chemistry: Improving Zn Anode Compatibility via Functionalized Chitin Nanofibers</title><link>https://arxiv.org/abs/2512.19449</link><description>arXiv:2512.19449v2 Announce Type: replace 
 Abstract: Aqueous zinc (Zn) batteries (AZBs) face significant challenges due to the limited compatibility of Zn anodes with conventional separators, leading to dendrite growth, hydrogen evolution reaction (HER), and poor cycling stability. While separator design is crucial for optimizing battery performance, its potential remains underexplored. The commonly used glass fiber (GF) filters were not originally designed as battery separators. To address their limitations, nanochitin derived from waste shrimp shells was used to fabricate separators with varying concentrations of amine and carboxylic functional groups. This study investigates how the type and concentration of these groups influence the separator's properties and performance. In a mild acidic electrolyte that protonates the amine groups, the results showed that the density of both ammonium and carboxylic groups in the separators significantly affected water structure and ionic conductivity. Quasi-Elastic Neutron Scattering (QENS) revealed that low-functionalized chitin, particularly with only ammonium groups, promotes strongly bound water with restricted mobility, thereby enhancing Zn plating and stripping kinetics. These separators exhibit exceptional Zn stability over 2000 hours at low current densities (0.5 mA/cm2), maintaining low overpotentials and stable polarization. Additionally, the full cell consisting of Zn||NaV3O8.1.5H2O showed a cycle life of over 2000 cycles at 2 A/g, demonstrating the compatibility of the nanochitin-based separators with low concentrations of functional surface groups. These results demonstrate the importance of a simple separator design for improving the overall performance of AZBs.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.19449v2</guid></item><item><title>[cond-mat updates on arXiv.org] Unsupervised Topological Phase Discovery in Periodically Driven Systems via Floquet-Bloch State</title><link>https://arxiv.org/abs/2512.24822</link><description>arXiv:2512.24822v2 Announce Type: replace-cross 
-Abstract: Floquet engineering offers an unparalleled platform for realizing novel non-equilibrium topological phases. However, the unique structure of Floquet systems, which includes multiple quasienergy gaps, poses a significant challenge to classification using conventional analytical methods. We propose a novel unsupervised machine learning framework that employs a kernel defined in momentum-time ($\boldsymbol{k},t$) space, constructed directly from Floquet-Bloch eigenstates. This approach is intrinsically data-driven and requires no prior knowledge of the underlying topological invariants, providing a fundamental advantage over prior methods that rely on abstract concepts like the micromotion operator or homotopic transformations. Crucially, this work successfully reveals the intrinsic topological characteristics encoded within the Floquet eigenstates themselves. We demonstrate that our method robustly and simultaneously identifies the topological invariants associated with both the $0$-gap and the $\pi$-gap across various symmetry classes (1D AIII, 1D D, and 2D A), establishing a robust methodology for the systematic classification and discovery of complex non-equilibrium topological matter.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.24822v2</guid></item><item><title>[Nature Communications] Thermotropic liquid-assisted interface management enables efficient and stable perovskite solar cells and modules</title><link>https://www.nature.com/articles/s41467-025-68231-0</link><description>&lt;p&gt;Nature Communications, Published online: 07 January 2026; &lt;a href="https://www.nature.com/articles/s41467-025-68231-0"&gt;doi:10.1038/s41467-025-68231-0&lt;/a&gt;&lt;/p&gt;In this work, Chang et al. report a thermotropic liquid additive for perovskite solar cells that enables dynamic interface management, simultaneously passivating defects and suppressing ion migration to deliver high efficiency and substantially enhanced operational stability.</description><author>Nature Communications</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41467-025-68231-0</guid></item><item><title>[ChemRxiv] A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-zd9mr-v6?rft_dat=source%3Ddrss</link><description>Computational blind challenges offer critical, unbiased assessment opportunities to assess and accelerate scientific progress, as demonstrated by a breadth of breakthroughs over the last decade. We report the outcomes and key insights from an open science community blind challenge focused on computational methods in drug discovery, using lead optimization data from the AI-driven Structure-enabled Antiviral Platform (ASAP) Discovery Consortium’s pan-coronavirus antiviral discovery program, in partnership with Polaris and the OpenADMET project. This collaborative initiative invited global participants from both academia and industry to develop and apply computational methods to predict the biochemical potency and crystallographic ligand poses of small molecules against key coronavirus targets, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) main protease (Mpro), as well as multiple ADMET assay endpoints, using previously undisclosed comprehensive experimental drug discovery datasets as benchmarks. By evaluating submissions across multiple tasks and compounds, we established performance leaderboards and conducted meta-analyses to assess methodological strengths, common pitfalls, and areas for improvement. This analysis provides a foundation for best practices in real-world machine learning evaluation, grounded in community-driven benchmarking. We also highlight how next-generation platforms, such as Polaris, enable rigorous challenge design, embedded evaluation frameworks, and broad community engagement. This paper reports the collective findings of the challenge, offering a high-level overview of the data, evaluation infrastructure, and top- performing strategies. We further provide context and support for the accompanying papers authored by the challenge participants in this special issue, which explore individual approaches in greater depth. Together, these contributions aim to advance reproducible, trustworthy, and high-impact computational methods in drug discovery, and to explore best practices and pitfalls in future blind challenge design and execution, including planned initiatives for the OpenADMET project.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-zd9mr-v6?rft_dat=source%3Ddrss</guid></item><item><title>[Nature Communications] Uncertainty-aware machine learning to predict non-cancer human toxicity for the global chemicals market</title><link>https://www.nature.com/articles/s41467-025-67374-4</link><description>&lt;p&gt;Nature Communications, Published online: 07 January 2026; &lt;a href="https://www.nature.com/articles/s41467-025-67374-4"&gt;doi:10.1038/s41467-025-67374-4&lt;/a&gt;&lt;/p&gt;Uncertainty-aware machine learning models predict human toxicity for more than 100,000 chemicals, highlighting potency and uncertainty hotspots to guide safer use and to focus efforts to improve prediction confidence.