From db982e01562eaed83f9573cd7e78ab85c5081672 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]" <github-actions[bot]@users.noreply.github.com>
Date: Wed, 7 Jan 2026 06:33:34 +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>Wed, 07 Jan 2026 01:42:41 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] 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 
+<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>Wed, 07 Jan 2026 06:33:34 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] 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 
+Abstract: In computational physics and materials science, first-principles methods, particularly density functional theory, have become central tools for electronic structure prediction and materials design. Recently, rapid advances in artificial intelligence (AI) have begun to reshape the research landscape, giving rise to the emerging field of deep-learning electronic structure calculations. Despite numerous pioneering studies, the field remains in its early stages; existing software implementations are often fragmented, lacking unified frameworks and standardized interfaces required for broad community adoption. Here we present DeepH-pack, a comprehensive and unified software package that integrates first-principles calculations with deep learning. By incorporating fundamental physical principles into neural-network design, such as the nearsightedness principle and the equivariance principle, DeepH-pack achieves robust cross-scale and cross-material generalizability. This allows models trained on small-scale structures to generalize to large-scale and previously unseen materials. The toolkit preserves first-principles accuracy while accelerating electronic structure calculations by several orders of magnitude, establishing an efficient and intelligent computational paradigm for large-scale materials simulation, high-throughput materials database construction, and AI-driven materials 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:2601.02938v1</guid></item><item><title>[cond-mat updates on arXiv.org] Novel fast Li-ion conductors for solid-state electrolytes from first-principles</title><link>https://arxiv.org/abs/2601.03151</link><description>arXiv:2601.03151v1 Announce Type: new 
+Abstract: We present a high-throughput computational screening for fast lithium-ion conductors to identify promising materials for application in all solid-state electrolytes. Starting from more than 30,000 Li-containing experimental structures sourced from Crystallography Open Database, Inorganic Crystal Structure Database and Materials Platform for Data Science, we perform highly automated calculations to identify electronic insulators. On these ~1000 structures, we use molecular dynamics simulations to estimate Li-ion diffusivities using the pinball model, which describes the potential energy landscape of diffusing lithium with accuracy similar to density functional theory while being 200-500 times faster. Then we study the ~60 most promising and previously unknown fast conductors with full first-principles molecular dynamics simulations at several temperatures to estimate their activation barriers. The results are discussed in detail for the 9 fastest conductors, including $Li_7NbO_6$ which shows a remarkable ionic conductivity of ~5 mS/cm at room temperature. We further present the entire screening protocol, including the workflows where the accuracy of the pinball model is improved self-consistently, necessary to automatically running the required calculations and analysing their results.</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:2601.03151v1</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:2601.03207v1 Announce Type: new 
+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>Wed, 07 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.03207v1</guid></item><item><title>[cond-mat updates on arXiv.org] Machine Learning H-theorem</title><link>https://arxiv.org/abs/2508.14003</link><description>arXiv:2508.14003v3 Announce Type: replace 
+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>[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 
@@ -23,7 +33,7 @@ Abstract: Accurate prediction of radiation damage in YBa$_2$Cu$3$O${7-\delta}$ (
 Abstract: We present quantum heat machines using a diatomic molecule modelled by a $q$-deformed potential as a working medium. We analyze the effect of the deformation parameter and other potential parameters on the work output and efficiency of the quantum Otto and quantum Carnot heat cycles. Furthermore, we derive the analytical expressions of work and efficiency as a function of these parameters. Interestingly, our system operates as a quantum heat engine across the range of parameters considered. In addition, the efficiency of the quantum Otto heat engine is seen to be tunable by the deformation parameter. Our findings provide useful insight for understanding the impact of anharmonicity on the design of quantum thermal machines.