My Customized Papers

[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

Wiley: Angewandte Chemie International Edition: Table of Contents — Wed, 07 Jan 2026 05:23:27 GMT

Angewandte Chemie International Edition, EarlyView.

[Wiley: ENERGY & 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

Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents — Wed, 07 Jan 2026 05:20:14 GMT

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.

[Wiley: Advanced Materials: Table of Contents] Customizing Ion Transport by Anionphilic Nanofiber‐Polymer Electrolyte for Stable Zinc Metal Batteries

Wiley: Advanced Materials: Table of Contents — Wed, 07 Jan 2026 05:17:00 GMT

Advanced Materials, EarlyView.

[cond-mat updates on arXiv.org] A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse design

ScienceDirect Publication: Journal of Energy Storage — Wed, 07 Jan 2026 18:32:48 GMT

arXiv:2601.02424v1 Announce Type: new +My Customized Papershttps://github.com/your_username/your_repoAggregated research papers

[ScienceDirect Publication: Progress in Materials Science] Advanced simulations from DFT to machine learning for solid-state hydrogen storage: fundamentals, progresses, challenges and perspectives

ScienceDirect Publication: Progress in Materials Science — Wed, 07 Jan 2026 18:32:44 GMT

Publication date: Available online 6 January 2026

Source: Progress in Materials Science

Author(s): Shuling Chen, Mei Yang, Shaoyang Shen, Liuzhang Ouyang

[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

Wiley: Angewandte Chemie International Edition: Table of Contents — Wed, 07 Jan 2026 05:23:27 GMT

Angewandte Chemie International Edition, EarlyView.

[Wiley: ENERGY & 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

Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents — Wed, 07 Jan 2026 05:20:14 GMT

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.

[Wiley: Advanced Materials: Table of Contents] Customizing Ion Transport by Anionphilic Nanofiber‐Polymer Electrolyte for Stable Zinc Metal Batteries

Wiley: Advanced Materials: Table of Contents — Wed, 07 Jan 2026 05:17:00 GMT

Advanced Materials, EarlyView.

[cond-mat updates on arXiv.org] A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse design

cond-mat updates on arXiv.org — Wed, 07 Jan 2026 05:00:00 GMT

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.

[cond-mat updates on arXiv.org] Protein-Water Energy Transfer via Anharmonic Low-Frequency Vibrations

cond-mat updates on arXiv.org — Wed, 07 Jan 2026 05:00:00 GMT

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.

[cond-mat updates on arXiv.org] Interplay of Structure and Dynamics in Solid Polymer Electrolytes: a Molecular Dynamics Study of LiPF6/polypropylene carbonate

cond-mat updates on arXiv.org — Wed, 07 Jan 2026 05:00:00 GMT

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.

[cond-mat updates on arXiv.org] DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations

cond-mat updates on arXiv.org — Wed, 07 Jan 2026 05:00:00 GMT

arXiv:2601.02938v1 Announce Type: new @@ -9,7 +9,7 @@ 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.

[cond-mat updates on arXiv.org] Bloch oscillations of helicoidal spin-orbit coupled Bose-Einstein condensates in deep optical lattices

cond-mat updates on arXiv.org — Wed, 07 Jan 2026 05:00:00 GMT

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.

[cond-mat updates on arXiv.org] Tuning Separator Chemistry: Improving Zn Anode Compatibility via Functionalized Chitin Nanofibers

cond-mat updates on arXiv.org — Wed, 07 Jan 2026 05:00:00 GMT

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.

[cond-mat updates on arXiv.org] Unsupervised Topological Phase Discovery in Periodically Driven Systems via Floquet-Bloch State

cond-mat updates on arXiv.org — Wed, 07 Jan 2026 05:00:00 GMT

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.

[Nature Communications] Thermotropic liquid-assisted interface management enables efficient and stable perovskite solar cells and modules

Nature Communications — Wed, 07 Jan 2026 00:00:00 GMT

Nature Communications, Published online: 07 January 2026; doi:10.1038/s41467-025-68231-0

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.

[ChemRxiv] A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data

ChemRxiv — Wed, 07 Jan 2026 00:00:00 GMT

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.

[Nature Communications] Uncertainty-aware machine learning to predict non-cancer human toxicity for the global chemicals market

Nature Communications — Wed, 07 Jan 2026 00:00:00 GMT

Nature Communications, Published online: 07 January 2026; doi:10.1038/s41467-025-67374-4

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.

[ChemRxiv] Learning EXAFS from atomic structure through physics-informed machine learning

ChemRxiv — Wed, 07 Jan 2026 00:00:00 GMT

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.

[ChemRxiv] Defined by Shape: Elucidating the Molecular Recognition of Dynamic Loops with Covalent Ligands

ChemRxiv — Wed, 07 Jan 2026 00:00:00 GMT

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.

[ChemRxiv] QuantumPDB: A Workflow for High-Throughput Quantum Cluster Model Generation from Protein Structures

ChemRxiv — Wed, 07 Jan 2026 00:00:00 GMT

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.

[ChemRxiv] Generalization and Usability of Co-Folded GPCR–Ligand Complexes: A Physics-Guided Assessment

ChemRxiv — Wed, 07 Jan 2026 00:00:00 GMT

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.

[Communications Physics] Interpolation-based coordinate descent method for parameterized quantum circuits

Communications Physics — Wed, 07 Jan 2026 00:00:00 GMT

Communications Physics, Published online: 07 January 2026; doi:10.1038/s42005-025-02473-8

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.

[ScienceDirect Publication: Journal of Energy Storage] Optimizing solid electrolyte interphase with KOTF for dendrites-free and high-performance Lithium Metal Batteries

ScienceDirect Publication: Journal of Energy Storage — Tue, 06 Jan 2026 18:31:06 GMT

Publication date: 20 February 2026

Source: Journal of Energy Storage, Volume 147

Author(s): Yangtao Zhou, Dequan Huang, Man Zhang, Guangda Yin, Yi Liang, Qichang Pan, Fenghua Zheng, Sijiang Hu, Hongqiang Wang, Qingyu Li

[ScienceDirect Publication: Journal of Energy Storage] A hierarchical sandwich Li_6.4Ga_0.2La₃Zr₂O₁₂/ZIF-8@SiO₂/PVDF-HFP heterostructure with high ionic conductivity for dendrite-free solid-state lithium batteries

ScienceDirect Publication: Journal of Energy Storage — Tue, 06 Jan 2026 18:31:06 GMT

Publication date: 20 February 2026

Source: Journal of Energy Storage, Volume 147

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

[ScienceDirect Publication: Journal of Energy Storage] Hierarchical rose-like VS₂ with sulfur vacancies for high-performance all-solid-state lithium-ion batteries

ScienceDirect Publication: Journal of Energy Storage — Tue, 06 Jan 2026 18:31:06 GMT

Publication date: 20 February 2026

Source: Journal of Energy Storage, Volume 147

Author(s): Peidian Chong, Shijie Yu, Lin Zheng, Lei Zhang, Mingdeng Wei, Hongfei Liu, Yi Ren, Jianbiao Wang

[ScienceDirect Publication: Journal of Energy Storage] Prediction of Lithium-ion battery states via combination of implantable sensors and machine learning

ScienceDirect Publication: Journal of Energy Storage — Tue, 06 Jan 2026 18:31:06 GMT

Publication date: 20 February 2026

Source: Journal of Energy Storage, Volume 147

Author(s): Zijun Huang, Feng Tong, Guo Chen, Xuan Chen, Xianjie Xu, Zhefu Mu, Jiaxin Sun, Sheng Huang, Xiuquan Gu

[ScienceDirect Publication: Journal of Energy Storage] A review on metal–organic framework-based polymer solid-state electrolytes for energy storage

ScienceDirect Publication: Journal of Energy Storage — Tue, 06 Jan 2026 18:31:06 GMT

Publication date: 20 February 2026

Source: Journal of Energy Storage, Volume 147

Author(s): Zelong Zhuang, Xiaojin Yang, Jie Cui, Jingwei Liu, Xueming Yang

[ScienceDirect Publication: Computational Materials Science] Descriptor and graph-based molecular representations in prediction of copolymer properties using machine learning

ScienceDirect Publication: Computational Materials Science — Tue, 06 Jan 2026 12:43:08 GMT

Publication date: October 2026

Source: Computational Materials Science, Volume 264

Author(s): Elaheh Kazemi-Khasragh, Rocío Mercado, Carlos Gonzalez, Maciej Haranczyk

[Recent Articles in Phys. Rev. B] Signatures of coherent phonon transport in frequency-dependent lattice thermal conductivity

Recent Articles in Phys. Rev. B — Tue, 06 Jan 2026 10:00:00 GMT

Author(s): Đorđe Dangić

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…

[Phys. Rev. B 113, 024301] Published Tue Jan 06, 2026

[Wiley: Advanced Energy Materials: Table of Contents] Accelerating the Discovery of High‐Conductivity Glass Electrolytes via Machine Learning

Wiley: Advanced Energy Materials: Table of Contents — Tue, 06 Jan 2026 05:35:12 GMT

Advanced Energy Materials, EarlyView.

[cond-mat updates on arXiv.org] Physically-Constrained Autoencoder-Assisted Bayesian Optimization for Refinement of High-Dimensional Defect-Sensitive Single Crystalline Structure

cond-mat updates on arXiv.org — Wed, 07 Jan 2026 05:00:00 GMT

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.

[Nature Communications] Thermotropic liquid-assisted interface management enables efficient and stable perovskite solar cells and modules

Nature Communications — Wed, 07 Jan 2026 00:00:00 GMT

Nature Communications, Published online: 07 January 2026; doi:10.1038/s41467-025-68231-0

[ChemRxiv] A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data

ChemRxiv — Wed, 07 Jan 2026 00:00:00 GMT

[Nature Communications] Uncertainty-aware machine learning to predict non-cancer human toxicity for the global chemicals market

Nature Communications — Wed, 07 Jan 2026 00:00:00 GMT

Nature Communications, Published online: 07 January 2026; doi:10.1038/s41467-025-67374-4

[ChemRxiv] Learning EXAFS from atomic structure through physics-informed machine learning

ChemRxiv — Wed, 07 Jan 2026 00:00:00 GMT

[ChemRxiv] Defined by Shape: Elucidating the Molecular Recognition of Dynamic Loops with Covalent Ligands

ChemRxiv — Wed, 07 Jan 2026 00:00:00 GMT

[ChemRxiv] QuantumPDB: A Workflow for High-Throughput Quantum Cluster Model Generation from Protein Structures

ChemRxiv — Wed, 07 Jan 2026 00:00:00 GMT

[ChemRxiv] Generalization and Usability of Co-Folded GPCR–Ligand Complexes: A Physics-Guided Assessment

ChemRxiv — Wed, 07 Jan 2026 00:00:00 GMT

[Communications Physics] Interpolation-based coordinate descent method for parameterized quantum circuits

Communications Physics — Wed, 07 Jan 2026 00:00:00 GMT

Communications Physics, Published online: 07 January 2026; doi:10.1038/s42005-025-02473-8

[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

RSC - Digital Discovery latest articles — Wed, 07 Jan 2026 00:00:00 GMT

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00456J, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Weiling Wang, Isabel Cooley, Morgan R. Alexander, Ricky D. Wildman, Anna K. Croft, Blair F. Johnston
Machine learning pipeline integrates COSMO-RS and multiple molecular descriptors to predict and interpret solubility across diverse solute–solvent systems.
To cite this article before page numbers are assigned, use the DOI form of citation above.
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[ChemRxiv] ConfDENSE: A conformer aware electron density based machine learning paradigm for navigating the odorant landscape

ChemRxiv — Wed, 07 Jan 2026 00:00:00 GMT

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.

[AI for Science - latest papers] MOSES: combining automated ontology construction with a multi-agent system for explainable chemical knowledge reasoning

AI for Science - latest papers — Wed, 07 Jan 2026 00:00:00 GMT

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.

[Cell Reports Physical Science] A review of advancements and challenges in nanoplastics detection

Cell Reports Physical Science — Wed, 07 Jan 2026 00:00:00 GMT

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.