</description><author>Nature Communications</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41467-025-67374-4</guid></item><item><title>[ChemRxiv] Learning EXAFS from atomic structure through physics-informed machine learning</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-sv3f6?rft_dat=source%3Ddrss</link><description>Extended X-ray absorption fine structure (EXAFS) provides element-specific access to local atomic environments and is widely used to relate structure and reactivity across chemical systems. However, quantitative EXAFS interpretation still relies on manually constructed structural models and extensive parameter tuning, creating a growing bottleneck as experimental datasets increase in size and complexity. Addressing this bottleneck requires a direct and systematic mapping between atomic structure and EXAFS response. Here we introduce AI-EXAFS, a physics-informed graph neural network that predicts full EXAFS spectra directly from three-dimensional atomic coordinates. By formulating the learning problem around the physical principles governing EXAFS signal formation, the model learns transferable structure–spectrum relationships and eliminates the need for user-defined parameter selection at inference. Trained on 86,000 transition-metal complexes, AI-EXAFS reproduces reference theoretical spectra with accuracy consistent with established EXAFS analysis practice and generalizes to experimentally relevant systems, including platinum single-atom catalysts. AI-EXAFS provides an accurate and readily deployable forward model for EXAFS, enabling standardized first-pass structural screening and offering a scalable foundation for future extensions toward more realistic and data-rich EXAFS analysis.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-sv3f6?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Defined by Shape: Elucidating the Molecular Recognition of Dynamic Loops with Covalent Ligands</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-qwc85?rft_dat=source%3Ddrss</link><description>Protein loops harness conformational heterogeneity to perform an array of functions, ranging from catalyzing enzymatic reactions to communicating allosteric signals. Although attractive targets for small molecule modulation, these functional hubs are often considered unligandable due to their lack of well-defined binding pockets and highly dynamic structure. Recent studies, however, have demonstrated the power of covalent chemistry to selectively capture cryptic pockets formed by protein loops. Herein, we leverage machine learning to elucidate the molecular basis of covalent ligand:loop recognition in the transcriptional coactivator Med25. Key to our success was classification by ligand shape prior to model training, which led to descriptive and predictive models. The models were experimentally validated through the synthesis and in vitro testing of novel top-ranked ligands, revealing canonical structure-affinity relationships, including an activity cliff. Further feature analyses identified traditional topological and spatial parameters predictive of binding, and molecular modeling uncovered a potential binding pocket with at least two distinct conformations with high shape complementarity. Collectively, these findings reveal the hidden potential of dynamic loops as specific sites for covalent small molecule modulation, challenging the notion that protein loops are unligandable and demonstrating their capacity for exquisite, shape-based molecular recognition.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-qwc85?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] QuantumPDB: A Workflow for High-Throughput Quantum Cluster Model Generation from Protein Structures</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-w5x1d?rft_dat=source%3Ddrss</link><description>Computational modeling of enzymes provides molecular-level insight into catalysis, but the preparation of quantum mechanical (QM) calculations starting from experimental structures is a significant bottleneck for high-throughput studies. Automated tools developed to accelerate this process may fail to generalize across distinct active site chemistries and geometries. To overcome these limitations, we present QuantumPDB, a Python package that automates the generation of hierarchical coordination/interaction spheres around an active center to create QM cluster models directly from raw protein structures. The workflow integrates structure cleaning, protonation state assignment, and QM calculation setup. It uses chemically meaningful models constructed from contact-based interaction spheres derived from Voronoi tessellation, enabling accurate representation of complex active site geometries. We provide an overview of our modular code and describe how it may be employed to automate high-throughput protein screening. To demonstrate its utility, we curated a dataset of 989 holo-enzymes from the PDB and performed QM calculations on 1,673 enzyme cluster models of 842 of these enzymes. Analysis of computed properties suggests that enzyme environments simulated with density functional theory consistently modulate substrate charge toward neutrality and reduce the substrate dipole moment. This phenomenon appears to be general, even in cases where the active site consists predominantly of neutral residues. By automating and standardizing multi-sphere QM model construction, QuantumPDB provides a robust platform for large-scale, data-driven investigations of proteins.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-w5x1d?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Generalization and Usability of Co-Folded GPCR–Ligand Complexes: A Physics-Guided Assessment</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-1rkqz?rft_dat=source%3Ddrss</link><description>Deep learning co-folding models for end-to-end protein–ligand structure prediction mark a major advance beyond AlphaFold2, yet their reliability for decision-making in drug discovery remains unclear. Here, we benchmark Boltz, a state-of-the-art co-folding model, using a curated set of ligand-bound human G protein-coupled receptors (GPCRs) from families unseen during training. We find that the receptor backbones are generally predicted with reasonable accuracy, but ligand poses often deviate significantly from experimental structures. We then evaluate physics-based refinement with rigid-receptor (Glide) and induced-fit docking (IFD-MD) methods, which recover more than half of the misplaced ligands to near-experimental accuracy. As conventional evaluations for co-folded structures focus on distance-based metrics such as root-mean-squared deviation (RMSD), which can miss subtle but consequential binding-site errors, we carry out a further assessment of Boltz performance using free-energy perturbation (FEP+), which is both accurate and sensitive to starting-structure quality, on curated congeneric ligand series with known binding affinities that target the GPCRs. A significant fraction of the 14 congeneric series tested in this fashion fail to reproduce experimental binding affinities via FEP+ when employing the Boltz generated complex, even when the binding-site RMSD is low in some cases. IFD-MD rescues these failures and restores retrospective FEP signals to native-like level for all of these series. Together, these results delineate current generalization and usability limits of co-folded GPCR–ligand complexes and motivate a workflow that pairs deep learning predictions with physics-based refinement and validation before high-stakes decisions in drug discovery.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-1rkqz?rft_dat=source%3Ddrss</guid></item><item><title>[Communications Physics] Interpolation-based coordinate descent method for parameterized quantum circuits</title><link>https://www.nature.com/articles/s42005-025-02473-8</link><description>&lt;p&gt;Communications Physics, Published online: 07 January 2026; &lt;a href="https://www.nature.com/articles/s42005-025-02473-8"&gt;doi:10.1038/s42005-025-02473-8&lt;/a&gt;&lt;/p&gt;Parameterized quantum circuits are a common tool in variational quantum algorithms and quantum machine learning. The authors design an interpolation-based coordinate descent method that reconstructs the cost landscape from a few circuit runs and achieves more efficient training than standard gradient and coordinate descent methods in our numerical tests.