</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:2504.03131v2</guid></item><item><title>[ChemRxiv] A Systematic Review of Prompt Engineering Paradigms in Organic Chemistry: Mining, Prediction, and Model Architectures</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-625v3?rft_dat=source%3Ddrss</link><description>Large language models (LLMs) have emerged as transformative tools in scientific research, offering a powerful alternative to traditional, resource-intensive machine learning methods. By leveraging the vast knowledge encoded during pre-training, prompt engineering—the systematic design and optimization of input instructions—enables researchers to guide LLMs toward accurate and domain-specific outputs without updating model parameters. This review presents the first systematic examination of prompt engineering techniques within organic chemistry, focusing on two critical application areas: text mining and predictive tasks. We analyze the core paradigms of prompt engineering, including prompt design, prompt learning, and prompt tuning, and clarify terminological inconsistencies in the literature. The discussion is contextualized within the three principal LLM architectures (encoder-only, decoder-only, and encoder-decoder), with an evaluation of their respective performances on chemistry-related tasks.  Furthermore, we explore practical workflows for extracting structured chemical data from texts and knowledge graphs, as well as advanced prompt strategies for reaction condition prediction, reaction optimization, and catalytic performance prediction. This review highlights the significant potential of LLM-driven prompt engineering to accelerate discovery in organic chemistry, from synthetic pathway optimization to automated literature analysis, while also addressing persistent challenges such as the limitations associated with various prompt engineering techniques and the constraints related to each related sub-task. We conclude by outlining future research directions aimed at deepening the integration of chemical knowledge with evolving AI methodologies.</description><author>ChemRxiv</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-625v3?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Ensemble Analyzer: An Open-Source Python Framework for Automated Conformer Ensemble Refinement</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-kq1wk?rft_dat=source%3Ddrss</link><description>Accurate prediction of molecular properties often requires considering the full conformational ensemble rather than a single optimized structure. While modern sampling tools have revolutionized conformational sampling by enabling the rapid generation of ensembles, the subsequent refinement at higher levels of theory remains computationally demanding and technically complex. Existing workflows typically rely on ad hoc scripts and manual intervention, limiting reproducibility and accessibility.
 Here, we present Ensemble Analyzer (EnAn), an open-source Python framework designed to automate the refinement and analysis of conformational ensembles. Built on the Atomic Simulation Environment (ASE), EnAn integrates seamlessly with widely known quantum chemistry engines such as ORCA and Gaussian, providing a modular and extensible architecture that streamlines the entire pipeline. EnAn also supports automated generation and comparison of electronic and vibronic spectra, enabling rapid visualization and interpretation. By minimizing manual data handling and standardizing workflows, EnAn effectively manages reproducible exploration of complex conformational spaces.</description><author>ChemRxiv</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-kq1wk?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Electrostatic Patterning Controls Mineral Nucleation Inside Ferritin</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-8bq1h?rft_dat=source%3Ddrss</link><description>Ferritin protein nanocages store iron across nearly all living organisms.  In mammals, two subtypes of ferritin exist: heavy (H) chain and light (L) chain.  They have very similar 3D structures, but each performs a slightly different role in iron mineral formation.  How sequence differences between the two subtypes affect mineral formation within the nanocages is still unclear.  Single-particle reconstruction of cryo-TEM images was used to build models of unmineralized and partially mineralized human L-chain and H-chain ferritin, which showed that subtle differences in protein structure led to changes in the location of mineral formation within ferritin.  Explicit-solvent atomistic molecular dynamics (MD) simulations were used to explore how sequence-dependent electrostatics modulate ion transport, cluster formation, and mineral nucleation within the confined environment of human L- and H-chain apoferritin nanocages. Employing NaCl as a computational probe, we show that the internal charge distribution governs ion selectivity and nucleation pathways.  Analysis of liquid and solid ionic clusters, combined with Markov State Models (MSMs), reveals that mineralization proceeds through a two-step mechanism involving dense liquid-like precursors that crystallize homogeneously within the cavity.  These findings provide molecular insight into how ferritin sequence variability tunes confinement-driven nucleation and suggest general principles for designing biomimetic nanoreactors.</description><author>ChemRxiv</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-8bq1h?