[ScienceDirect Publication: Journal of Energy Storage] Optimizing solid electrolyte interphase with KOTF for dendrites-free and high-performance Lithium Metal Batteries

ScienceDirect Publication: Journal of Energy Storage — Tue, 06 Jan 2026 18:31:06 GMT

Publication date: 20 February 2026

Source: Journal of Energy Storage, Volume 147

Author(s): Yangtao Zhou, Dequan Huang, Man Zhang, Guangda Yin, Yi Liang, Qichang Pan, Fenghua Zheng, Sijiang Hu, Hongqiang Wang, Qingyu Li

[ScienceDirect Publication: Journal of Energy Storage] A hierarchical sandwich Li_6.4Ga_0.2La₃Zr₂O₁₂/ZIF-8@SiO₂/PVDF-HFP heterostructure with high ionic conductivity for dendrite-free solid-state lithium batteries

ScienceDirect Publication: Journal of Energy Storage — Tue, 06 Jan 2026 18:31:06 GMT

Publication date: 20 February 2026

Source: Journal of Energy Storage, Volume 147

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

[ScienceDirect Publication: Journal of Energy Storage] Hierarchical rose-like VS₂ with sulfur vacancies for high-performance all-solid-state lithium-ion batteries

ScienceDirect Publication: Journal of Energy Storage — Tue, 06 Jan 2026 18:31:06 GMT

Publication date: 20 February 2026

Source: Journal of Energy Storage, Volume 147

Author(s): Peidian Chong, Shijie Yu, Lin Zheng, Lei Zhang, Mingdeng Wei, Hongfei Liu, Yi Ren, Jianbiao Wang

[ScienceDirect Publication: Journal of Energy Storage] Prediction of Lithium-ion battery states via combination of implantable sensors and machine learning

ScienceDirect Publication: Journal of Energy Storage — Tue, 06 Jan 2026 18:31:06 GMT

Publication date: 20 February 2026

Source: Journal of Energy Storage, Volume 147

Author(s): Zijun Huang, Feng Tong, Guo Chen, Xuan Chen, Xianjie Xu, Zhefu Mu, Jiaxin Sun, Sheng Huang, Xiuquan Gu

[ScienceDirect Publication: Journal of Energy Storage] A review on metal–organic framework-based polymer solid-state electrolytes for energy storage

ScienceDirect Publication: Journal of Energy Storage — Tue, 06 Jan 2026 18:31:06 GMT

Publication date: 20 February 2026

Source: Journal of Energy Storage, Volume 147

Author(s): Zelong Zhuang, Xiaojin Yang, Jie Cui, Jingwei Liu, Xueming Yang

[ScienceDirect Publication: Computational Materials Science] Descriptor and graph-based molecular representations in prediction of copolymer properties using machine learning

ScienceDirect Publication: Computational Materials Science — Tue, 06 Jan 2026 12:43:08 GMT

Publication date: October 2026

Source: Computational Materials Science, Volume 264

Author(s): Elaheh Kazemi-Khasragh, Rocío Mercado, Carlos Gonzalez, Maciej Haranczyk

[Recent Articles in Phys. Rev. B] Signatures of coherent phonon transport in frequency-dependent lattice thermal conductivity

Recent Articles in Phys. Rev. B — Tue, 06 Jan 2026 10:00:00 GMT

Author(s): Đorđe Dangić

[Phys. Rev. B 113, 024301] Published Tue Jan 06, 2026

[Wiley: Advanced Energy Materials: Table of Contents] Accelerating the Discovery of High‐Conductivity Glass Electrolytes via Machine Learning

Wiley: Advanced Energy Materials: Table of Contents — Tue, 06 Jan 2026 05:35:12 GMT

Advanced Energy Materials, EarlyView.

[cond-mat updates on arXiv.org] Physically-Constrained Autoencoder-Assisted Bayesian Optimization for Refinement of High-Dimensional Defect-Sensitive Single Crystalline Structure

cond-mat updates on arXiv.org — Tue, 06 Jan 2026 05:00:00 GMT

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.

[cond-mat updates on arXiv.org] A Chemically Grounded Evaluation Framework for Generative Models in Materials Discovery

cond-mat updates on arXiv.org — Tue, 06 Jan 2026 05:00:00 GMT

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.

[cond-mat updates on arXiv.org] Disordered Dynamics in High Dimensions: Connections to Random Matrices and Machine Learning

cond-mat updates on arXiv.org — Tue, 06 Jan 2026 05:00:00 GMT

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.

[cond-mat updates on arXiv.org] Predicting Coherent B2 Stability in Ru-Containing Refractory Alloys Through Thermodynamic Elastic Design Maps

ChemRxiv — Wed, 31 Dec 2025 00:00:00 GMT

arXiv:2601.01326v1 Announce Type: new @@ -94,4 +94,4 @@ efficiency. We highlight our work in large-scale molecular dynamics using ANI po benchmark results for water boxes (up to 100 million atoms) and a solvated HIV capsid (44 million atoms). We also present results for accurately simulating complex reaction processes at unprecedented scales, such as methane combustion (300 thousand atoms) and early Earth chemistry experiment (228 -thousand atoms) demonstrating the spontaneous formation of glycine.

[Cell Reports Physical Science] Hierarchical optimization of perovskite solar cell fabrication via step-by-step machine learning

Cell Reports Physical Science — Wed, 31 Dec 2025 00:00:00 GMT

Pu et al. report a hierarchical multi-target Bayesian optimization (MTBO) framework that optimizes the electrospray deposition process for perovskite solar cells. By integrating adaptive constraints and prioritizing thin-film quality across multiple fabrication stages, MTBO efficiently identifies feasible, high-performance conditions, enabling 1.63 eV FA0.82Cs0.18Pb(I0.86Br0.11Cl0.03)3 devices with a champion efficiency of 21.95%.

[ACS Materials Letters: Latest Articles (ACS Publications)] [ASAP] Machine Learning Applications in Predicting Friction Properties of Bearing Steel: A Review

ACS Materials Letters: Latest Articles (ACS Publications) — Tue, 30 Dec 2025 19:59:57 GMT

ACS Materials Letters

DOI: 10.1021/acsmaterialslett.5c01047

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Machine Learning-Guided Discovery of Sterically Protected High Triplet Exciplex Hosts for Ultra-Bright Green OLEDs

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Tue, 30 Dec 2025 19:03:11 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c16369

[Wiley: Small Methods: Table of Contents] Standardization and Machine Learning Prediction of Tafel Slope of Pt‐Based Nanocatalysts for High‐Performance HER Catalyst Development

Wiley: Small Methods: Table of Contents — Tue, 30 Dec 2025 12:06:41 GMT

Small Methods, EarlyView.

[npj Computational Materials] Toward high entropy material discovery for energy applications using computational and machine learning methods

npj Computational Materials — Tue, 30 Dec 2025 00:00:00 GMT

npj Computational Materials, Published online: 30 December 2025; doi:10.1038/s41524-025-01918-6

Toward high entropy material discovery for energy applications using computational and machine learning methods

[APL Machine Learning Current Issue] AI agents for photonic integrated circuit design automation

APL Machine Learning Current Issue — Tue, 30 Dec 2025 00:00:00 GMT

We present photonics intelligent design and optimization, a proof-of-concept multi-agent framework that converts natural-language photonic integrated circuit (PIC) design requests into layout mask files. This work demonstrates end-to-end PIC design automation using large language models (LLMs), with the goal of achieving structurally valid rather than performance-qualified layouts. We compare seven reasoning LLMs using a testbench of 102 design descriptions that ranged from single devices to 112-component PICs. The success rate for single-device designs was up to 91%. For design queries with ≤15 components, o1, Gemini-2.5-pro, and Claude Opus 4 achieved the highest end-to-end pass@5 success rates of ∼57%, with Gemini-2.5-pro requiring the fewest output tokens and the lowest cost. Future work will extend this framework toward performance qualification through expanded datasets, tighter simulation and optimization loops, and fabrication feedback integration.

[Applied Physics Letters Current Issue] Rattling-induced anharmonicity and multi-valley enhanced thermoelectric performance in layered SmZnSbO material

Applied Physics Letters Current Issue — Tue, 30 Dec 2025 00:00:00 GMT

Layered rare-earth oxides have become promising candidates for high-performance thermoelectric (TE) materials on account of the distinctive electronic structures and anisotropic transport properties. In this work, the phonon dynamics, carrier transport, and TE performance of the layered SmZnSbO compound are comprehensively evaluated using first-principles calculations, machine learning interatomic potentials, Boltzmann transport theory, and the two-channel model. The coexistence of weak interlayer van der Waals interactions, robust intralayer covalent bonding interactions, and rattling-like vibrations of Zn atoms synergistically induces significant lattice anharmonicity, resulting in a decreased lattice thermal conductivity (0.84 W/mK@900 K within the framework of the two-channel model) for the SmZnSbO compound. The natural quantum well architecture formed by the alternative conductive [Zn₂Sb₂]²⁻ layer and the insulated [Sm₂O₂]²⁺ layer endows quasi-two-dimensional transport characteristics, enabling a high carrier mobility of 34.1 cm²/Vs. Moreover, the multi-valley electronic band structure with an indirect bandgap of 0.80 eV simultaneously optimizes electrical conductivity (σ) and Seebeck coefficient (S), resulting in an enhanced power factor. Benefiting from these synergistic features, the layered SmZnSbO compound achieves optimal dimensionless figures of merit (ZTs) of 1.47 and 1.40 for the p-type and n-type doping circumstances at 900 K. The current work not only elucidates the thermal and electronic transport mechanisms for the SmZnSbO compound but also establishes a paradigm for designing high-efficiency layered oxide TE materials through combined strategies of quantum confinement, phonon engineering, and multi-valley band convergence.

[Applied Physics Letters Current Issue] Magneto-ionic control of perpendicular anisotropy in epitaxial Mn 4 N films

Applied Physics Letters Current Issue — Tue, 30 Dec 2025 00:00:00 GMT

We report reversible control of the magnetism and perpendicular magnetic anisotropy (PMA) in Mn₄N thin films through solid-state magneto-ionic gating. We grow Mn₄N on MgO(100) substrates, exhibiting bulk-like magnetization and strain-induced PMA, also promoted by capping the film with material with large spin–orbit coupling. We demonstrate that the interfacial anisotropy can be reversibly tuned through voltage-driven nitrogen ion migration when Mn₄N is in contact with a nitrogen-affine metal, such as Ta and V. We also show that solid-state gating effectively enhances the spin–orbit torque switching efficiency by reducing the coercive field without compromising the interface transparency. Finally, we demonstrate that gate-tunable devices can be harnessed for efficient nonvolatile memory functionality.

[Applied Physics Letters Current Issue] Predicting anode coatings for solid-state lithium metal batteries via first-principles thermodynamic calculations and hierarchical ion-transport algorithms

Applied Physics Letters Current Issue — Tue, 30 Dec 2025 00:00:00 GMT

Solid-state lithium metal batteries (SSLMBs) are promising for next-generation energy storage devices due to their superior energy density and excellent safety. Among solid-state electrolytes, garnet-type Li₇La₃Zr₂O₁₂ (LLZO) exhibits a wide electrochemical window and high lithium-ion conductivity, but poor electrode contact and Li dendrite growth restrict its practical application. To address these challenges, this study explores the application of thin film coatings composed of Li₈MP₄ (M = Si, Ge) at the lithium metal anode/LLZO interface. Through comprehensive first-principles thermodynamic calculations and hierarchical ion-transport algorithms, the phase stability, electrochemical stability, chemical stability, ionic transport, Li wettability, and mechanical properties of the candidate materials were systematically predicted and analyzed. Results indicate that the candidate coatings are thermodynamically stable at 0 K, with superior reduction stability against the lithium metal anode and good chemical compatibility with LLZO. Their Li-ion migration barriers are as low as 0.32 eV, enabling room-temperature ionic conductivity of approximately 10⁻⁵ S/cm. Moreover, the predicted works of adhesion for Li/Li₈MP₄ (M = Si, Ge) are 0.99 and 0.76 J/m², respectively, corresponding to the contact angles of 0° and 49.3°, indicating that metallic Li shows good wettability on Li₈MP₄ (M = Si, Ge) materials. This work provides a comprehensive understanding of the thermodynamic and dynamic behaviors of Li₈MP₄ (M = Si, Ge) coatings and will guide the experimental design for desired SSLMB anode coatings.