</description><author>Communications Physics</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s42005-025-02473-8</guid></item><item><title>[RSC - Digital Discovery latest articles] A case study on hybrid machine learning and quantum-informed modelling for solubility prediction of drug compounds in organic solvents</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00456J</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00456J" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2026, Advance Article&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00456J, 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;Weiling Wang, Isabel Cooley, Morgan R. Alexander, Ricky D. Wildman, Anna K. Croft, Blair F. Johnston&lt;br /&gt;Machine learning pipeline integrates COSMO-RS and multiple molecular descriptors to predict and interpret solubility across diverse solute–solvent systems.&lt;br /&gt;To cite this article before page numbers are assigned, use the DOI form of citation above.&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>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00456J</guid></item><item><title>[ChemRxiv] ConfDENSE: A conformer aware electron density based machine learning paradigm for navigating the odorant landscape</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-8tmtg?rft_dat=source%3Ddrss</link><description>Olfaction arises from the interaction of odorants with olfactory receptors, a process shaped by molecular geometry, electron distribution, and conformational preference. We present ConfDENSE, a Set2Set enhanced PointNet model that learns directly from Hirshfeld promolecule electron-density point clouds, preserving full 3D electronic in- formation without downsampling.Despite using no receptor structural data, ConfDENSE accurately identifies bioac- tive conformers from ensemble inputs. For the only available human odorant receptor structures, the model’s selected conformers achieve sub-angstrom RMSDs to crystallo- graphic ligand poses and frequently outperform conventional docking. Combining ConfDENSE with explainability analysis further reveals the substruc- tures most responsible for receptor engagement, aligning with experimental interaction patterns. This ligand-centric and interpretable framework naturally supports phar- macophore extraction and scaffold-based design, enabling identification of conserved binding motifs even when receptor structures are missing. ConfDENSE thus provides a compact, physics-aware approach to computational olfaction, linking electron density, conformational preference, and odorant recognition in a structurally agnostic manner.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-8tmtg?rft_dat=source%3Ddrss</guid></item><item><title>[AI for Science - latest papers] MOSES: combining automated ontology construction with a multi-agent system for explainable chemical knowledge reasoning</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae3127</link><description>The vast and multiscale nature of chemical knowledge—from molecular structures to material properties—presents significant challenges for both human researchers and artificial intelligence (AI) systems. While large language models (LLMs) can process chemical information, they operate as black boxes without transparent reasoning. Here, we present our multi-agent ontology system for explainable knowledge synthesis (MOSES), a framework that combines automated knowledge organization with multi-agent collaboration to create an AI system for interpretable chemical knowledge reasoning. Using supramolecular chemistry as a testbed, we automatically constructed an ontology of over 10 000 classes from 52 publications and developed a multi-agent system that enables transparent knowledge retrieval and reasoning. Evaluations by human experts and LLMs show that MOSES significantly outperforms chemistry-oriented LLMs and leading general-purpose LLMs—including GPT-4.1 and o3—as well as GraphRAG-augmented GPT-4.1 models, on complex chemical questions, achieving superior scores in both direct assessments and Elo ratings. MOSES’s traceable reasoning paths reveal how it constructs answers through iterative refinement rather than probabilistic generation. However, we observe an asymmetry in handling positive versus negative knowledge claims, underscoring fundamental challenges in open-world reasoning. Our work demonstrates a pathway toward AI systems that can reason over complex scientific knowledge in a transparent and explainable manner.</description><author>AI for Science - latest papers</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae3127</guid></item><item><title>[Cell Reports Physical Science] A review of advancements and challenges in nanoplastics detection</title><link>https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00641-1?rss=yes</link><description>Zhang et al. review cutting-edge strategies for separating, detecting, and characterizing nanoplastics across environmental and biological systems. This work bridges advances in spectroscopy, microscopy, mass spectrometry, and machine learning to highlight analytical limitations, emerging solutions, and future opportunities for standardized nanoplastics monitoring.</description><author>Cell Reports Physical Science</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00641-1?rss=yes</guid></item><item><title>[iScience] Large language models for predicting one-year major adverse cardiovascular events in acute coronary syndrome</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(26)00019-2?rss=yes</link><description>Effective risk stratification is crucial for managing acute coronary syndrome (ACS). This study evaluated whether general-purpose large language models (LLMs) can reliably execute the complex clinical reasoning required for cardiovascular prognosis. We quantitatively assessed three LLMs —ChatGPT 4o, DeepSeek R1, and Grok 3—for predicting one-year major adverse cardiovascular events (MACEs), using 29 guideline-recommended features from 903 participants in the LM-ACS cohort and 64 participants in the MIMIC database.</description><author>iScience</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(26)00019-2?rss=yes</guid></item><item><title>[iScience] Diverse Intracellular Trafficking of Insulin Analogs by Machine Learning-based Colocalization and Diffusion Analysis</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02777-4?rss=yes</link><description>Insulin signaling is vital for cellular homeostasis, with dysregulation leading to severe metabolic disorders, particularly diabetes. While insulin analogs are crucial in type-1 diabetes treatment, identifying potential variations in intracellular trafficking and sorting from endogenous insulin is challenging. Current methods rely on static imaging and bulk receptor assays in non-physiological conditions, which disrupts native signaling and masks temporal trafficking dynamics. Here, we directly recorded and compared the intracellular trafficking of ATTO655-labeled recombinant human insulin (HI655) and rapid-acting analog insulin aspart (IAsp655) in live cells.</description><author>iScience</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02777-4?rss=yes</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Optimizing solid electrolyte interphase with KOTF for dendrites-free and high-performance Lithium Metal Batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25048984?