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Machine Learning Prediction of Henry Coefficients of Polar and Nonpolar Gases in Covalent Organic Frameworks: Effects of Interlayer Shifts and Functionalization</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-ps69l?rft_dat=source%3Ddrss</link><description>Covalent organic frameworks (COFs) are promising materials for gas separation and carbon capture. Computational techniques based on Monte Carlo simulation can be used to predict the gas adsorption properties of COFs with high accuracy, however they are too inefficient to be deployed in a high-throughput manner for screening large COF databases. In this paper, we systematically train and evaluate a range of machine learning models for predicting the Henry coefficients for CO2 and CH4 gas adsorption in COF materials. To account for COF structural variability, we train our models on datasets that include both chemically functionalized frameworks and interlayer displaced stacking configurations. By comparing predictive performance across descriptor–model architecture combinations, we demonstrate how different models capture the key physical factors governing gas adsorption, including electrostatics, local atomic environments, and van der Waals interactions. Our results therefore provide a framework for building machine learning models for scalable, high-throughput screening of COF materials with targeted gas adsorption properties.</description><author>ChemRxiv</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-ps69l?rft_dat=source%3Ddrss</guid></item><item><title>[ChemRxiv] Bridging the Gap: Aqueous Phase Organic Synthesis as a Foundation for Advanced Chemical and Biological Discovery</title><link>https://dx.doi.org/10.26434/chemrxiv-2026-0hw4d?rft_dat=source%3Ddrss</link><description>For over a century, synthetic chemistry demanded the rigorous exclusion of water, relying on toxic, volatile organic solvents. This paradigm, while successful, is environmentally and economically costly. This review advocates for a fundamental shift: adopting water not just as a green solvent, but as a transformative medium that reshapes our understanding of reactivity and bridges chemistry with biology. Aqueous phase organic synthesis (APOS) has evolved from accidental observations to a deliberate discipline. Water’s unique properties—its high polarity, hydrogen-bonding capacity, and the hydrophobic effect—make it an active participant. This effect drives reactant aggregation and stabilizes transition states, leading to dramatic rate enhancements in pericyclic and condensation reactions. A broad range of reactions thrive in water, including classical carbon–carbon bond-forming reactions like the aldol and Diels–Alder, and modern cross-couplings (e.g., Suzuki–Miyaura) enabled by water-tolerant catalysts. Multicomponent and click chemistries are particularly powerful. Challenges like poor solubility are addressed with micellar catalysis, water-soluble ligands, and precise control of the reaction microenvironment. Beyond sustainability, APOS drives discovery, often yielding improved selectivities, new pathways, and streamlined syntheses of complex targets like pharmaceuticals. Its greatest promise lies in interfacing with biology. Bioorthogonal reactions, such as azide–alkyne cycloadditions, enable labeling and imaging in living organisms. Aqueous compatibility is essential for in situ therapeutic strategies, chemical biology, and advanced bioconjugation techniques for modifying biomolecules. The future converges with emerging technologies: machine learning to navigate complex aqueous systems, flow chemistry for scalability, and the integration of enzymatic with synthetic catalysis. This points toward a unified chemical-biological engineering paradigm. In conclusion, APOS is a mature, versatile field. It is a cornerstone of green chemistry and a critical bridge to biology, accelerating progress in medicine and materials science. Embracing water is both an environmental imperative and a strategic pathway to the next generation of scientific discovery.
 Introduction: The Solvent Problem in Organic Synthesis
-For generations, organic synthesis has been defined by precise control over molecular structure carried out in rigorously dried environments. Since the emergence of modern organic chemistry in the nineteenth century, water – the very medium that sustains life – has been regarded as an obstacle to chemical transformation. This long-standing assumption has shaped laboratory routines, industrial manufacturing, and chemical education, reinforcing the idea that successful synthesis depends on strict exclusion of moisture. This introduction revisits that historical mindset, evaluates its environmental and economic consequences, and presents the case for a fundamental transition toward aqueous phase organic synthesis (APOS) as a more suitable platform for future chemical and biological innovation.</description><author>ChemRxiv</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-0hw4d?rft_dat=source%3Ddrss</guid></item><item><title>[RSC - Chem. Sci. latest articles] Reactive Fluorescent Probe for Covalent Membrane-Anchoring: Enabling Real-time Imaging of Protein Aggregation Dynamics in Live Cells</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07716H</link><description>&lt;div&gt;&lt;i&gt;&lt;b&gt;Chem. Sci.