[APL Materials Current Issue] Lithography-free fabrication of transparent, durable surfaces with embedded functional materials in glass nanoholes

APL Materials Current Issue — Tue, 30 Dec 2025 00:00:00 GMT

Touch-enabled technologies, from smartphones to public kiosks, are ubiquitous, yet frequent use turns their surfaces into reservoirs for microbial contamination. Routine alcohol-based cleaning can be impractical on high-touch optical surfaces due to damage risk and usability concerns. Here, we present a scalable approach to transparent, mechanically robust glass surfaces by embedding materials with ad hoc functionality into surface glass nanoholes. We demonstrate the concept with copper nanodisks: copper is an established antimicrobial agent, but its wear susceptibility pose challenges for use on transparent displays. Our design shields the functional material from lateral wear while allowing ion diffusion for antimicrobial efficacy. Fabrication uses only wafer-compatible, lithography-free steps: thermal dewetting of a thin silver film to create a nanosized mask; inverting it to a polymer nanoholes mask by etching the silver nanoparticles; wet etching of the glass to form nanoholes; selective copper deposition inside these holes; and liftoff of excess material. The resulting surfaces exhibit mean transmission of 80%–85% in the 380–750 nm range with haze <1% and minimal color shift, compared to uncoated glass. Antimicrobial efficacy, assessed against Escherichia coli OP50 under a modified U.S. EPA protocol, shows ≈99% bacterial reduction within one hour. Abrasion tests with a crockmeter simulating finger swipes confirm that the embedded copper remains intact, with no measurable change in optical performance. This embedded design provides a scalable route to integrate antimicrobial functionality into high-touch transparent systems while preserving optical clarity and wear resistance, with potential relevance for medical, consumer, and transportation interfaces.

[ChemRxiv] Augmenting Large Language Models for Automated Discovery of f-Element Extractants

ChemRxiv — Tue, 30 Dec 2025 00:00:00 GMT

Efficient separation of f-elements is a critical challenge for a wide range of emerging technologies. The chemical similarity among these elements makes the development of selective solvent extraction reagents both slow and difficult. Here, we present a quasi-autonomous, AI-enabled workflow for the design and computational screening of selective extractant ligands. Molecular design is guided by SAFE-MolGen, a large language model-based agentic system that leverages curated extraction data to propose new ligands and preliminarily rank their performance using a supervised machine learning model trained on experimental datasets to consider the impact of realistic experimental conditions. Promising human-approved ligands are then passed to a second automated pipeline that constructs three-dimensional metal-ligand complexes and performs quantum mechanical free energy calculations to directly assess metal selectivity. We demonstrate this approach for Am(III)/Eu(III) separations and report several newly designed ligands predicted to exhibit higher Am(III)/Eu(III) selectivity than the benchmark extractant CyMe4BTBP. This workflow accelerates computational exploration of the molecular space in this data-sparse field and provides a general strategy for the rapid generation and evaluation of novel lanthanide (Ln) and actinide (An) extractants.

[RSC - Chem. Sci. latest articles] Solid Dual-Salt Plastic Crystal Electrolyte Enabling Rapid Ion Transfer and Stable Interphases for High-Performance Solid-State Sodium Ion Batteries

RSC - Chem. Sci. latest articles — Tue, 30 Dec 2025 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC09186A, Edge Article

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Yang Jiang, Rui Wang, Peng Xiong, Yangyang Liu, Hongbao Li, Longhai Zhang, Ya You, Chaofeng Zhang
As promising next-generation energy storage systems, solid-state sodium ion batteries (SIBs) are hindered by the low ionic conductivity of their solid electrolytes and poor interfacial compatibility. Here, we developed a...
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[Wiley: Advanced Science: Table of Contents] Intelligent Sensing: The Emerging Integration of Machine Learning and Soft Sensors Based on Hydrogels and Ionogels

Wiley: Advanced Science: Table of Contents — Mon, 29 Dec 2025 21:01:21 GMT

Advanced Science, Volume 12, Issue 48, December 29, 2025.

[Wiley: Advanced Science: Table of Contents] Pre‐Constructed Mechano‐Electrochemical Adaptive Solid Electrolyte Interphase to Enhance Li+ Diffusion Kinetics and Interface Stability for Chemically Prelithiated SiO Anodes

Wiley: Advanced Science: Table of Contents — Mon, 29 Dec 2025 21:01:21 GMT

Advanced Science, Volume 12, Issue 48, December 29, 2025.

[Wiley: Small: Table of Contents] Unraveling A‐Site Cation Control of Hot Carrier Relaxation in Vacancy‐Ordered Halide Perovskites Through Quantum Dynamics and Interpretable Machine Learning

Wiley: Small: Table of Contents — Mon, 29 Dec 2025 20:38:41 GMT

Small, Volume 21, Issue 52, December 29, 2025.

[Wiley: Advanced Materials: Table of Contents] Rational Design of Metal–Organic Frameworks for Pancreatic Cancer Therapy: from Machine Learning Screening to In Vivo Efficacy (Adv. Mater. 52/2025)

Wiley: Advanced Materials: Table of Contents — Mon, 29 Dec 2025 19:50:02 GMT

Advanced Materials, Volume 37, Issue 52, December 29, 2025.

[Wiley: Advanced Materials: Table of Contents] Rational Design of Metal–Organic Frameworks for Pancreatic Cancer Therapy: from Machine Learning Screening to In Vivo Efficacy

Wiley: Advanced Materials: Table of Contents — Mon, 29 Dec 2025 19:50:02 GMT

Advanced Materials, Volume 37, Issue 52, December 29, 2025.

[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Chloride-Based Solid Electrolytes from Crystal Structure to Electrochemical Performance

ACS Energy Letters: Latest Articles (ACS Publications) — Mon, 29 Dec 2025 19:20:24 GMT

ACS Energy Letters

DOI: 10.1021/acsenergylett.5c03415

[Wiley: Small: Table of Contents] Enhancing Cycling Stability and Suppressing Lithium Dendrite Formation With A Hierarchical Artificial Solid Electrolyte Interphase Layer on Lithium Anodes for High‐Voltage Lithium Metal Batteries

Wiley: Small: Table of Contents — Mon, 29 Dec 2025 09:13:44 GMT

Small, EarlyView.

[Wiley: Advanced Materials: Table of Contents] Gradient‐Heterojunction in Solid Electrolytes for Fast‐Charging Dendrite‐Free Solid‐State Lithium Metal Batteries

Wiley: Advanced Materials: Table of Contents — Mon, 29 Dec 2025 07:59:12 GMT

Advanced Materials, EarlyView.

[APL Machine Learning Current Issue] Synthetic images from generative AI for compositional analysis of dried solution patterns

APL Machine Learning Current Issue — Mon, 29 Dec 2025 00:00:00 GMT

Image-based identification of solutes from dried drop stains offers a low-cost, scalable alternative to traditional chemical analysis, enabled by artificial intelligence and annotated image libraries. While recent advances in robotic droplet imaging have facilitated the collection of such datasets, experimental acquisition remains a key bottleneck. Here, we explore whether synthetic image generation can supplement or replace real data in the context of salt classification. Using Stable Diffusion with low-rank adaptation, we generate 133 000 synthetic images from as few as N = 1–128 experimental images per salt across seven common inorganic solutes. Synthetic images become visually indistinguishable from real ones for N ≥ 6; however, quantitative analysis based on 47 image metrics reveals subtle differences that vanish around N = 128. When used to train random forest, XGBoost, and multilayer perceptron (MLP) models, synthetic data alone achieve classification accuracies of up to 90% in some low-data regimes. Synthetic augmentation offers a powerful strategy for expanding training sets and enabling image-based chemical classification in data-scarce scenarios, even if it seldom outperforms models trained on real data. The realism of synthetic images also highlights the growing need for tools that detect AI-generated scientific images to ensure data integrity.

[iScience] An Interpretable Machine Learning Model for Predicting the Effectiveness of Low-Dose Methylprednisolone in Treating Long COVID: A Retrospective Study

iScience — Mon, 29 Dec 2025 00:00:00 GMT

Long COVID is a chronic, multisystem disease with limited response to conventional treatments. While low-dose methylprednisolone has shown effectiveness in some patients, individual responses vary, and accurate predictive tools are lacking. This retrospective study included 330 Long COVID patients who received low-dose methylprednisolone treatment across three hospitals. Patients were divided into training (n=202), test (n=33), and external validation sets (n=53, n=42). Using Least Absolute Shrinkage and Selection Operator (LASSO) regression, 38 variables were analyzed to develop six machine learning models.

[iScience] River plastic hotspot detection from space

iScience — Mon, 29 Dec 2025 00:00:00 GMT

Plastic pollution threatens terrestrial and aquatic ecosystems, and rivers play a central role in transporting and retaining plastics across landscapes. Effective mitigation requires scalable methods to identify riverine plastic accumulation hotspots. Here, we present a semi-automated, cloud-based pipeline that integrates satellite remote sensing and machine learning to detect river plastic hotspots. High-resolution PlanetScope imagery is used to annotate training regions, which are transferred to Sentinel-2 multispectral data to train Random Forest classifiers within Google Earth Engine.

[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Machine Learning−Accelerated Discovery of Proton-Conducting 2D Materials for Proton Exchange Membranes

ACS Nano: Latest Articles (ACS Publications) — Sat, 27 Dec 2025 14:37:43 GMT

ACS Nano

DOI: 10.1021/acsnano.5c15161

[Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)] [ASAP] Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Data set Generation for Training Machine-Learned Interatomic Potentials

Journal of Chemical Theory and Computation: Latest Articles (ACS Publications) — Fri, 26 Dec 2025 18:25:53 GMT

Journal of Chemical Theory and Computation

DOI: 10.1021/acs.jctc.5c01610

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Machine Learning Photodynamics Unveils a Controlled H2 Loss Channel in the Methaniminium Cation

The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications) — Fri, 26 Dec 2025 17:51:53 GMT

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03196

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Solvent Structure and Dynamics Controlled Memristive Ion Transport in Å-Scale Channels

The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications) — Fri, 26 Dec 2025 16:50:38 GMT

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03397

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Design of Open Ion Transport Channels in Thick Electrodes through Directional Freezing-Assisted 3D Printing: Enhancing the Electrochemical Performance of High-Load Electrodes

The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications) — Fri, 26 Dec 2025 16:49:57 GMT

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c02968

[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Machine Learning Analysis of Dimensionality Effects on Bandgap Prediction

The Journal of Physical Chemistry C: Latest Articles (ACS Publications) — Fri, 26 Dec 2025 16:06:02 GMT

The Journal of Physical Chemistry C

DOI: 10.1021/acs.jpcc.5c05232

[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Machine Learning-Accelerated Development of Li-Rich Layered Oxides with High Reversible Capacity and Coulombic Efficiency

ACS Nano: Latest Articles (ACS Publications) — Fri, 26 Dec 2025 09:21:05 GMT

ACS Nano

DOI: 10.1021/acsnano.5c16117

[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] A complete spatial map of mouse retinal ganglion cells reveals density and gene expression specializations

Fri, 26 Dec 2025 08:00:00 GMT

Proceedings of the National Academy of Sciences, Volume 122, Issue 52, December 2025.
SignificanceRetinal ganglion cells (RGCs) transmit visual signals to the brain, and their diversity supports specialized visual functions. Using gene expression mapping and machine learning, we charted the spatial distribution of all known RGC types in ...

[Wiley: Advanced Science: Table of Contents] Mendelian Randomization and Double Machine Learning Modeling Reveal Brain Imaging‐Derived Phenotypes as Functional Contributors to 18 Autoimmune Inflammatory Diseases

Wiley: Advanced Science: Table of Contents — Fri, 26 Dec 2025 06:23:13 GMT

Advanced Science, EarlyView.

[Wiley: Carbon Energy: Table of Contents] Data‐Driven Design of Scalable Perovskite Film Fabrication via Machine Learning–Guided Processing

Wiley: Carbon Energy: Table of Contents — Fri, 26 Dec 2025 06:22:51 GMT

Carbon Energy, EarlyView.

[iScience] Entropy-based byte patching transformer for self-supervised pretraining of SMILES data

iScience — Fri, 26 Dec 2025 00:00:00 GMT

Efficient molecular representations are critical for improving the performance and generalization of large language models in chemical learning. Transformer-based architectures have advanced molecular representation learning, yet capturing localized and hierarchical chemical structures remains challenging. We introduce the SMILES Byte-Patch Transformer (SMiBPT), an adaptive model that dynamically segments SMILES and DeepSMILES strings into chemically meaningful substructures through entropy-based byte patching.