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 20 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Yangtao Zhou, Dequan Huang, Man Zhang, Guangda Yin, Yi Liang, Qichang Pan, Fenghua Zheng, Sijiang Hu, Hongqiang Wang, Qingyu Li&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25048984</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] A hierarchical sandwich Li&lt;sub&gt;6.4&lt;/sub&gt;Ga&lt;sub&gt;0.2&lt;/sub&gt;La&lt;sub&gt;3&lt;/sub&gt;Zr&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;12&lt;/sub&gt;/ZIF-8@SiO&lt;sub&gt;2&lt;/sub&gt;/PVDF-HFP heterostructure with high ionic conductivity for dendrite-free solid-state lithium batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25048583?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 20 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Hu Wang, Shala Yang, Pengfei Pang, Jiangchao Chen, Yongbo Yan, Mingjie Liao, Dazhi Pang, Zheqi Zhang, Yunyun Zhao, Wenping Liu, Huarui Xu, Guisheng Zhu, Kunpeng Jiang&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25048583</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Hierarchical rose-like VS&lt;sub&gt;2&lt;/sub&gt; with sulfur vacancies for high-performance all-solid-state lithium-ion batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25050005?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 20 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Peidian Chong, Shijie Yu, Lin Zheng, Lei Zhang, Mingdeng Wei, Hongfei Liu, Yi Ren, Jianbiao Wang&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25050005</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Prediction of Lithium-ion battery states via combination of implantable sensors and machine learning</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25047243?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 20 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Zijun Huang, Feng Tong, Guo Chen, Xuan Chen, Xianjie Xu, Zhefu Mu, Jiaxin Sun, Sheng Huang, Xiuquan Gu&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25047243</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] A review on metal–organic framework-based polymer solid-state electrolytes for energy storage</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25049096?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 20 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Zelong Zhuang, Xiaojin Yang, Jie Cui, Jingwei Liu, Xueming Yang&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25049096</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Descriptor and graph-based molecular representations in prediction of copolymer properties using machine learning</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625008183?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): Elaheh Kazemi-Khasragh, Rocío Mercado, Carlos Gonzalez, Maciej Haranczyk&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 06 Jan 2026 12:43:08 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625008183</guid></item><item><title>[Recent Articles in Phys. Rev. B] Signatures of coherent phonon transport in frequency-dependent lattice thermal conductivity</title><link>http://link.aps.org/doi/10.1103/kn91-g9hh</link><description>Author(s): Đorđe Dangić&lt;br /&gt;&lt;p&gt;Thermal transport in highly anharmonic, amorphous, or alloyed materials often deviates from the predictions of conventional phonon-based models. First-principles approaches have introduced a coherent contribution to account for these deviations and to explain ultralow lattice thermal conductivity, b…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 113, 024301] Published Tue Jan 06, 2026</description><author>Recent Articles in Phys. Rev. B</author><pubDate>Tue, 06 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/kn91-g9hh</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Accelerating the Discovery of High‐Conductivity Glass Electrolytes via Machine Learning</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202503813?af=R</link><description>Advanced Energy Materials, EarlyView.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 06 Jan 2026 05:35:12 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202503813</guid></item><item><title>[cond-mat updates on arXiv.org] Physically-Constrained Autoencoder-Assisted Bayesian Optimization for Refinement of High-Dimensional Defect-Sensitive Single Crystalline Structure</title><link>https://arxiv.org/abs/2601.00855</link><description>arXiv:2601.00855v1 Announce Type: new 
+Abstract: Floquet engineering offers an unparalleled platform for realizing novel non-equilibrium topological phases. However, the unique structure of Floquet systems, which includes multiple quasienergy gaps, poses a significant challenge to classification using conventional analytical methods. We propose a novel unsupervised machine learning framework that employs a kernel defined in momentum-time ($\boldsymbol{k},t$) space, constructed directly from Floquet-Bloch eigenstates. This approach is intrinsically data-driven and requires no prior knowledge of the underlying topological invariants, providing a fundamental advantage over prior methods that rely on abstract concepts like the micromotion operator or homotopic transformations. Crucially, this work successfully reveals the intrinsic topological characteristics encoded within the Floquet eigenstates themselves. We demonstrate that our method robustly and simultaneously identifies the topological invariants associated with both the $0$-gap and the $\pi$-gap across various symmetry classes (1D AIII, 1D D, and 2D A), establishing a robust methodology for the systematic classification and discovery of complex non-equilibrium topological matter.</description><author>cond-mat updates on arXiv.org</author><pubDate>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2512.24822v2</guid></item><item><title>[Nature Communications] Thermotropic liquid-assisted interface management enables efficient and stable perovskite solar cells and modules</title><link>https://www.nature.com/articles/s41467-025-68231-0</link><description>&lt;p&gt;Nature Communications, Published online: 07 January 2026; &lt;a href="https://www.nature.com/articles/s41467-025-68231-0"&gt;doi:10.1038/s41467-025-68231-0&lt;/a&gt;&lt;/p&gt;In this work, Chang et al. report a thermotropic liquid additive for perovskite solar cells that enables dynamic interface management, simultaneously passivating defects and suppressing ion migration to deliver high efficiency and substantially enhanced operational stability.