&lt;/b&gt;&lt;/i&gt;, 2026, Accepted Manuscript&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5SC07716H, Edge Article&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;Hongbei Wei, Liren Xu, Ke Wei, Wenhai Bian, Yifan Wen, Wanyi Yu, Hui Zhang, Tony D. James, Xiaolong Sun&lt;br /&gt;Aberrant aggregation of membrane proteins is a pathological hallmark of various diseases, including neurodegenerative disorders and cancer. The visualization of membrane protein aggregation has emerged as an important approach for...&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Chem. Sci. latest articles</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07716H</guid></item><item><title>[npj Computational Materials] Composition-dependent dislocation mobility in FeNiCrCoCu high-entropy alloys based on atomistic simulations and machine learning analysis</title><link>https://www.nature.com/articles/s41524-025-01942-6</link><description>&lt;p&gt;npj Computational Materials, Published online: 06 January 2026; &lt;a href="https://www.nature.com/articles/s41524-025-01942-6"&gt;doi:10.1038/s41524-025-01942-6&lt;/a&gt;&lt;/p&gt;Composition-dependent dislocation mobility in FeNiCrCoCu high-entropy alloys based on atomistic simulations and machine learning analysis</description><author>npj Computational Materials</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41524-025-01942-6</guid></item><item><title>[npj Computational Materials] AI-assisted rapid crystal structure generation towards a target local environment</title><link>https://www.nature.com/articles/s41524-025-01931-9</link><description>&lt;p&gt;npj Computational Materials, Published online: 06 January 2026; &lt;a href="https://www.nature.com/articles/s41524-025-01931-9"&gt;doi:10.1038/s41524-025-01931-9&lt;/a&gt;&lt;/p&gt;AI-assisted rapid crystal structure generation towards a target local environment</description><author>npj Computational Materials</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41524-025-01931-9</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] Microkinetic modeling of methane activation in Mo/ZSM-5 with machine learning potentials</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725007250?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Catalysis, Volume 454&lt;/p&gt;&lt;p&gt;Author(s): Yanqi Huang, Xiang Ryan Zhou, Brandon C. Bukowski&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Mon, 05 Jan 2026 18:32:11 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725007250</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Thermoplastic Elastomeric Bottlebrush Copolymer Glue Electrolyte Featuring Dual‐Ion Transport Channels and Strong Segregation: Experimental and Simulation Insights (Adv. Funct. Mater. 2/2026)</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.73555?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Mon, 05 Jan 2026 15:13:34 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.73555</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Regulating Metallic Deposition Behavior by Gradient Alloy/Solid Electrolyte Interphase for Durable Na/K Anodes (Adv. Funct. Mater. 2/2026)</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.73556?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Mon, 05 Jan 2026 15:13:34 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.73556</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Autonomous Liquid Metal Droplets Actuated by Ion Diffusion</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202511943?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Mon, 05 Jan 2026 15:13:34 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202511943</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Microcrack‐Structured Visualizable Hydrogel Sensor for Machine Learning‐Assisted Handwriting Recognition</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202512316?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Mon, 05 Jan 2026 15:13:34 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202512316</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Regulating Metallic Deposition Behavior by Gradient Alloy/Solid Electrolyte Interphase for Durable Na/K Anodes</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202515253?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Mon, 05 Jan 2026 15:13:34 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202515253</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Thermoplastic Elastomeric Bottlebrush Copolymer Glue Electrolyte Featuring Dual‐Ion Transport Channels and Strong Segregation: Experimental and Simulation Insights</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202515492?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Mon, 05 Jan 2026 15:13:34 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202515492</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] CGRP Enhances the Regeneration of Bone Defects by Regulating Bone Marrow Mesenchymal Stem Cells Through Promoting ANGPTL4 Secretion by Bone Blood Vessels</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202522295?af=R</link><description>Advanced Science, EarlyView.