[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Navigating the Catholyte Landscape in All-Solid-State Batteries

ACS Energy Letters: Latest Articles (ACS Publications) — Wed, 24 Dec 2025 16:14:16 GMT

ACS Energy Letters

DOI: 10.1021/acsenergylett.5c03429

[Wiley: Advanced Functional Materials: Table of Contents] Printing Nacre‐Mimetic MXene‐Based E‐Textile Devices for Sensing and Breathing‐Pattern Recognition Using Machine Learning

Wiley: Advanced Functional Materials: Table of Contents — Wed, 24 Dec 2025 15:52:36 GMT

Advanced Functional Materials, Volume 35, Issue 52, December 23, 2025.

[Wiley: Advanced Functional Materials: Table of Contents] Role of Crosslinking and Backbone Segmental Dynamics on Ion Transport in Hydrated Anion‐Conducting Polyelectrolytes

Wiley: Advanced Functional Materials: Table of Contents — Wed, 24 Dec 2025 15:52:36 GMT

Advanced Functional Materials, Volume 35, Issue 52, December 23, 2025.

[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Conjunctive population coding integrates sensory evidence to guide adaptive behavior

Wed, 24 Dec 2025 08:00:00 GMT

Proceedings of the National Academy of Sciences, Volume 122, Issue 52, December 2025.
SignificanceContext-dependent behavior, i.e., the appropriate action selection according to current circumstances, long-term goals, and recent experiences, hallmarks human cognitive flexibility. But which neural mechanisms integrate prior knowledge with ...

[Wiley: Advanced Energy Materials: Table of Contents] Hyperquaternized Biomass‐Derived Solid Electrolytes: Architecting Superionic Conduction for Sustainable Flexible Zinc‐Air Batteries

Wiley: Advanced Energy Materials: Table of Contents — Wed, 24 Dec 2025 07:08:52 GMT

Advanced Energy Materials, EarlyView.

[npj Computational Materials] High-throughput computational framework for high-order anharmonic thermal transport in cubic and tetragonal crystals

npj Computational Materials — Wed, 24 Dec 2025 00:00:00 GMT

npj Computational Materials, Published online: 24 December 2025; doi:10.1038/s41524-025-01920-y

High-throughput computational framework for high-order anharmonic thermal transport in cubic and tetragonal crystals

[Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)] [ASAP] Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials

Journal of Chemical Theory and Computation: Latest Articles (ACS Publications) — Tue, 23 Dec 2025 19:20:50 GMT

Journal of Chemical Theory and Computation

DOI: 10.1021/acs.jctc.5c01712

[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Tuning ORR Activity of N-Doped Biphenylene-Based Single-Atom Catalysts via DFT and Machine Learning Synergy

The Journal of Physical Chemistry C: Latest Articles (ACS Publications) — Tue, 23 Dec 2025 18:21:52 GMT

The Journal of Physical Chemistry C

DOI: 10.1021/acs.jpcc.5c06757

[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Concomitant Enhancement of the Reorientational Dynamics of the BH4– Anions and Mg2+ Ionic Conductivity in Mg(BH4)2·NH3 upon Ligand Incorporation

The Journal of Physical Chemistry C: Latest Articles (ACS Publications) — Tue, 23 Dec 2025 13:34:12 GMT

The Journal of Physical Chemistry C

DOI: 10.1021/acs.jpcc.5c07031

[Wiley: Advanced Energy Materials: Table of Contents] Interpretable Machine Learning for Battery Prognosis: Retrospect and Prospect

Wiley: Advanced Energy Materials: Table of Contents — Tue, 23 Dec 2025 10:15:25 GMT

Advanced Energy Materials, Volume 15, Issue 48, December 23, 2025.

[Wiley: Advanced Energy Materials: Table of Contents] Novel Sodium‐Rare‐Earth‐Silicate‐Based Solid Electrolytes for All‐Solid‐State Sodium Batteries: Structure, Synthesis, Conductivity, and Interface

Wiley: Advanced Energy Materials: Table of Contents — Tue, 23 Dec 2025 10:15:25 GMT

Advanced Energy Materials, Volume 15, Issue 48, December 23, 2025.

[Wiley: Advanced Energy Materials: Table of Contents] Ambipolar Ion Transport Membranes Enable Stable Noble‐Metal‐Free CO2 Electrolysis in Neutral Media

Wiley: Advanced Energy Materials: Table of Contents — Tue, 23 Dec 2025 10:15:25 GMT

Advanced Energy Materials, Volume 15, Issue 48, December 23, 2025.

[Wiley: Small: Table of Contents] Supersaturation‐Driven Co‐Precipitation Enables Scalable Wet‐Chemical Synthesis of High‐Purity Na3InCl6 Solid Electrolyte for Sodium‐Ion Batteries

Wiley: Small: Table of Contents — Tue, 23 Dec 2025 07:06:10 GMT

Small, Volume 21, Issue 51, December 23, 2025.

[Wiley: Small: Table of Contents] Synergistic Co‐Optimization Strategy for Electron‐Ion Transport Kinetics in all‐Solid‐State Sulfurized Polyacrylonitrile Cathodes

Wiley: Small: Table of Contents — Tue, 23 Dec 2025 07:06:10 GMT

Small, Volume 21, Issue 51, December 23, 2025.

[RSC - Chem. Sci. latest articles] Robust Janus-Faced Quasi-Solid-State Electrolytes Mimicking Honeycomb for Fast Transport and Adequate Supply of Sodium Ions

RSC - Chem. Sci. latest articles — Tue, 23 Dec 2025 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC08536E, Edge Article

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Fang Chen, Yadan Xie, Zhoubin Yu, Na Li, Xiang Ding, Yu Qiao
Quasi-solid-state electrolytes are one of the most promising alternative candidate for traditional liquid state electrolytes with fast ion transport rate, high mechanical strength and wide temperature adaptation. Here we designed...
The content of this RSS Feed (c) The Royal Society of Chemistry

[RSC - Chem. Sci. latest articles] Automated Closed-Loop Continuous Flow Block Copolymer Synthesizer

RSC - Chem. Sci. latest articles — Tue, 23 Dec 2025 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC07307C, Edge Article

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

WeiNian Wong, Daniel Phillips, MD Taifur Rahman, Tanja Junkers
A fully automated continuous flow synthesizer for diblock copolymer (BCP) synthesis was constructed comprising elements of flow chemistry, automation, machine learning and in-line monitoring. A new method using in-line FTIR...
The content of this RSS Feed (c) The Royal Society of Chemistry

[RSC - Chem. Sci. latest articles] Node-Equivariant Message Passing for Efficient and Accurate Machine Learning Interatomic Potentials

RSC - Chem. Sci. latest articles — Tue, 23 Dec 2025 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC07248D, Edge Article

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Yaolong Zhang, Hua Guo
Machine learned interatomic potentials, particularly equivariant message-passing (MP) models, have demonstrated high fidelity in representing first-principles data, revolutionizing computational studies in materials science, biophysics, and catalysis. However, these equivariant MP...
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[iScience] A Multicenter Multimodel Habitat Radiomics Model for Predicting Immunotherapy Response in Advanced NSCLC

iScience — Tue, 23 Dec 2025 00:00:00 GMT

Robust predictive biomarker is critical for identifying NSCLC patients who may benefit from immunotherapy. This study developed a CT-based habitat model using 590 advanced NSCLC cases. The model was constructed in contrast-enhanced CT images and validated on an independent cohort with non-contrast CT. Tumor volumes were segmented into three subregions via K-means clustering. Radiomic features were extracted from each habitat and used to build predictive models with six machine learning classifiers.The ExtraTrees-based habitat model demonstrated superior predictive performance in the test cohort(AUC = 0.814).

[Joule] Machine learning-driven interface material design for high-performance perovskite solar cells with scalability and band-gap universality

Joule — Tue, 23 Dec 2025 00:00:00 GMT

Machine learning-driven molecular design integrating correlation analysis, clustering, and LASSO regression discovers BIPA, an efficient interface modifier that concurrently passivates defects, optimizes band alignment, and enhances perovskite crystallinity. This strategy enables high-efficiency, scalable, and stable perovskite solar cells across a wide band-gap range (1.55–1.85 eV).

[Cell Reports Physical Science] A global thermodynamic-kinetic model capturing the hallmarks of liquid-liquid phase separation and amyloid aggregation

Cell Reports Physical Science — Tue, 23 Dec 2025 00:00:00 GMT

Bhandari et al. develop a unified thermodynamic-kinetic framework that integrates liquid-liquid phase separation (LLPS) with amyloid aggregation. By considering oligomerization and fibrillization in both protein-poor and protein-rich phases, the model reproduces concentration-dependent aggregation kinetics and rationalizes the seemingly contradictory reports on whether LLPS accelerates or suppresses fibril formation.

[RSC - Chem. Sci. latest articles] Chemically-informed active learning enables data-efficient multi-objective optimization of self-healing polyurethanes

RSC - Chem. Sci. latest articles — Tue, 23 Dec 2025 00:00:00 GMT

Chem. Sci., 2026, Advance Article
DOI: 10.1039/D5SC07752D, Edge Article

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Kang Liang, Xinke Qi, Xu Xiao, Li Wang, Jinglai Zhang
A chemically-informed active learning (CIAL) framework synergizes chemical knowledge with machine learning to achieve multi-objective optimization of self-healing polyurethanes with only 20 samples, overcoming traditional material design trade-offs.
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[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Regulating Solvation Structure and Ion Transport via Lewis-Base Dual-Functional Covalent Organic Polymer Separators for Dendrite-Free Li-Metal Anodes

ACS Nano: Latest Articles (ACS Publications) — Mon, 22 Dec 2025 20:52:05 GMT

ACS Nano

DOI: 10.1021/acsnano.5c14722

[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Highly Selective Lithium-Ion Separation by Regulating Ion Transport Energy Barriers of Vermiculite Membranes

ACS Nano: Latest Articles (ACS Publications) — Mon, 22 Dec 2025 18:30:41 GMT

ACS Nano

DOI: 10.1021/acsnano.5c17718

[Wiley: Advanced Intelligent Discovery: Table of Contents] A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Wiley: Advanced Intelligent Discovery: Table of Contents — Mon, 22 Dec 2025 17:43:04 GMT

Advanced Intelligent Discovery, EarlyView.

[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Multianion Synergism Boosts High-Performance All-Solid-State Lithium Batteries

ACS Nano: Latest Articles (ACS Publications) — Mon, 22 Dec 2025 14:37:35 GMT

ACS Nano

DOI: 10.1021/acsnano.5c12987

[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Density Effects on the Thermal Decomposition of LLM-105 Explored by Neural Network Potential

The Journal of Physical Chemistry C: Latest Articles (ACS Publications) — Mon, 22 Dec 2025 14:01:00 GMT

The Journal of Physical Chemistry C

DOI: 10.1021/acs.jpcc.5c06140

[Accounts of Chemical Research: Latest Articles (ACS Publications)] [ASAP] Accelerating Materials Discovery Through Sparse Gaussian Process Machine Learning Potentials

Accounts of Chemical Research: Latest Articles (ACS Publications) — Mon, 22 Dec 2025 13:59:15 GMT

Accounts of Chemical Research

DOI: 10.1021/acs.accounts.5c00667

[Recent Articles in Phys. Rev. Lett.] Gate-Tunable Spectrum and Charge Dispersion Mitigation in a Graphene Superconducting Qubit

Recent Articles in Phys. Rev. Lett. — Mon, 22 Dec 2025 10:00:00 GMT

Author(s): Nicolas Aparicio, Simon Messelot, Edgar Bonet-Orozco, Eric Eyraud, Kenji Watanabe, Takashi Taniguchi, Johann Coraux, and Julien Renard

Controlling the energy spectrum of quantum-coherent superconducting circuits, i.e., the energies of excited states, the circuit anharmonicity, and the states’ charge dispersion, is essential for designing performant qubits. This control is usually achieved by adjusting the circuit’s geometry. In sit…

[Phys. Rev. Lett. 135, 266001] Published Mon Dec 22, 2025

[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Maladaptive immunity to the microbiota promotes neuronal hyperinnervation and itch via IL-17A

Mon, 22 Dec 2025 08:00:00 GMT

Proceedings of the National Academy of Sciences, Volume 122, Issue 52, December 2025.
SignificancePruritus (itch), a phenomenon associated with various inflammatory skin diseases including psoriasis and atopic dermatitis, remains a major unmet clinical need with few effective treatments. While sensory hyperinnervation is a hallmark of ...