</description><author>Nature Communications</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41467-025-68231-0</guid></item><item><title>[ChemRxiv] A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data</title><link>https://dx.doi.org/10.26434/chemrxiv-2025-zd9mr-v6?rft_dat=source%3Ddrss</link><description>Computational blind challenges offer critical, unbiased assessment opportunities to assess and accelerate scientific progress, as demonstrated by a breadth of breakthroughs over the last decade. We report the outcomes and key insights from an open science community blind challenge focused on computational methods in drug discovery, using lead optimization data from the AI-driven Structure-enabled Antiviral Platform (ASAP) Discovery Consortium’s pan-coronavirus antiviral discovery program, in partnership with Polaris and the OpenADMET project. This collaborative initiative invited global participants from both academia and industry to develop and apply computational methods to predict the biochemical potency and crystallographic ligand poses of small molecules against key coronavirus targets, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) main protease (Mpro), as well as multiple ADMET assay endpoints, using previously undisclosed comprehensive experimental drug discovery datasets as benchmarks. By evaluating submissions across multiple tasks and compounds, we established performance leaderboards and conducted meta-analyses to assess methodological strengths, common pitfalls, and areas for improvement. This analysis provides a foundation for best practices in real-world machine learning evaluation, grounded in community-driven benchmarking. We also highlight how next-generation platforms, such as Polaris, enable rigorous challenge design, embedded evaluation frameworks, and broad community engagement. This paper reports the collective findings of the challenge, offering a high-level overview of the data, evaluation infrastructure, and top- performing strategies. We further provide context and support for the accompanying papers authored by the challenge participants in this special issue, which explore individual approaches in greater depth. Together, these contributions aim to advance reproducible, trustworthy, and high-impact computational methods in drug discovery, and to explore best practices and pitfalls in future blind challenge design and execution, including planned initiatives for the OpenADMET project.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2025-zd9mr-v6?rft_dat=source%3Ddrss</guid></item><item><title>[Nature Communications] Uncertainty-aware machine learning to predict non-cancer human toxicity for the global chemicals market</title><link>https://www.nature.com/articles/s41467-025-67374-4</link><description>&lt;p&gt;Nature Communications, Published online: 07 January 2026; &lt;a href="https://www.nature.com/articles/s41467-025-67374-4"&gt;doi:10.1038/s41467-025-67374-4&lt;/a&gt;&lt;/p&gt;Uncertainty-aware machine learning models predict human toxicity for more than 100,000 chemicals, highlighting potency and uncertainty hotspots to guide safer use and to focus efforts to improve prediction confidence.</description><author>Nature Communications</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41467-025-67374-4</guid></item><item><title>[ChemRxiv] Learning EXAFS from atomic structure through physics-informed machine learning</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-sv3f6?rft_dat=source%3Ddrss</link><description>Extended X-ray absorption fine structure (EXAFS) provides element-specific access to local atomic environments and is widely used to relate structure and reactivity across chemical systems. However, quantitative EXAFS interpretation still relies on manually constructed structural models and extensive parameter tuning, creating a growing bottleneck as experimental datasets increase in size and complexity. Addressing this bottleneck requires a direct and systematic mapping between atomic structure and EXAFS response. Here we introduce AI-EXAFS, a physics-informed graph neural network that predicts full EXAFS spectra directly from three-dimensional atomic coordinates. By formulating the learning problem around the physical principles governing EXAFS signal formation, the model learns transferable structure–spectrum relationships and eliminates the need for user-defined parameter selection at inference. Trained on 86,000 transition-metal complexes, AI-EXAFS reproduces reference theoretical spectra with accuracy consistent with established EXAFS analysis practice and generalizes to experimentally relevant systems, including platinum single-atom catalysts. AI-EXAFS provides an accurate and readily deployable forward model for EXAFS, enabling standardized first-pass structural screening and offering a scalable foundation for future extensions toward more realistic and data-rich EXAFS analysis.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-sv3f6?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Defined by Shape: Elucidating the Molecular Recognition of Dynamic Loops with Covalent Ligands</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-qwc85?rft_dat=source%3Ddrss</link><description>Protein loops harness conformational heterogeneity to perform an array of functions, ranging from catalyzing enzymatic reactions to communicating allosteric signals. Although attractive targets for small molecule modulation, these functional hubs are often considered unligandable due to their lack of well-defined binding pockets and highly dynamic structure. Recent studies, however, have demonstrated the power of covalent chemistry to selectively capture cryptic pockets formed by protein loops. Herein, we leverage machine learning to elucidate the molecular basis of covalent ligand:loop recognition in the transcriptional coactivator Med25. Key to our success was classification by ligand shape prior to model training, which led to descriptive and predictive models. The models were experimentally validated through the synthesis and in vitro testing of novel top-ranked ligands, revealing canonical structure-affinity relationships, including an activity cliff. Further feature analyses identified traditional topological and spatial parameters predictive of binding, and molecular modeling uncovered a potential binding pocket with at least two distinct conformations with high shape complementarity. Collectively, these findings reveal the hidden potential of dynamic loops as specific sites for covalent small molecule modulation, challenging the notion that protein loops are unligandable and demonstrating their capacity for exquisite, shape-based molecular recognition.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-qwc85?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] QuantumPDB: A Workflow for High-Throughput Quantum Cluster Model Generation from Protein Structures</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-w5x1d?rft_dat=source%3Ddrss</link><description>Computational modeling of enzymes provides molecular-level insight into catalysis, but the preparation of quantum mechanical (QM) calculations starting from experimental structures is a significant bottleneck for high-throughput studies. Automated tools developed to accelerate this process may fail to generalize across distinct active site chemistries and geometries. To overcome these limitations, we present QuantumPDB, a Python package that automates the generation of hierarchical coordination/interaction spheres around an active center to create QM cluster models directly from raw protein structures. The workflow integrates structure cleaning, protonation state assignment, and QM calculation setup. It uses chemically meaningful models constructed from contact-based interaction spheres derived from Voronoi tessellation, enabling accurate representation of complex active site geometries. We provide an overview of our modular code and describe how it may be employed to automate high-throughput protein screening. To demonstrate its utility, we curated a dataset of 989 holo-enzymes from the PDB and performed QM calculations on 1,673 enzyme cluster models of 842 of these enzymes. Analysis of computed properties suggests that enzyme environments simulated with density functional theory consistently modulate substrate charge toward neutrality and reduce the substrate dipole moment. This phenomenon appears to be general, even in cases where the active site consists predominantly of neutral residues. By automating and standardizing multi-sphere QM model construction, QuantumPDB provides a robust platform for large-scale, data-driven investigations of proteins.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-w5x1d?