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Mon, 05 Jan 2026 09:55:39 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202522295</guid></item><item><title>[Wiley: Carbon Energy: Table of Contents] Strategies to Enhance Ionic Conductivity of Na3Zr2Si2O12 Solid Electrolyte for Advanced Solid‐State Sodium Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/cey2.70157?af=R</link><description>Carbon Energy, EarlyView.</description><author>Wiley: Carbon Energy: Table of Contents</author><pubDate>Mon, 05 Jan 2026 07:00:12 GMT</pubDate><guid isPermaLink="true">10.1002/cey2.70157</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Diffusion‐MRI‐Based Estimation of Cortical Architecture via Machine Learning (DECAM) in Primate Brains</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202512752?af=R</link><description>Advanced Science, EarlyView.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Mon, 05 Jan 2026 05:33:28 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202512752</guid></item><item><title>[cond-mat updates on arXiv.org] Automated electrostatic characterization of quantum dot devices in single- and bilayer heterostructures</title><link>https://arxiv.org/abs/2601.00067</link><description>arXiv:2601.00067v1 Announce Type: new 
+For generations, organic synthesis has been defined by precise control over molecular structure carried out in rigorously dried environments. Since the emergence of modern organic chemistry in the nineteenth century, water – the very medium that sustains life – has been regarded as an obstacle to chemical transformation. This long-standing assumption has shaped laboratory routines, industrial manufacturing, and chemical education, reinforcing the idea that successful synthesis depends on strict exclusion of moisture. This introduction revisits that historical mindset, evaluates its environmental and economic consequences, and presents the case for a fundamental transition toward aqueous phase organic synthesis (APOS) as a more suitable platform for future chemical and biological innovation.</description><author>ChemRxiv</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://dx.doi.org/10.26434/chemrxiv-2026-0hw4d?rft_dat=source%3Ddrss</guid></item><item><title>[RSC - Chem. Sci. latest articles] Reactive Fluorescent Probe for Covalent Membrane-Anchoring: Enabling Real-time Imaging of Protein Aggregation Dynamics in Live Cells</title><link>http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07716H</link><description>&lt;div&gt;&lt;i&gt;&lt;b&gt;Chem. Sci.&lt;/b&gt;&lt;/i&gt;, 2026, Accepted Manuscript&lt;br /&gt;&lt;b&gt;DOI&lt;/b&gt;: 10.1039/D5SC07716H, Edge Article&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;Hongbei Wei, Liren Xu, Ke Wei, Wenhai Bian, Yifan Wen, Wanyi Yu, Hui Zhang, Tony D. James, Xiaolong Sun&lt;br /&gt;Aberrant aggregation of membrane proteins is a pathological hallmark of various diseases, including neurodegenerative disorders and cancer. The visualization of membrane protein aggregation has emerged as an important approach for...&lt;br /&gt;The content of this RSS Feed (c) The Royal Society of Chemistry&lt;/div&gt;</description><author>RSC - Chem. Sci. latest articles</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">http://pubs.rsc.org/en/Content/ArticleLanding/2026/SC/D5SC07716H</guid></item><item><title>[npj Computational Materials] Composition-dependent dislocation mobility in FeNiCrCoCu high-entropy alloys based on atomistic simulations and machine learning analysis</title><link>https://www.nature.com/articles/s41524-025-01942-6</link><description>&lt;p&gt;npj Computational Materials, Published online: 06 January 2026; &lt;a href="https://www.nature.com/articles/s41524-025-01942-6"&gt;doi:10.1038/s41524-025-01942-6&lt;/a&gt;&lt;/p&gt;Composition-dependent dislocation mobility in FeNiCrCoCu high-entropy alloys based on atomistic simulations and machine learning analysis</description><author>npj Computational Materials</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41524-025-01942-6</guid></item><item><title>[npj Computational Materials] AI-assisted rapid crystal structure generation towards a target local environment</title><link>https://www.nature.com/articles/s41524-025-01931-9</link><description>&lt;p&gt;npj Computational Materials, Published online: 06 January 2026; &lt;a href="https://www.nature.com/articles/s41524-025-01931-9"&gt;doi:10.1038/s41524-025-01931-9&lt;/a&gt;&lt;/p&gt;AI-assisted rapid crystal structure generation towards a target local environment</description><author>npj Computational Materials</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://www.nature.com/articles/s41524-025-01931-9</guid></item><item><title>[Applied Physics Letters Current Issue] Polarization-controlled multistate thermal conductivity in ferroelectric HfO 2  thin films</title><link>https://pubs.aip.org/aip/apl/article/128/1/012902/3376407/Polarization-controlled-multistate-thermal</link><description>&lt;span class="paragraphSection"&gt;While nanoscale electronic logic circuits are well established, the development of nanoscale thermal logic circuits has been slow, mainly due to the absence of efficient and controllable nonvolatile field-effect thermal transistors. In this study, we investigate polarization-dependent thermal conductivity in ferroelectric orthorhombic hafnium dioxide (o-HfO&lt;sub&gt;2&lt;/sub&gt;) thin films. Using molecular dynamics simulations with machine learning potentials, we show that a 24-nm-long o-HfO&lt;sub&gt;2&lt;/sub&gt; film can exhibit four distinct and stable thermal conductivity states arising from different ferroelectric polarization configurations. Notably, these states achieve a maximum switching ratio of 160.8% under 2% tensile strain. Our results suggest a practical pathway toward nonvolatile field-effect thermal transistors.