[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] SR-LLM: An incremental symbolic regression framework driven by LLM-based retrieval-augmented generation

Mon, 22 Dec 2025 08:00:00 GMT

Proceedings of the National Academy of Sciences, Volume 122, Issue 52, December 2025.
SignificanceScientists have long sought to derive models from extensive observational input–output data, ensuring these models accurately capture the underlying mapping from inputs to outputs while remaining interpretable to humans through clear meanings. ...

[Applied Physics Reviews Current Issue] Thermal conductivity limits of MoS 2 and MoSe 2 : Revisiting high-order anharmonic lattice dynamics with machine learning potentials

Applied Physics Reviews Current Issue — Mon, 22 Dec 2025 00:00:00 GMT

Group-VI transition metal dichalcogenides (TMDs), MoS₂ and MoSe₂, have emerged as prototypical low-dimensional systems with distinctive phononic and electronic properties, making them attractive for applications in nanoelectronics, optoelectronics, and thermoelectrics. However, their reported lattice thermal conductivities ( κ) remain highly inconsistent, with experimental values and theoretical predictions differing by more than an order of magnitude. These discrepancies stem from uncertainties in measurement techniques, variations in computational protocols, and ambiguities in the treatment of higher-order anharmonic processes. In this study, we critically review these inconsistencies, first by mapping the spread of experimental and modeling results, and then by identifying the methodological origins of divergence. To this end, we bridge first-principles calculations, molecular dynamics simulations, and state-of-the-art machine learning force fields (MLFFs), including recently developed foundation models. We train and benchmark GAP, MACE, NEP, and HIPHIVE against density functional theory and rigorously evaluate the impact of third- and fourth-order phonon scattering processes on κ. The computational efficiency of MLFFs enables us to extend convergence tests beyond conventional limits and to validate predictions through homogeneous nonequilibrium molecular dynamics as well. Our analysis demonstrates that, contrary to some recent claims, fully converged four-phonon processes contribute negligibly to the intrinsic thermal conductivity of both MoS₂ and MoSe₂. These findings not only refine the intrinsic transport limits of 2D TMDs but also establish MLFF-based approaches as a robust and scalable framework for predictive modeling of phonon-mediated thermal transport in low-dimensional materials.

[iScience] Widely Targeted Metabolomics and Machine Learning Identify Succinate as a Key Metabolite in Sepsis-Associated Encephalopathy

iScience — Mon, 22 Dec 2025 00:00:00 GMT

Sepsis-associated encephalopathy (SAE) is a common and serious complication of sepsis that leads to acute brain dysfunction and long-term cognitive impairment. We used widely targeted LC–MS/MS plasma metabolomics in 29 healthy controls, 32 sepsis patients, and 27 SAE patients, combined with machine learning, to define metabolic patterns across these groups. This approach identified 12 discriminatory metabolites, with succinate showing a stepwise increase from health to sepsis to SAE and associations with clinical severity scores.

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Interpreting X-ray Diffraction Patterns of Metal–Organic Frameworks via Generative Artificial Intelligence

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Sat, 20 Dec 2025 15:03:45 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c16416

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] A Transformative Molecular Muscle Solid Electrolyte

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Fri, 19 Dec 2025 20:12:03 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c18427

[Wiley: Small Structures: Table of Contents] Li6−xFe1−xAlxCl8 Solid Electrolytes for Cost‐Effective All‐Solid‐State LiFePO4 Batteries

Wiley: Small Structures: Table of Contents — Fri, 19 Dec 2025 18:40:34 GMT

Small Structures, EarlyView.

[Joule] Boosting ionic conductivity of fluoride electrolytes by polyanion coordination chemistry enabling 5 V-Class all-solid-state batteries

Joule — Fri, 19 Dec 2025 00:00:00 GMT

This study developed a novel class of highly conductive amorphous fluoride solid-state electrolytes (SSEs) LixTi(PO4)x/3F4, using a polyanion coordination strategy. The optimized Li1.3Ti(PO4)1.3/3F4 achieves a Li+ conductivity of 1.16 × 10−5 S cm−1, two orders of magnitude higher than that of analogous Li2TiF6. Combining the inherent high-voltage stability of fluoride and the excellent ionic conductivity of Li1.3Ti(PO4)1.3/3F4, this material enables superior 5 V-class all-solid-state battery performance. This work opens a new avenue for designing high-conductivity fluoride SSEs and advancing the performance of 5 V-class all-solid-state batteries.

[Recent Articles in Phys. Rev. Lett.] Variational Machine Learning Model for Electronic Structure Optimization via the Density Matrix

Recent Articles in Phys. Rev. Lett. — Thu, 18 Dec 2025 10:00:00 GMT

Author(s): Luqi Dong, Shuxiang Yang, Su-Huai Wei, and Yunhao Lu

We present a novel approach that combines machine learning with direct variational energy optimization via the density matrix to solve the Kohn-Sham equation in density functional theory. Instead of relying on the conventional self-consistent field method, our approach directly optimizes the ground …

[Phys. Rev. Lett. 135, 256403] Published Thu Dec 18, 2025

[Wiley: Advanced Science: Table of Contents] Polymorphism Crystal Structure Prediction with Adaptive Space Group Diversity Control

Wiley: Advanced Science: Table of Contents — Thu, 18 Dec 2025 09:38:21 GMT

Advanced Science, Volume 12, Issue 47, December 18, 2025.

[Wiley: Advanced Science: Table of Contents] Computationally‐Guided Development of Sulfide Solid Electrolyte Powder Coatings for Enhanced Stability and Performance of Solid‐State Batteries

Wiley: Advanced Science: Table of Contents — Thu, 18 Dec 2025 09:38:21 GMT

Advanced Science, Volume 12, Issue 47, December 18, 2025.

[Proceedings of the National Academy of Sciences: Physical Sciences] Uncovering inequalities in new knowledge learning by large language models across different languages

Proceedings of the National Academy of Sciences: Physical Sciences — Thu, 18 Dec 2025 08:00:00 GMT

Proceedings of the National Academy of Sciences, Volume 122, Issue 51, December 2025.
SignificanceLarge language models (LLMs) are transforming daily life, yet users across different languages may not benefit equally. Our study shows that LLMs face greater challenges in learning new knowledge and resisting incorrect or misleading ...

[AAAS: Science: Table of Contents] State-independent ionic conductivity

AAAS: Science: Table of Contents — Thu, 18 Dec 2025 07:00:11 GMT

Science, Volume 390, Issue 6779, Page 1254-1258, December 2025.

[AAAS: Science: Table of Contents] Scientific production in the era of large language models

AAAS: Science: Table of Contents — Thu, 18 Dec 2025 07:00:11 GMT

Science, Volume 390, Issue 6779, Page 1240-1243, December 2025.

[ACS Nano: Latest Articles (ACS Publications)] [ASAP] van Hove Source for Ultralow Power Field-Effect Transistors

ACS Nano: Latest Articles (ACS Publications) — Wed, 17 Dec 2025 18:12:49 GMT

ACS Nano

DOI: 10.1021/acsnano.5c17157

[Wiley: Advanced Functional Materials: Table of Contents] Machine Learning‐assisted Ultrasensitive SERS Immunoassays Across Wide Concentration Ranges Toward Clinical Ovarian Cancer Diagnosis

Wiley: Advanced Functional Materials: Table of Contents — Wed, 17 Dec 2025 14:49:25 GMT

Advanced Functional Materials, Volume 35, Issue 51, December 16, 2025.

[Wiley: Advanced Functional Materials: Table of Contents] Prediction and Fine Screening of Small Molecular Passivation Materials for High‐Efficiency Perovskite Solar Cells Via an Enhanced Machine Learning Workflow

Wiley: Advanced Functional Materials: Table of Contents — Wed, 17 Dec 2025 14:49:25 GMT

Advanced Functional Materials, Volume 35, Issue 51, December 16, 2025.

[Wiley: Advanced Materials: Table of Contents] Tailoring Graphite Interlayers with Electron‐Acceptor Bridges Raises Ion Diffusion Kinetics for Ultrafast Charging Batteries

Wiley: Advanced Materials: Table of Contents — Wed, 17 Dec 2025 14:13:34 GMT

Advanced Materials, Volume 37, Issue 50, December 17, 2025.

[Wiley: Small: Table of Contents] Interfacial Catalysis Engineering of Solid Electrolyte Interphase Toward High‐Performance Batteries

Wiley: Small: Table of Contents — Wed, 17 Dec 2025 12:51:53 GMT

Small, Volume 21, Issue 50, December 17, 2025.

[Wiley: Small: Table of Contents] In Situ Construction of Dual‐Functional UiO‐66‐NH2 Coated Li1.3Al0.3Ti1.7(PO4)3 to Achieve Lithium Metal Cells with Efficient Ion Transport in Quasi‐Solid Electrolytes

Wiley: Small: Table of Contents — Wed, 17 Dec 2025 12:51:53 GMT

Small, Volume 21, Issue 50, December 17, 2025.

[Wiley: Small: Table of Contents] 1D Lithium‐Ion Transport in a LiMn2O4 Nanowire Cathode during Charge–Discharge Cycles

Wiley: Small: Table of Contents — Wed, 17 Dec 2025 12:51:53 GMT

Small, Volume 21, Issue 50, December 17, 2025.

[Wiley: Small: Table of Contents] A Reversible Zinc Metal Anode with an Inorganic/Organic Solid Electrolyte Interphase Enriched for Epitaxial Deposition Along the Zn (101) Plane

Wiley: Small: Table of Contents — Wed, 17 Dec 2025 12:51:53 GMT

Small, Volume 21, Issue 50, December 17, 2025.

[Nature Materials] Probing frozen solid electrolyte interphases

Nature Materials — Wed, 17 Dec 2025 00:00:00 GMT

Nature Materials, Published online: 17 December 2025; doi:10.1038/s41563-025-02443-z

Probing frozen solid electrolyte interphases

[Cell Reports Physical Science] Accelerated inorganic materials design with generative AI agents

Cell Reports Physical Science — Wed, 17 Dec 2025 00:00:00 GMT

Takahara et al. introduce MatAgent, a generative AI agent for inorganic materials design that integrates large language model reasoning with diffusion-based generation and property prediction. The human-inspired reasoning process facilitates interpretable and property-directed discovery of inorganic materials.

[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Fluorinated Halide Solid Electrolytes for High-Voltage All-Solid-State Sodium-Ion Batteries Enabling Reversible Oxygen Redox

ACS Energy Letters: Latest Articles (ACS Publications) — Tue, 16 Dec 2025 20:00:00 GMT

ACS Energy Letters

DOI: 10.1021/acsenergylett.5c03248

[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Ionic Liquids in Quasi-Solid-State Li–S Batteries with Sulfide-Based Solid Electrolytes: A Density Functional Theory and Ab Initio Molecular Dynamics Study

The Journal of Physical Chemistry C: Latest Articles (ACS Publications) — Tue, 16 Dec 2025 14:13:16 GMT

The Journal of Physical Chemistry C

DOI: 10.1021/acs.jpcc.5c05916

[Wiley: Advanced Energy Materials: Table of Contents] How Machine Learning Has Driven the Development of Rechargeable Ion Batteries

Wiley: Advanced Energy Materials: Table of Contents — Tue, 16 Dec 2025 10:18:19 GMT

Advanced Energy Materials, Volume 15, Issue 47, December 16, 2025.

[Wiley: Advanced Energy Materials: Table of Contents] Interplay Between the Dissolved Mn2+ and Solid Electrolyte Interphases of Graphite Anode

Wiley: Advanced Energy Materials: Table of Contents — Tue, 16 Dec 2025 10:18:19 GMT

Advanced Energy Materials, Volume 15, Issue 47, December 16, 2025.