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Generalization and Usability of Co-Folded GPCR–Ligand Complexes: A Physics-Guided Assessment</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-1rkqz?rft_dat=source%3Ddrss</link><description>Deep learning co-folding models for end-to-end protein–ligand structure prediction mark a major advance beyond AlphaFold2, yet their reliability for decision-making in drug discovery remains unclear. Here, we benchmark Boltz, a state-of-the-art co-folding model, using a curated set of ligand-bound human G protein-coupled receptors (GPCRs) from families unseen during training. We find that the receptor backbones are generally predicted with reasonable accuracy, but ligand poses often deviate significantly from experimental structures. We then evaluate physics-based refinement with rigid-receptor (Glide) and induced-fit docking (IFD-MD) methods, which recover more than half of the misplaced ligands to near-experimental accuracy. As conventional evaluations for co-folded structures focus on distance-based metrics such as root-mean-squared deviation (RMSD), which can miss subtle but consequential binding-site errors, we carry out a further assessment of Boltz performance using free-energy perturbation (FEP+), which is both accurate and sensitive to starting-structure quality, on curated congeneric ligand series with known binding affinities that target the GPCRs. A significant fraction of the 14 congeneric series tested in this fashion fail to reproduce experimental binding affinities via FEP+ when employing the Boltz generated complex, even when the binding-site RMSD is low in some cases. IFD-MD rescues these failures and restores retrospective FEP signals to native-like level for all of these series. Together, these results delineate current generalization and usability limits of co-folded GPCR–ligand complexes and motivate a workflow that pairs deep learning predictions with physics-based refinement and validation before high-stakes decisions in drug discovery.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-1rkqz?rft_dat=source%3Ddrss</guid></item><item><title>[Communications Physics] Interpolation-based coordinate descent method for parameterized quantum circuits</title><link>https://www.nature.com/articles/s42005-025-02473-8</link><description>&lt;p&gt;Communications Physics, Published online: 07 January 2026; &lt;a href="https://www.nature.com/articles/s42005-025-02473-8"&gt;doi:10.1038/s42005-025-02473-8&lt;/a&gt;&lt;/p&gt;Parameterized quantum circuits are a common tool in variational quantum algorithms and quantum machine learning. The authors design an interpolation-based coordinate descent method that reconstructs the cost landscape from a few circuit runs and achieves more efficient training than standard gradient and coordinate descent methods in our numerical tests.</description><author>Communications Physics</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s42005-025-02473-8</guid></item><item><title>[RSC - Digital Discovery latest articles] A case study on hybrid machine learning and quantum-informed modelling for solubility prediction of drug compounds in organic solvents</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00456J</link><description>&lt;div&gt;&lt;p&gt;&lt;img align="center" src="http://pubs.rsc.org/services/images/RSCpubs.ePlatform.Service.FreeContent.ImageService.svc/ImageService/image/GA?id=D5DD00456J" /&gt;&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;i&gt;&lt;b&gt;Digital Discovery&lt;/b&gt;&lt;/i&gt;, 2026, Advance Article&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5DD00456J, 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;Weiling Wang, Isabel Cooley, Morgan R. Alexander, Ricky D. Wildman, Anna K. Croft, Blair F. Johnston&lt;br /&gt;Machine learning pipeline integrates COSMO-RS and multiple molecular descriptors to predict and interpret solubility across diverse solute–solvent systems.&lt;br /&gt;To cite this article before page numbers are assigned, use the DOI form of citation above.&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>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/DD/D5DD00456J</guid></item><item><title>[ChemRxiv] ConfDENSE: A conformer aware electron density based machine learning paradigm for navigating the odorant landscape</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-8tmtg?rft_dat=source%3Ddrss</link><description>Olfaction arises from the interaction of odorants with olfactory receptors, a process shaped by molecular geometry, electron distribution, and conformational preference. We present ConfDENSE, a Set2Set enhanced PointNet model that learns directly from Hirshfeld promolecule electron-density point clouds, preserving full 3D electronic in- formation without downsampling.Despite using no receptor structural data, ConfDENSE accurately identifies bioac- tive conformers from ensemble inputs. For the only available human odorant receptor structures, the model’s selected conformers achieve sub-angstrom RMSDs to crystallo- graphic ligand poses and frequently outperform conventional docking. Combining ConfDENSE with explainability analysis further reveals the substruc- tures most responsible for receptor engagement, aligning with experimental interaction patterns. This ligand-centric and interpretable framework naturally supports phar- macophore extraction and scaffold-based design, enabling identification of conserved binding motifs even when receptor structures are missing. ConfDENSE thus provides a compact, physics-aware approach to computational olfaction, linking electron density, conformational preference, and odorant recognition in a structurally agnostic manner.</description><author>ChemRxiv</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-8tmtg?rft_dat=source%3Ddrss</guid></item><item><title>[AI for Science - latest papers] MOSES: combining automated ontology construction with a multi-agent system for explainable chemical knowledge reasoning</title><link>https://iopscience.iop.org/article/10.1088/3050-287X/ae3127</link><description>The vast and multiscale nature of chemical knowledge—from molecular structures to material properties—presents significant challenges for both human researchers and artificial intelligence (AI) systems. While large language models (LLMs) can process chemical information, they operate as black boxes without transparent reasoning. Here, we present our multi-agent ontology system for explainable knowledge synthesis (MOSES), a framework that combines automated knowledge organization with multi-agent collaboration to create an AI system for interpretable chemical knowledge reasoning. Using supramolecular chemistry as a testbed, we automatically constructed an ontology of over 10 000 classes from 52 publications and developed a multi-agent system that enables transparent knowledge retrieval and reasoning. Evaluations by human experts and LLMs show that MOSES significantly outperforms chemistry-oriented LLMs and leading general-purpose LLMs—including GPT-4.1 and o3—as well as GraphRAG-augmented GPT-4.1 models, on complex chemical questions, achieving superior scores in both direct assessments and Elo ratings. MOSES’s traceable reasoning paths reveal how it constructs answers through iterative refinement rather than probabilistic generation. However, we observe an asymmetry in handling positive versus negative knowledge claims, underscoring fundamental challenges in open-world reasoning. Our work demonstrates a pathway toward AI systems that can reason over complex scientific knowledge in a transparent and explainable manner.