&lt;/span&gt;</description><author>Applied Physics Letters Current Issue</author><pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate><guid isPermaLink="true">https://pubs.aip.org/aip/apl/article/128/1/012902/3376407/Polarization-controlled-multistate-thermal</guid></item><item><title>[ScienceDirect Publication: Journal of Catalysis] Microkinetic modeling of methane activation in Mo/ZSM-5 with machine learning potentials</title><link>https://www.sciencedirect.com/science/article/pii/S0021951725007250?dgcid=rss_sd_all</link><description>&lt;p&gt;Publication date: February 2026&lt;/p&gt;&lt;p&gt;&lt;b&gt;Source:&lt;/b&gt; Journal of Catalysis, Volume 454&lt;/p&gt;&lt;p&gt;Author(s): Yanqi Huang, Xiang Ryan Zhou, Brandon C. Bukowski&lt;/p&gt;</description><author>ScienceDirect Publication: Journal of Catalysis</author><pubDate>Mon, 05 Jan 2026 18:32:11 GMT</pubDate><guid isPermaLink="true">https://www.sciencedirect.com/science/article/pii/S0021951725007250</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Thermoplastic Elastomeric Bottlebrush Copolymer Glue Electrolyte Featuring Dual‐Ion Transport Channels and Strong Segregation: Experimental and Simulation Insights (Adv. Funct. Mater. 2/2026)</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.73555?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Mon, 05 Jan 2026 15:13:34 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.73555</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Regulating Metallic Deposition Behavior by Gradient Alloy/Solid Electrolyte Interphase for Durable Na/K Anodes (Adv. Funct. Mater. 2/2026)</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.73556?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Mon, 05 Jan 2026 15:13:34 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.73556</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Autonomous Liquid Metal Droplets Actuated by Ion Diffusion</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202511943?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Mon, 05 Jan 2026 15:13:34 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202511943</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Microcrack‐Structured Visualizable Hydrogel Sensor for Machine Learning‐Assisted Handwriting Recognition</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202512316?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Mon, 05 Jan 2026 15:13:34 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202512316</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Regulating Metallic Deposition Behavior by Gradient Alloy/Solid Electrolyte Interphase for Durable Na/K Anodes</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202515253?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Mon, 05 Jan 2026 15:13:34 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202515253</guid></item><item><title>[Wiley: Advanced Functional Materials: Table of Contents] Thermoplastic Elastomeric Bottlebrush Copolymer Glue Electrolyte Featuring Dual‐Ion Transport Channels and Strong Segregation: Experimental and Simulation Insights</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/adfm.202515492?af=R</link><description>Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.</description><author>Wiley: Advanced Functional Materials: Table of Contents</author><pubDate>Mon, 05 Jan 2026 15:13:34 GMT</pubDate><guid isPermaLink="true">10.1002/adfm.202515492</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] CGRP Enhances the Regeneration of Bone Defects by Regulating Bone Marrow Mesenchymal Stem Cells Through Promoting ANGPTL4 Secretion by Bone Blood Vessels</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202522295?af=R</link><description>Advanced Science, EarlyView.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Mon, 05 Jan 2026 09:55:39 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202522295</guid></item><item><title>[Wiley: Carbon Energy: Table of Contents] Strategies to Enhance Ionic Conductivity of Na3Zr2Si2O12 Solid Electrolyte for Advanced Solid‐State Sodium Batteries</title><link>https://onlinelibrary.wiley.com/doi/10.1002/cey2.70157?af=R</link><description>Carbon Energy, EarlyView.</description><author>Wiley: Carbon Energy: Table of Contents</author><pubDate>Mon, 05 Jan 2026 07:00:12 GMT</pubDate><guid isPermaLink="true">10.1002/cey2.70157</guid></item><item><title>[Wiley: Advanced Science: Table of Contents] Diffusion‐MRI‐Based Estimation of Cortical Architecture via Machine Learning (DECAM) in Primate Brains</title><link>https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.202512752?af=R</link><description>Advanced Science, EarlyView.</description><author>Wiley: Advanced Science: Table of Contents</author><pubDate>Mon, 05 Jan 2026 05:33:28 GMT</pubDate><guid isPermaLink="true">10.1002/advs.202512752</guid></item><item><title>[cond-mat updates on arXiv.org] Automated electrostatic characterization of quantum dot devices in single- and bilayer heterostructures</title><link>https://arxiv.org/abs/2601.00067</link><description>arXiv:2601.00067v1 Announce Type: new 