[Wiley: Advanced Energy Materials: Table of Contents] From HF Scavenging to Li‐Ion Transport Enhancement: Multifunctional Separator Enabling Stable Li Metal Batteries in Carbonate‐Based Electrolytes

Wiley: Advanced Energy Materials: Table of Contents — Tue, 16 Dec 2025 09:58:08 GMT

Advanced Energy Materials, EarlyView.

[Wiley: Advanced Energy Materials: Table of Contents] Insight Into All‐Solid‐State Lithium‐Sulfur Batteries: Challenges and Interface Engineering at the Electrode‐Sulfide Solid Electrolyte Interface

Wiley: Advanced Energy Materials: Table of Contents — Tue, 16 Dec 2025 09:45:18 GMT

Advanced Energy Materials, EarlyView.

[Proceedings of the National Academy of Sciences: Physical Sciences] Designing strongly coupled polaritonic structures via statistical machine learning

Proceedings of the National Academy of Sciences: Physical Sciences — Tue, 16 Dec 2025 08:00:00 GMT

Proceedings of the National Academy of Sciences, Volume 122, Issue 51, December 2025.
SignificanceStrong coupling photonics enables precise control of light at subwavelength scales, offering transformative potential in energy conversion and optical information processing. However, designing these systems remains a significant challenge due ...

[iScience] What Makes a Scent Trigger a Memory? A Cognitive Decomposition of Odor-Evoked Retrieval

iScience — Tue, 16 Dec 2025 00:00:00 GMT

A single scent can unlock vivid memories. This study investigates the factors that make some odors more evocative than others. We examined odor-evoked episodic memory in 106 participants who experienced odors embedded in distinct visuospatial contexts, and whose memory was tested 24–72 hours later. The protocol empirically dissociates odor recognition (“I’ve already smelled this scent”) and associative memory (“It evokes a memory”) processes. Using machine learning with SHapley Additive exPlanations, we identified distinct predictors for each process.

[iScience] Combining DNA Methylation Features and Clinical Characteristics Predicts Ketamine Treatment Response for PTSD

iScience — Tue, 16 Dec 2025 00:00:00 GMT

Post-traumatic stress disorder (PTSD) exhibits extensive clinical and biological variability, making treatment challenging. The Consortium to Alleviate PTSD (CAP)-ketamine trial, the largest randomized study of ketamine for PTSD, found no overall benefit of ketamine over placebo, underscoring the necessity to identify responsive subgroups. Using pre-treatment blood DNA methylation profiles and clinical measures from the CAP-ketamine trial, we applied machine learning to predict treatment response.

[Chem] In situ cryogenic X-ray photoelectron spectroscopy unveils metastable components of the solid electrolyte interphase in Li-ion batteries

Chem — Tue, 16 Dec 2025 00:00:00 GMT

Nguyen et al. integrate cryogenic X-ray photoelectron spectroscopy (XPS) with residual gas analysis to study the solid electrolyte interphase (SEI) on graphite anodes in Li-ion batteries. The cryo-state preserves metastable SEI components, such as LiPOxFy, which decompose into stable products, such as LiF, with gas release upon warming. Discussions highlight critical XPS conditions, including ultrahigh vacuum exposure, X-ray-induced damage, and neutralizer settings, which could alter the detection and characterization of SEI components.

[RSC - Digital Discovery latest articles] Deep learning based SEM image analysis for predicting ionic conductivity in LiZr2(PO4)3-based solid electrolytes

RSC - Digital Discovery latest articles — Mon, 15 Dec 2025 00:00:00 GMT

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00232J, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Kento Murakami, Yudai Yamaguchi, Yo Kato, Kazuki Ishikawa, Naoto Tanibata, Hayami Takeda, Masanobu Nakayama, Masayuki Karasuyama
Lithium-ion-conductive oxide materials have attracted considerable attention as solid electrolytes for all-solid-state batteries.
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[RSC - Digital Discovery latest articles] Hierarchical Attention Graph Learning with LLMs Enhancement for Molecular Solubility Prediction

RSC - Digital Discovery latest articles — Mon, 15 Dec 2025 00:00:00 GMT

Digital Discovery, 2025, Accepted Manuscript
DOI: 10.1039/D5DD00407A, Paper

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Yangxin Fan, Yinghui Wu, Roger French, Danny Perez, Michael Taylor, Ping Yang
Solubility quantifies the concentration of a molecule that can dissolve in a given solvent. Accurate prediction of solubility is essential for optimizing drug efficacy, improving chemical and separation processes, waste...
The content of this RSS Feed (c) The Royal Society of Chemistry

[iScience] Interpretable machine learning for accessible dysphagia screening and staging in older adults

iScience — Mon, 15 Dec 2025 00:00:00 GMT

Gastroenterology; Health sciences; Internal medicine; Medical specialty; Medicine

[Joule] Dendrite suppression in garnet electrolytes via thermally induced compressive stress

Joule — Mon, 15 Dec 2025 00:00:00 GMT

Thermal gradients induce compressive stress in garnet solid electrolytes, mechanically toughening them against lithium penetration. The resulting 3-fold increase in critical current density demonstrates that stress engineering can increase critical current densities in solid-state batteries and isolates the role of mechanical stress as a dominant factor in dendrite suppression.

[Wiley: Advanced Energy Materials: Table of Contents] Machine Learning Descriptors for Mapping Structure‐Property‐Performance Relationships of Perovskite Solar Cells

Wiley: Advanced Energy Materials: Table of Contents — Sat, 13 Dec 2025 07:01:43 GMT

Advanced Energy Materials, EarlyView.

[ACS Materials Letters: Latest Articles (ACS Publications)] [ASAP] Reaction Pathway Informed Strategy for Fast Solid-State Synthesis of Garnet-Type Solid Electrolyte

ACS Materials Letters: Latest Articles (ACS Publications) — Fri, 12 Dec 2025 14:10:55 GMT

ACS Materials Letters

DOI: 10.1021/acsmaterialslett.5c01262

[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Structural Aspects, Ionic Conductivity, and Electrochemical Properties of New Bromine-Substituted Alkali-Based Crystalline Phases MTa(Nb)X6–yBry (M = Li, Na, K; X = Cl, F)

ACS Energy Letters: Latest Articles (ACS Publications) — Fri, 12 Dec 2025 13:47:45 GMT

ACS Energy Letters

DOI: 10.1021/acsenergylett.5c02904

[APL Machine Learning Current Issue] Smart detection of plant nutrient deficiencies using machine learning and image fusion

APL Machine Learning Current Issue — Fri, 12 Dec 2025 00:00:00 GMT

Plant nutrient deficiencies are a continual challenge for enhancing global crop output and food security. Historically, manual inspections have been plagued by subjectivity, inefficiency, and restricted scalability, necessitating the development of enhanced detection algorithms. This research introduces a novel approach utilizing image processing and machine learning to enhance detection accuracy and practical applicability. This strategy promotes classification stability by integrating machine learning classifiers, including k-Nearest Neighbors, Artificial Neural Networks, Decision Trees, and Linear Discriminant Analysis, with fusion techniques such as Majority Voting and Mean Fusion. The experiments utilize Leave-One-Out Cross-Validation for model evaluation to address dataset variability and deliver thorough assessments. The study’s results indicate that the suggested system surpasses existing systems in accuracy, precision, recall, and F1 score, attaining an overall accuracy of 98.17%. The method is effective across various noise and resolution parameters, allowing for scalability in precision agriculture applications. This discovery not only enhances the diagnosis of plant nutrient deficiencies but also enables further investigations into real-time plant health monitoring.

[RSC - Digital Discovery latest articles] PEMD: a high-throughput simulation and analysis framework for solid polymer electrolytes

RSC - Digital Discovery latest articles — Fri, 12 Dec 2025 00:00:00 GMT

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00454C, Paper

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Shendong Tan, Bochun Liang, Dexin Lu, Chaoyuan Ji, Wenke Ji, Zihui Li, Tingzheng Hou
PEMD is an open-source Python framework that integrates polymer construction, force-field parameterization, multiscale simulation, and property analysis, with standardized workflows for screening and data-driven design of solid polymer electrolytes.
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[AI for Science - latest papers] Investigating CO adsorption on Cu(111) and Rh(111) surfaces using machine learning exchange-correlation functionals

AI for Science - latest papers — Fri, 12 Dec 2025 00:00:00 GMT

The ‘CO adsorption puzzle’, a persistent failure of utilizing generalized gradient approximations in density functional theory to replicate CO’s experimental preference for top-site adsorption on transition-metal surfaces, remains a critical barrier in surface chemistry. While hybrid functionals such as HSE06 partially resolve this discrepancy, their prohibitive computational cost limits broader applications. We tackle this issue by adopting the Deep Kohn–Sham (DeePKS) method to train machine-learned exchange-correlation functionals. Principal component analysis reveals that the input descriptors for electronic structures separate distinctly across different chemical environments, enabling the DeePKS models to generalize to multi-element systems. We train system-specific DeePKS models for transition-metal surfaces Cu(111) and Rh(111). These models successfully recover experimental site preferences, yielding adsorption energy differences of about 10 meV compared to HSE06, and can be applied to study unseen adsorbate coverages. Furthermore, a single model for the two surfaces is trained, and the model achieves comparable accuracy in predicting not only adsorption energies and site preference but also potential energy surfaces and relaxed surface adsorption structures. The work demonstrates a promising path towards universal models, enabling catalyst exploration with hybrid functional accuracy at substantially reduced cost.

[iScience] Consensus machine learning identifies cell death gene signature for carotid artery stenosis diagnosis

iScience — Fri, 12 Dec 2025 00:00:00 GMT

Carotid artery stenosis (CAS) is a major contributor to ischemic stroke, and molecular tools for its early detection remain limited. To address this need, we integrated one in-house RNA-seq cohort with eight public datasets comprising 696 samples, together with proteomic profiling, qRT-PCR, single-cell sequencing, and FYCO1 silencing experiments. From 1,258 curated cell death–related genes, candidates were filtered by logistic regression across cohorts, and ten machine learning algorithms were combined into 105 model configurations to derive a consensus diagnostic classifier.

[Wiley: Advanced Science: Table of Contents] A Cost‐Effective and Scalable Machine Learning Approach for Quality Assessment of Fresh Maize Kernel Using NIR Spectroscopy

Wiley: Advanced Science: Table of Contents — Thu, 11 Dec 2025 09:23:00 GMT

Advanced Science, Volume 12, Issue 46, December 11, 2025.

[Wiley: Advanced Science: Table of Contents] High‐Performance Zinc–Bromine Rechargeable Batteries Enabled by In‐Situ Formed Solid Electrolyte Interphase

Wiley: Advanced Science: Table of Contents — Thu, 11 Dec 2025 09:23:00 GMT

Advanced Science, Volume 12, Issue 46, December 11, 2025.

[Wiley: Advanced Science: Table of Contents] Nonalcoholic Fatty Liver Disease Exacerbates the Advancement of Renal Fibrosis by Modulating Renal CCR2+PIRB+ Macrophages Through the ANGPTL8/PIRB/ALOX5AP Axis

Wiley: Advanced Science: Table of Contents — Thu, 11 Dec 2025 09:23:00 GMT

Advanced Science, Volume 12, Issue 46, December 11, 2025.

[Wiley: Advanced Science: Table of Contents] Inverse Design of Metal‐Organic Frameworks for CH4/N2 Separation Enabled by Coupled Machine Learning and Genetic Algorithms

Wiley: Advanced Science: Table of Contents — Thu, 11 Dec 2025 09:23:00 GMT

Advanced Science, Volume 12, Issue 46, December 11, 2025.

[Wiley: Advanced Science: Table of Contents] Synergistic Effect of Dual‐Functional Groups in MOF‐Modified Separators for Efficient Lithium‐Ion Transport and Polysulfide Management of Lithium‐Sulfur Batteries

Wiley: Advanced Science: Table of Contents — Thu, 11 Dec 2025 09:23:00 GMT

Advanced Science, Volume 12, Issue 46, December 11, 2025.

[Proceedings of the National Academy of Sciences: Physical Sciences] Evaluating large language models in biomedical data science challenges through a classroom experiment

Proceedings of the National Academy of Sciences: Physical Sciences — Thu, 11 Dec 2025 08:00:00 GMT

Proceedings of the National Academy of Sciences, Volume 122, Issue 50, December 2025.
SignificanceLarge language models (LLMs) are increasingly used in science and engineering, yet their real-world effectiveness in data analysis remains unclear. In this study, graduate students used LLMs to tackle biomedical data challenges on Kaggle, a ...