</description><author>AI for Science - latest papers</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://iopscience.iop.org/article/10.1088/3050-287X/ae3127</guid></item><item><title>[Cell Reports Physical Science] A review of advancements and challenges in nanoplastics detection</title><link>https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00641-1?rss=yes</link><description>Zhang et al. review cutting-edge strategies for separating, detecting, and characterizing nanoplastics across environmental and biological systems. This work bridges advances in spectroscopy, microscopy, mass spectrometry, and machine learning to highlight analytical limitations, emerging solutions, and future opportunities for standardized nanoplastics monitoring.</description><author>Cell Reports Physical Science</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/cell-reports-physical-science/fulltext/S2666-3864(25)00641-1?rss=yes</guid></item><item><title>[iScience] Large language models for predicting one-year major adverse cardiovascular events in acute coronary syndrome</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(26)00019-2?rss=yes</link><description>Effective risk stratification is crucial for managing acute coronary syndrome (ACS). This study evaluated whether general-purpose large language models (LLMs) can reliably execute the complex clinical reasoning required for cardiovascular prognosis. We quantitatively assessed three LLMs —ChatGPT 4o, DeepSeek R1, and Grok 3—for predicting one-year major adverse cardiovascular events (MACEs), using 29 guideline-recommended features from 903 participants in the LM-ACS cohort and 64 participants in the MIMIC database.</description><author>iScience</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(26)00019-2?rss=yes</guid></item><item><title>[iScience] Diverse Intracellular Trafficking of Insulin Analogs by Machine Learning-based Colocalization and Diffusion Analysis</title><link>https://www.cell.com/iscience/fulltext/S2589-0042(25)02777-4?rss=yes</link><description>Insulin signaling is vital for cellular homeostasis, with dysregulation leading to severe metabolic disorders, particularly diabetes. While insulin analogs are crucial in type-1 diabetes treatment, identifying potential variations in intracellular trafficking and sorting from endogenous insulin is challenging. Current methods rely on static imaging and bulk receptor assays in non-physiological conditions, which disrupts native signaling and masks temporal trafficking dynamics. Here, we directly recorded and compared the intracellular trafficking of ATTO655-labeled recombinant human insulin (HI655) and rapid-acting analog insulin aspart (IAsp655) in live cells.</description><author>iScience</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.cell.com/iscience/fulltext/S2589-0042(25)02777-4?rss=yes</guid></item><item><title>[Applied Physics Letters Current Issue] Rigid-flexible free-standing multichannel carbon nanofiber-silicon composite anodes due to PS-induced channel ordering</title><link>https://pubs.aip.org/aip/apl/article/128/1/013903/3376912/Rigid-flexible-free-standing-multichannel-carbon</link><description>&lt;span class="paragraphSection"&gt;Silicon (Si)-carbon composite has been regarded as one of the most promising anodes for next-generation lithium-ion batteries (LIBs). However, low mechanical strength of carbon matrix is incapable of maintaining structural stability and electron/ion conductivity of Si anodes. Herein, we employ electrospinning-carbonization to construct free-standing Si@carbon nanofibers with internal ordered channels, uniformly distributed Si nanoparticles, and extraordinary elastic modulus (0.22 GPa) by introducing polystyrene as an oriented filler. The free-standing and rigid-flexible ordered multichannel carbon nanofibers (OM-CNFs) can absorb the volume variation of Si, effectively enhancing the mechanical strength and chemical stability of electrodes. The Si nanoparticles uniformly embedded into the highly conductive OM-CNFs matrix establish a bicontinuous structure and increase the contact area between Si and CNFs, thus boosting the rate capability. Consequently, the Si@OM-CNFs anode delivers an excellent reversible capacity of 939.9 mA h g&lt;sup&gt;−1&lt;/sup&gt; even at 5 A g&lt;sup&gt;−1&lt;/sup&gt; after 300 cycles. The assembled full-cell with a prelithiated Si@OM-CNFs anode and LiFePO&lt;sub&gt;4&lt;/sub&gt; cathode delivers a high energy density of 341 Wh kg&lt;sup&gt;−1&lt;/sup&gt;. This work provides insights into the design of high mechanical strength Si/C composite anodes for high-performance LIBs.&lt;/span&gt;</description><author>Applied Physics Letters Current Issue</author><pubDate>Wed, 07 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apl/article/128/1/013903/3376912/Rigid-flexible-free-standing-multichannel-carbon</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Optimizing solid electrolyte interphase with KOTF for dendrites-free and high-performance Lithium Metal Batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25048984?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 20 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Yangtao Zhou, Dequan Huang, Man Zhang, Guangda Yin, Yi Liang, Qichang Pan, Fenghua Zheng, Sijiang Hu, Hongqiang Wang, Qingyu Li&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25048984</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] A hierarchical sandwich Li&lt;sub&gt;6.4&lt;/sub&gt;Ga&lt;sub&gt;0.2&lt;/sub&gt;La&lt;sub&gt;3&lt;/sub&gt;Zr&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;12&lt;/sub&gt;/ZIF-8@SiO&lt;sub&gt;2&lt;/sub&gt;/PVDF-HFP heterostructure with high ionic conductivity for dendrite-free solid-state lithium batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25048583?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 20 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Hu Wang, Shala Yang, Pengfei Pang, Jiangchao Chen, Yongbo Yan, Mingjie Liao, Dazhi Pang, Zheqi Zhang, Yunyun Zhao, Wenping Liu, Huarui Xu, Guisheng Zhu, Kunpeng Jiang&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25048583</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Hierarchical rose-like VS&lt;sub&gt;2&lt;/sub&gt; with sulfur vacancies for high-performance all-solid-state lithium-ion batteries</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25050005?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 20 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Peidian Chong, Shijie Yu, Lin Zheng, Lei Zhang, Mingdeng Wei, Hongfei Liu, Yi Ren, Jianbiao Wang&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25050005</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] Prediction of Lithium-ion battery states via combination of implantable sensors and machine learning</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25047243?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 20 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Zijun Huang, Feng Tong, Guo Chen, Xuan Chen, Xianjie Xu, Zhefu Mu, Jiaxin Sun, Sheng Huang, Xiuquan Gu&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25047243</guid></item><item><title>[ScienceDirect Publication: Journal of Energy Storage] A review on metal–organic framework-based polymer solid-state electrolytes for energy storage</title><link>https://www.sciencedirect.com/science/article/pii/S2352152X25049096?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: 20 February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Energy Storage, Volume 147&lt;/p&gt;&lt;p&gt;Author(s): Zelong Zhuang, Xiaojin Yang, Jie Cui, Jingwei Liu, Xueming Yang&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Energy Storage</author><pubDate>Tue, 06 Jan 2026 18:31:06 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S2352152X25049096</guid></item><item><title>[ScienceDirect Publication: Computational Materials Science] Descriptor and graph-based molecular representations in prediction of copolymer properties using machine learning</title><link>https://www.sciencedirect.com/science/article/pii/S0927025625008183?