 Abstract: As quantum dot (QD)-based spin qubits advance toward larger, more complex device architectures, rapid, automated device characterization and data analysis tools become critical. The orientation and spacing of transition lines in a charge stability diagram (CSD) contain a fingerprint of a QD device's capacitive environment, making these measurements useful tools for device characterization. However, manually interpreting these features is time-consuming, error-prone, and impractical at scale. Here, we present an automated protocol for extracting underlying capacitive properties from CSDs. Our method integrates machine learning, image processing, and object detection to identify and track charge transitions across large datasets without manual labeling. We demonstrate this method using experimentally measured data from a strained-germanium single-quantum-well (planar) and a strained-germanium double-quantum-well (bilayer) QD device. Unlike for planar QD devices, CSDs in bilayer germanium heterostructure exhibit a larger set of transitions, including interlayer tunneling and distinct loading lines for the vertically stacked QDs, making them a powerful testbed for automation methods. By analyzing the properties of many CSDs, we can statistically estimate physically relevant quantities, like relative lever arms and capacitive couplings. Thus, our protocol enables rapid extraction of useful, nontrivial information about QD devices.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 05 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.00067v1</guid></item><item><title>[cond-mat updates on arXiv.org] Atomic-Scale Mechanisms of Li-Ion Transport Mediated by Li10GeP2S12 in Composite Solid Polyethylene Oxide Electrolytes</title><link>https://arxiv.org/abs/2601.00112</link><description>arXiv:2601.00112v1 Announce Type: new 
 Abstract: Polymer electrolytes incorporating Li$_{10}$GeP$_{2}$S$_{12}$ (LGPS) nanoparticles show promise for solid-state lithium batteries owing to their enhanced ionic conductivity, though the governing mechanisms remain unclear. We combine molecular dynamics (MD) simulations, experimental ionic conductivity measurements, and density functional theory (DFT) calculations to elucidate the effect of LGPS loading on polyethylene oxide (PEO) structure and Li-ion transport. MD and experimental results agree up to 10\% LGPS, showing a volcano-shaped conductivity trend driven by polymer segmental dynamics and interfacial effects. Beyond 10\%, experiments reveal additional conductivity enhancement unexplained by MD, suggesting a distinct transport regime. DFT calculations indicate that Li-ion migration at the PEO|LGPS interface proceeds via vacancy-mediated hopping, with low barriers favored by S-rich interfacial sites and hindered by Ge. These findings link interfacial chemistry and microstructure to Li-ion dynamics, offering guidelines for designing high-performance composite polymer electrolytes.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 05 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.00112v1</guid></item><item><title>[cond-mat updates on arXiv.org] Engineering Ideal 2D Type-II Nodal Line Semimetals via Stacking and Intercalation of van der Waals Layers</title><link>https://arxiv.org/abs/2601.00407</link><description>arXiv:2601.00407v1 Announce Type: new 
 Abstract: Two-dimensional type-II topological semimetals (TSMs), characterized by strongly tilted Dirac cones, have attracted intense interest for their unconventional electronic properties and exotic transport behaviors. However, rational design remains challenging due to the sensitivity of band tilting to lattice geometry, atomic coordination, and symmetry constraints. Here, we present a bottom-up approach to engineer ideal type-II nodal line semimetals (NLSMs) in van der Waals bilayers via atomic intercalation. Using monolayer $h$-AlN as a prototype, we show that fluorine-intercalated bilayer AlN (F@BL-AlN) hosts a symmetry-protected type-II nodal loop precisely at the Fermi level, enabled by preserved mirror symmetry ($\mathcal{M}_z$) and tailored interlayer hybridization. First-principles calculations reveal that fluorine not only tunes interlayer coupling but also aligns the Fermi energy with the nodal line, stabilizing the type-II NLSM phase. The system exhibits tunable electronic properties under external electric and strain fields and features a van Hove singularity that induces spontaneous ferromagnetism, realizing a ferromagnetic topological semimetal state. This work provides a versatile platform for designing type-II NLSMs and offers practical guidance for their experimental realization.</description><author>cond-mat updates on arXiv.org</author><pubDate>Mon, 05 Jan 2026 05:00:00 GMT</pubDate><guid isPermaLink="true">oai:arXiv.org:2601.00407v1</guid></item><item><title>[cond-mat updates on arXiv.org] Kinetic Turing Instability and Emergent Spectral Scaling in Chiral Active Turbulence</title><link>https://arxiv.org/abs/2508.21012</link><description>arXiv:2508.21012v5 Announce Type: cross