[Wiley: Advanced Functional Materials: Table of Contents] Red Phosphorus@SnSe0.5S0.5 Core‐Shell Heterostructure Enables Superior Sodium Storage via Synergistic Ion Diffusion and Polyphosphides Trapping

Wiley: Advanced Functional Materials: Table of Contents — Thu, 11 Dec 2025 06:58:45 GMT

Advanced Functional Materials, Volume 35, Issue 50, December 9, 2025.

[Wiley: Advanced Functional Materials: Table of Contents] Dual‐Site Ni Nanoparticles‐Ru Clusters Anchored on Hierarchical Carbon with Decoupled Gas and Ion Diffusion Channels Enabling Low‐Overpotential, Highly Stable Li‐CO2 Batteries

Wiley: Advanced Functional Materials: Table of Contents — Thu, 11 Dec 2025 06:58:45 GMT

Advanced Functional Materials, Volume 35, Issue 50, December 9, 2025.

[RSC - Digital Discovery latest articles] Toward smart CO2 capture by the synthesis of metal organic frameworks using large language models

RSC - Digital Discovery latest articles — Thu, 11 Dec 2025 00:00:00 GMT

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00446B, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Hossein Mashhadimoslem, Mohammad Ali Abdol, Kourosh Zanganeh, Ahmed Shafeen, Encheng Liu, Sohrab Zendehboudi, Ali Elkamel, Aiping Yu
This research focuses on collecting experimental CO₂ adsorption data from 433 scientific papers to address the challenges of MOF synthesis methods and the correlation of MOF structure and the effect of their structure on CO₂ adsorption using LLMs.
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[Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents] Tailoring Thermophysical Properties and Multiscale Machine Learning Modeling of 2D Nanomaterial‐Infused Beeswax as a Green NePCM for Sustainable Thermal Management Systems

Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents — Wed, 10 Dec 2025 09:54:56 GMT

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.

[RSC - Digital Discovery latest articles] Optimizing Data Extraction from Materials Science Literature: A Study of Tools Using Large Language Models

RSC - Digital Discovery latest articles — Wed, 10 Dec 2025 00:00:00 GMT

Digital Discovery, 2025, Accepted Manuscript
DOI: 10.1039/D5DD00482A, Paper

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Wenkai Ning, Jeffrey Robert Reimers, Musen Li, Rika Kobayashi
Large Language Models (LLMs) are increasingly utilized for large-scale extraction and organization of unstructured data owing to their exceptional Natural Language Processing (NLP) capabilities. Empowering materials design, vast amounts of...
The content of this RSS Feed (c) The Royal Society of Chemistry

[RSC - Digital Discovery latest articles] Optimizing data extraction from materials science literature: a study of tools using large language models

RSC - Digital Discovery latest articles — Wed, 10 Dec 2025 00:00:00 GMT

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00482A, Paper

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Wenkai Ning, Musen Li, Jeffrey R. Reimers, Rika Kobayashi
Benchmarking five AI tools on materials science literature shows promising capabilities, but performance remains inadequate for large-scale data extraction. Our analysis offers detailed insight and guidance for future methodological improvements.
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[RSC - Chem. Sci. latest articles] A solid composite electrolyte based on three-dimensional structured zeolite networks for high-performance solid-state lithium metal batteries

RSC - Chem. Sci. latest articles — Sun, 07 Dec 2025 00:00:00 GMT

Chem. Sci., 2026, Advance Article
DOI: 10.1039/D5SC05786H, Edge Article

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Zhaodi Luo, Yuxin Cui, Zixuan Zhang, Malin Li, Jihong Yu
We report a composite solid electrolyte, 3D Zeo/PEO, constructed by integrating a 3D zeolite network into a LiTFSI–PEO matrix, which boosts the performance of batteries by regulating the Li⁺ conduction and deposition, as well as SEI formation.
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[Proceedings of the National Academy of Sciences: Physical Sciences] Local equations describe unreasonably efficient stochastic algorithms in random K-SAT

Proceedings of the National Academy of Sciences: Physical Sciences — Fri, 05 Dec 2025 08:00:00 GMT

Proceedings of the National Academy of Sciences, Volume 122, Issue 49, December 2025.
SignificanceThe difficulties of algorithmic dynamics in highly nonconvex landscapes are central in several research areas, from hard combinatorial optimization to machine learning. However, it is unclear why and how some particular algorithms find ...

[ACS Materials Letters: Latest Articles (ACS Publications)] [ASAP] Beyond Conventional Sodium Superionic Conductor: Fe-Substituted Na3V2(PO4)2F3 Cathodes with Accelerated Charge Transport via Polyol Reflux for Sodium-Ion Batteries

ACS Materials Letters: Latest Articles (ACS Publications) — Thu, 04 Dec 2025 13:33:58 GMT

ACS Materials Letters

DOI: 10.1021/acsmaterialslett.5c01502

[Wiley: Advanced Science: Table of Contents] Non‐Monotonic Ion Conductivity in Lithium‐Aluminum‐Chloride Glass Solid‐State Electrolytes Explained by Cascading Hopping

Wiley: Advanced Science: Table of Contents — Thu, 04 Dec 2025 08:00:00 GMT

Advanced Science, Volume 12, Issue 45, December 4, 2025.

[Wiley: Advanced Science: Table of Contents] Re‐Purposing a Modular Origami Manipulator Into an Adaptive Physical Computer for Machine Learning and Robotic Perception

Wiley: Advanced Science: Table of Contents — Thu, 04 Dec 2025 08:00:00 GMT

Advanced Science, Volume 12, Issue 45, December 4, 2025.

[Wiley: Advanced Materials: Table of Contents] Ultrastable Calcium Metal Anodes Enabled by a Strongly Coordinated Electrolyte Derived Bilayer Solid Electrolyte Interphase

Wiley: Advanced Materials: Table of Contents — Thu, 04 Dec 2025 07:04:36 GMT

Advanced Materials, Volume 37, Issue 48, December 3, 2025.

[RSC - Digital Discovery latest articles] Understanding and mitigating distribution shifts for universal machine learning interatomic potentials

RSC - Digital Discovery latest articles — Thu, 04 Dec 2025 00:00:00 GMT

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00260E, Paper

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Tobias Kreiman, Aditi S. Krishnapriyan
We find common distribution shifts that pose challenges for universal machine learning interatomic potentials (MLIPs). We develop test-time refinement strategies that mitigate the shifts and provide insights into why MLIPs struggle to generalize.
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[iScience] Physical Cognition in Altered Gravity: Link Between Sensorimotor and Cognitive Adaptability

iScience — Thu, 04 Dec 2025 00:00:00 GMT

A hallmark of human intelligence is rapid adaptation to changing environments. Yet the link between sensorimotor recalibration to new physical conditions and cognitive updating of internal models remains unclear. We addressed this using altered gravity as a model system. In a within-subject study, 25 adults completed a virtual-reality task requiring motor adjustment to non-terrestrial gravities and an online problem-solving task requiring physical reasoning under matched gravity manipulations. Adaptability in each domain was computed relative to performance under terrestrial gravity.

[Wiley: Small: Table of Contents] Label‐Free Detection of Nuclear Envelope Nucleoporation using 2D Morphological Embeddings and Machine Learning

Wiley: Small: Table of Contents — Wed, 03 Dec 2025 15:24:49 GMT

Small, Volume 21, Issue 48, December 3, 2025.

[Wiley: Advanced Functional Materials: Table of Contents] Machine Learning‐Enabled Polymer Discovery for Enhanced Pulmonary siRNA Delivery

Wiley: Advanced Functional Materials: Table of Contents — Wed, 03 Dec 2025 08:00:00 GMT

Advanced Functional Materials, Volume 35, Issue 49, December 2, 2025.

[Wiley: Advanced Functional Materials: Table of Contents] Enhanced Potassium Ion Diffusion and Interface Stability Enabled by Potassiophilic rGO/CNTs/NaF Micro‐Lattice Aerogel for High‐Performance Potassium Metal Batteries

Wiley: Advanced Functional Materials: Table of Contents — Wed, 03 Dec 2025 08:00:00 GMT

Advanced Functional Materials, Volume 35, Issue 49, December 2, 2025.

[Nature Reviews Physics] Predicting high-entropy alloy phases with machine learning

Nature Reviews Physics — Wed, 03 Dec 2025 00:00:00 GMT

Nature Reviews Physics, Published online: 03 December 2025; doi:10.1038/s42254-025-00903-8

Omokhuwele Umoru explains how generative adversarial networks can help to predict the phases of high-entropy alloys.

[iScience] AI enhancing differential diagnosis of acute chronic obstructive pulmonary disease and acute heart failure

iScience — Wed, 03 Dec 2025 00:00:00 GMT

Cardiovascular medicine; Respiratory medicine; Machine learning

[iScience] United multi-omics and machine learning refine regulatory T cell-defined hepatocellular carcinoma subtypes

iScience — Wed, 03 Dec 2025 00:00:00 GMT

Hepatocellular carcinoma (HCC) is highly heterogeneous and aggressive, and the absence of precision individual treatment regimen enables repeated immune escape. Exploiting Treg-marker genes as key classifiers, we used 10 clustering algorithms to integrate the multi-omics HCC patients data and combined with 10 machine learning (ML) algorithms to delineate molecular subtypes to define molecular subtypes predictive of prognosis and immune response. We identified two cancer subtypes (CSs) that are associated with prognosis, with the second subtype (CS2) showing the most favourable prognostic outcomes.

[Matter] Unknowium, beyond the banana, and AI discovery in materials science

Matter — Wed, 03 Dec 2025 00:00:00 GMT

Recently, the proportion of papers implementing some sort of artificial intelligence (AI) or machine learning (ML) methods in materials science has been growing. It’s hard to ignore such a powerful and exciting tool. Relatedly, I have just returned from the Pujiang Innovation Forum held in Shanghai, China, where I participated in the “AI for Materials Science” session (Figure 1A), speaking as a lowly editor among global experts in the field.

[Wiley: Advanced Energy Materials: Table of Contents] Taming Metal–Solid Electrolyte Interface Instability via Metal Strain Hardening

Wiley: Advanced Energy Materials: Table of Contents — Tue, 02 Dec 2025 13:55:59 GMT

Advanced Energy Materials, Volume 15, Issue 45, December 2, 2025.

[Wiley: Advanced Energy Materials: Table of Contents] Self‐Liquefying Conformal Nanocoatings via Phase‐Convertible Ion Conductors for Stable All‐Solid‐State Batteries (Adv. Energy Mater. 45/2025)

Wiley: Advanced Energy Materials: Table of Contents — Tue, 02 Dec 2025 13:55:59 GMT

Advanced Energy Materials, Volume 15, Issue 45, December 2, 2025.

[Wiley: Advanced Energy Materials: Table of Contents] Multiscale Design Strategies of Interface‐Stabilized Solid Electrolytes and Dynamic Interphase Decoding from Atomic‐to‐Macroscopic Perspectives

Wiley: Advanced Energy Materials: Table of Contents — Tue, 02 Dec 2025 13:55:59 GMT

Advanced Energy Materials, Volume 15, Issue 45, December 2, 2025.

[Wiley: Advanced Energy Materials: Table of Contents] Self‐Liquefying Conformal Nanocoatings via Phase‐Convertible Ion Conductors for Stable All‐Solid‐State Batteries

Wiley: Advanced Energy Materials: Table of Contents — Tue, 02 Dec 2025 13:55:59 GMT

Advanced Energy Materials, Volume 15, Issue 45, December 2, 2025.

[Chinese Chemical Society: CCS Chemistry: Table of Contents] LoRA-Chem: Modular Machine Learning for Multitask Prediction in Organic Reactions

Chinese Chemical Society: CCS Chemistry: Table of Contents — Tue, 02 Dec 2025 04:48:31 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202506542

The prediction of organic chemical reactions has historically presented significant challenges owing to the inherent complexity and mechanistic diversity of reaction processes. In this work, we present LoRA-Chem, an innovative modular framework that ...