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): Elaheh Kazemi-Khasragh, Rocío Mercado, Carlos Gonzalez, Maciej Haranczyk&lt;/p&gt;</description><author>ScienceDirect Publication: Computational Materials Science</author><pubDate>Tue, 06 Jan 2026 12:43:08 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0927025625008183</guid></item><item><title>[Recent Articles in Phys. Rev. B] Signatures of coherent phonon transport in frequency-dependent lattice thermal conductivity</title><link>http://link.aps.org/doi/10.1103/kn91-g9hh</link><description>Author(s): Đorđe Dangić&lt;br /&gt;&lt;p&gt;Thermal transport in highly anharmonic, amorphous, or alloyed materials often deviates from the predictions of conventional phonon-based models. First-principles approaches have introduced a coherent contribution to account for these deviations and to explain ultralow lattice thermal conductivity, b…&lt;/p&gt;&lt;br /&gt;[Phys. Rev. B 113, 024301] Published Tue Jan 06, 2026</description><author>Recent Articles in Phys. Rev. B</author><pubDate>Tue, 06 Jan 2026 10:00:00 GMT</pubDate><guid isPermaLink="true">http://link.aps.org/doi/10.1103/kn91-g9hh</guid></item><item><title>[Wiley: Advanced Energy Materials: Table of Contents] Accelerating the Discovery of High‐Conductivity Glass Electrolytes via Machine Learning</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/aenm.202503813?af=R</link><description>Advanced Energy Materials, EarlyView.</description><author>Wiley: Advanced Energy Materials: Table of Contents</author><pubDate>Tue, 06 Jan 2026 05:35:12 GMT</pubDate><guid isPermaLink="true">10.1002/aenm.202503813</guid></item><item><title>[cond-mat updates on arXiv.org] Physically-Constrained Autoencoder-Assisted Bayesian Optimization for Refinement of High-Dimensional Defect-Sensitive Single Crystalline Structure</title><link>https://arxiv.org/abs/2601.00855</link><description>arXiv:2601.00855v1 Announce Type: new 
 Abstract: Physical properties and functionalities of materials are dictated by global crystal structures as well as local defects. To establish a structure-property relationship, not only the crystallographic symmetry but also quantitative knowledge about defects are required. Here we present a hybrid Machine Learning framework that integrates a physically-constrained variational-autoencoder (pcVAE) with different Bayesian Optimization (BO) methods to systematically accelerate and improve crystal structure refinement with resolution of defects. We chose the pyrochlore structured Ho2Ti2O7 as a model system and employed the GSAS2 package for benchmarking crystallographic parameters from Rietveld refinement. However, the function space of these material systems is highly nonlinear, which limits optimizers like traditional Rietveld refinement, into trapping at local minima. Also, these naive methods don't provide an extensive learning about the overall function space, which is essential for large space, large time consuming explorations to identify various potential regions of interest. Thus, we present the approach of exploring the high Dimensional structure parameters of defect sensitive systems via pretrained pcVAE assisted BO and Sparse Axis Aligned BO. The pcVAE projects high-Dimensional diffraction data consisting of thousands of independently measured diffraction orders into a lowD latent space while enforcing scaling invariance and physical relevance. Then via BO methods, we aim to minimize the L2 norm based chisq errors in the real and latent spaces separately between experimental and simulated diffraction patterns, thereby steering the refinement towards potential optimum crystal structure parameters. We investigated and compared the results among different pcVAE assisted BO, non pcVAE assisted BO, and Rietveld refinement.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.00855v1</guid></item><item><title>[cond-mat updates on arXiv.org] A Chemically Grounded Evaluation Framework for Generative Models in Materials Discovery</title><link>https://arxiv.org/abs/2601.00886</link><description>arXiv:2601.00886v1 Announce Type: new 
 Abstract: Generative models hold great promise for accelerating materials discovery, but their evaluation often overlooks the chemical validity and stability requirements crucial to real-world applications. Density Functional Theory (DFT) simulations are the gold standard for evaluating such properties but are computationally intensive and inaccessible to non-experts. We propose a chemically grounded, user-friendly evaluation framework that integrates DFT-based stability analysis with commonly used machine learning (ML) metrics. Through systematic experiments using both perturbative and generative methods, we demonstrate that conventional ML metrics can misrepresent chemical feasibility. To address this, we propose new insights on robust metrics and highlight the importance of simulation-informed evaluation for developing reliable generative models in materials science.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.00886v1</guid></item><item><title>[cond-mat updates on arXiv.org] Disordered Dynamics in High Dimensions: Connections to Random Matrices and Machine Learning</title><link>https://arxiv.org/abs/2601.01010</link><description>arXiv:2601.01010v1 Announce Type: new 
 Abstract: We provide an overview of high dimensional dynamical systems driven by random matrices, focusing on applications to simple models of learning and generalization in machine learning theory. Using both cavity method arguments and path integrals, we review how the behavior of a coupled infinite dimensional system can be characterized as a stochastic process for each single site of the system. We provide a pedagogical treatment of dynamical mean field theory (DMFT), a framework that can be flexibly applied to these settings. The DMFT single site stochastic process is fully characterized by a set of (two-time) correlation and response functions. For linear time-invariant systems, we illustrate connections between random matrix resolvents and the DMFT response. We demonstrate applications of these ideas to machine learning models such as gradient flow, stochastic gradient descent on random feature models and deep linear networks in the feature learning regime trained on random data. We demonstrate how bias and variance decompositions (analysis of ensembling/bagging etc) can be computed by averaging over subsets of the DMFT noise variables. From our formalism we also investigate how linear systems driven with random non-Hermitian matrices (such as random feature models) can exhibit non-monotonic loss curves with training time, while Hermitian matrices with the matching spectra do not, highlighting a different mechanism for non-monotonicity than small eigenvalues causing instability to label noise. Lastly, we provide asymptotic descriptions of the training and test loss dynamics for randomly initialized deep linear neural networks trained in the feature learning regime with high-dimensional random data. In this case, the time translation invariance structure is lost and the hidden layer weights are characterized as spiked random matrices.</description><author>cond-mat updates on arXiv.org</author><pubDate>Tue, 06 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.01010v1</guid></item><item><title>[cond-mat updates on arXiv.org] Predicting Coherent B2 Stability in Ru-Containing Refractory Alloys Through Thermodynamic Elastic Design Maps</title><link>https://arxiv.org/abs/2601.01326</link><description>arXiv:2601.01326v1 Announce Type: new