[Chinese Chemical Society: CCS Chemistry: Table of Contents] LoRA-Chem: Modular Machine Learning for Multitask Prediction in Organic Reactions

Chinese Chemical Society: CCS Chemistry: Table of Contents — Tue, 02 Dec 2025 04:48:31 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202506542

[iScience] Dimensionality modulated generative AI for safe biomedical dataset augmentation

iScience — Tue, 02 Dec 2025 00:00:00 GMT

Generative artificial intelligence can expand small biomedical datasets but may amplify noise and distort statistical relationships. We developed genESOM, a framework integrating an error control system into a generative AI method based on emergent self-organizing maps. By separating structure learning from data synthesis, genESOM enables dimensionality modulation and injection of engineered diagnostic features, i.e., permuted versions of real variables, as negative controls that track feature importance stability.

[Wiley: Advanced Intelligent Discovery: Table of Contents] Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Wiley: Advanced Intelligent Discovery: Table of Contents — Mon, 01 Dec 2025 22:39:43 GMT

Advanced Intelligent Discovery, EarlyView.

[APL Machine Learning Current Issue] RTNinja : A generalized machine learning framework for analyzing random telegraph noise signals in nanoelectronic devices

APL Machine Learning Current Issue — Mon, 01 Dec 2025 00:00:00 GMT

Random telegraph noise is a prevalent variability phenomenon in nanoelectronic devices, arising from stochastic carrier exchange at defect sites and critically impacting device reliability and performance. Conventional analysis techniques often rely on restrictive assumptions or manual interventions, limiting their applicability to complex, noisy datasets. Here, we introduce RTNinja, a generalized, fully automated machine learning framework for the unsupervised analysis of random telegraph noise signals. RTNinja deconvolves complex signals to identify the number and characteristics of hidden individual sources without requiring prior knowledge of the system. The framework comprises two modular components: LevelsExtractor, which uses Bayesian inference and model selection to denoise and discretize the signal, and SourcesMapper, which infers source configurations through probabilistic clustering and optimization. To evaluate performance, we developed a Monte Carlo simulator that generates labeled datasets spanning broad signal-to-noise ratios and source complexities; across 7000 such datasets, RTNinja consistently demonstrated high-fidelity signal reconstruction and accurate extraction of source amplitudes and activity patterns. Our results demonstrate that RTNinja offers a robust, scalable, and device-agnostic tool for random telegraph noise characterization, enabling large-scale statistical benchmarking, reliability-centric technology qualification, predictive failure modeling, and device physics exploration in next-generation nanoelectronics.

[iScience] A pilot study: Incorporating Treponema pallidum antigens into machine learning models for accurate syphilis treatment outcome assessment

iScience — Mon, 01 Dec 2025 00:00:00 GMT

Health informatics; disease; artificial intelligence applications

[iScience] Bathymetry of the Philippine sea with convolution neural network from multisource marine geodetic data

iScience — Fri, 28 Nov 2025 00:00:00 GMT

Earth sciences; oceanography; geodesy; machine learning

[iScience] Interpretable machine learning for urothelial cells classification and risk scoring in urine cytology

iScience — Thu, 27 Nov 2025 00:00:00 GMT

Health sciences; Cancer; Machine learning

[Wiley: Advanced Intelligent Discovery: Table of Contents] Machine Learning‐Assisted Second‐Order Perturbation Theory for Chemical Potential Correction Toward Hubbard U Determination

Wiley: Advanced Intelligent Discovery: Table of Contents — Wed, 26 Nov 2025 03:49:32 GMT

Advanced Intelligent Discovery, EarlyView.

[Wiley: Chinese Journal of Chemistry: Table of Contents] Locked Coplanar Conformation Boosts Rapid Electron/Ion Transport in Linear Polyimide Cathodes for Sodium‐Ion Storage

Wiley: Chinese Journal of Chemistry: Table of Contents — Mon, 24 Nov 2025 07:33:36 GMT

Chinese Journal of Chemistry, EarlyView.

[APL Machine Learning Current Issue] A hybrid neural architecture: Online attosecond x-ray characterization

APL Machine Learning Current Issue — Fri, 21 Nov 2025 00:00:00 GMT

The emergence of high-repetition-rate x-ray free-electron lasers (XFELs), such as SLAC’s LCLS-II, serves as our canonical example for autonomous controls that necessitate high-throughput diagnostics paired with streaming computational pipelines capable of single-shot analysis with extremely low latency. We present the deterministic characterization with an integrated parallelizable hybrid resolver architecture, a hybrid machine learning framework designed for fast, accurate analysis of XFEL diagnostics using angular streaking-based sinogram images. This architecture integrates convolutional neural networks and bidirectional long short-term memory models to denoise input, identify x-ray sub-spike features, and extract sub-spike relative delays with sub-30 attosecond temporal resolution. Deployed on low-latency hardware, it achieves over 10 kHz throughput with 168.3 μs inference latency, indicating scalability to 14 kHz with field-programmable gate array integration. By transforming regression tasks into classification problems and leveraging optimized error encoding, we achieve high precision with low-latency performance that is critical for real-time streaming event selection and experimental control feedback signals. This represents a key development in real-time control pipelines for next-generation autonomous science, generally, and high repetition-rate x-ray experiments in particular.

[Joule] Accelerated discovery of CO2-to-C3-hydrocarbon electrocatalysts with human-in-the-loop

Joule — Fri, 21 Nov 2025 00:00:00 GMT

Despite advances in automation and AI, accelerating discovery in heterogeneous electrocatalysts remains hindered by the experimental challenges of building integrated platforms for synthesis and evaluation, as well as limited performance-relevant data. This work integrates accelerated experimentation, machine learning, and domain expertise to efficiently explore CO2-to-C3 electrocatalysts, adding new mechanistic and data-driven insights to energy science.

[Joule] Redox-mediated solid-state doping of Spiro-OMeTAD for efficient and robust perovskite photovoltaics

Joule — Fri, 21 Nov 2025 00:00:00 GMT

The volatile and unstable nature of conventional dopants severely limits the performance and operational lifetime of perovskite solar cells. Here, we demonstrate a solid-state doping strategy, enabling a uniform nanoscale doping profile while effectively suppressing ion migration. This strategy yields perovskite solar cells with a certified efficiency of 26.34% and high device stability.

[AI for Science - latest papers] Learning to be simple

AI for Science - latest papers — Thu, 20 Nov 2025 00:00:00 GMT

In this work we employ machine learning to understand structured mathematical data involving finite groups and derive a theorem about necessary properties of generators of finite simple groups. We create a database of all two-generated subgroups of the symmetric group on n-objects and conduct a classification of finite simple groups among them using shallow feed-forward neural networks. We show that this neural network classifier can decipher the property of simplicity with varying accuracies depending on the features. Our neural network model leads to a natural conjecture concerning the generators of a finite simple group. We subsequently prove this conjecture. This new toy theorem comments on the necessary properties of generators of finite simple groups. We show this explicitly for a class of sporadic groups for which the result holds. Our work further makes the case for a machine motivated study of algebraic structures in pure mathematics and highlights the possibility of generating new conjectures and theorems in mathematics with the aid of machine learning.

[Wiley: Advanced Intelligent Discovery: Table of Contents] Taguchi–Bayesian Sampling: A Roadmap for Polymer Database Construction Toward Small Representative Machine Learning

Wiley: Advanced Intelligent Discovery: Table of Contents — Wed, 19 Nov 2025 05:00:22 GMT

Advanced Intelligent Discovery, EarlyView.

[iScience] An explainable machine learning model predicts 30-day readmission after vertebral augmentation

iScience — Wed, 19 Nov 2025 00:00:00 GMT

Orthopedics; Machine learning

[Wiley: SmartMat: Table of Contents] Machine Learning Design of Soft Magnetoresponsive Materials With Multidirectional Fields

Wiley: SmartMat: Table of Contents — Tue, 18 Nov 2025 08:00:00 GMT

SmartMat, Volume 6, Issue 6, December 2025.

[RSC - Digital Discovery latest articles] Advancing metal organic framework and covalent organic framework design via the digital-intelligent paradigm

RSC - Digital Discovery latest articles — Tue, 18 Nov 2025 00:00:00 GMT

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00401B, Review Article

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Bing Ma, Na Qin, Qianqian Yan, Wei Zhou, Sheng Zhang, Xiao Wang, Lipiao Bao, Xing Lu
AI and machine learning combined with multiscale simulations accelerate framework materials design. This review summarizes AI-assisted strategies for synthesis prediction, condition optimization, and inverse functional design.
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[Applied Physics Reviews Current Issue] Synergistic integration of metasurfaces and quantum photonics: Pathways to next-generation technologies

Applied Physics Reviews Current Issue — Mon, 17 Nov 2025 00:00:00 GMT

The convergence of metamaterials and quantum optics heralds a transformative era in photonic technologies, poised to revolutionize applications ranging from information processing and imaging to sensing and beyond. This review explores the synergistic integration of metasurfaces—engineered sub-wavelength planar structures—and quantum optics, which exploits quantum mechanical principles to manipulate light at the most granular level. We outline the design principles, fabrication processes, and computational challenges involved in creating quantum metasurfaces, discussing both forward and inverse design approaches. Advances in nanofabrication and intelligent optimization techniques, such as machine learning and topology optimization, have enabled the development of metasurfaces with unparalleled control over electromagnetic waves. We examine recent progress in using quantum metasurfaces for single-photon and multi-photon generation, quantum imaging, and quantum sensing, showcasing how these innovations achieve unprecedented precision and novel functionalities. Additionally, we highlight the integration of metasurfaces into quantum light manipulation, emphasizing their role in enhancing wavefront shaping and entanglement control. By providing a comprehensive survey of current advancements and future research directions, this review highlights the vast potential of metasurfaces and quantum optics at the crossroads, setting the stage for next-generation technological innovations that will define the forthcoming decade.

[AI for Science - latest papers] Universal machine learning potentials for systems with reduced dimensionality

AI for Science - latest papers — Mon, 17 Nov 2025 00:00:00 GMT

We present a benchmark designed to evaluate the predictive capabilities of universal machine learning interatomic potentials (MLIPs) across systems of varying dimensionality. Specifically, our benchmark tests zero- (molecules, atomic clusters, etc), one- (nanowires, nanoribbons, nanotubes, etc), two- (atomic layers and slabs) and three-dimensional (3D) (bulk materials) compounds. The benchmark reveals that while all tested models demonstrate excellent performance for 3D systems, accuracy degrades progressively for lower-dimensional structures. The best performing models for geometry optimization are orbital version 2, equiformerV2, and the equivariant Smooth Energy Network, with the equivariant Smooth Energy Network also providing the most accurate energies. Our results indicate that the best models yield, on average, errors in the atomic positions in the range of 0.01–0.02 Å and errors in the energy below 10 meV atom−1 across all dimensionalities. These results demonstrate that state-of-the-art universal MLIPs have reached sufficient accuracy to serve as direct replacements for density functional theory calculations, at a small fraction of the computational cost, in simulations spanning the full range from isolated atoms to bulk solids. More significantly, the best performing models already enable efficient simulations of complex systems containing subsystems of mixed dimensionality, opening new possibilities for modeling realistic materials and interfaces.

[Cell Reports Physical Science] Conjugated polyelectrolyte-aptamer hybrid for organic-electrochemical-transistor-based sensing

Cell Reports Physical Science — Mon, 17 Nov 2025 00:00:00 GMT

Jiang et al. present biofunctionalized organic mixed ionic-electronic conductors (OMIECs), specifically single-component materials that integrate high specificity with semiconducting properties, exemplified by p(NDI-T-ZI/EG)-aptamer. This hybrid design enables covalent attachment of diverse functional units, thereby expanding the library of sensory OMIECs for future diagnostic applications.

[Cell Reports Physical Science] CatBench framework for benchmarking machine learning interatomic potentials in adsorption energy predictions for heterogeneous catalysis

Cell Reports Physical Science — Mon, 17 Nov 2025 00:00:00 GMT

Moon et al. introduce CatBench, a framework that systematically evaluates machine learning interatomic potentials for predicting molecular adsorption from small to large molecules on catalyst surfaces. Testing 13 state-of-the-art models across ≥47,000 reactions, they identify optimal accuracy-speed trade-offs and provide quantitative guidance for selecting models for practical catalyst discovery.

[Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents] Liquid‐Phase Synthesis of Halide Solid Electrolytes for All‐Solid‐State Batteries Using Organic Solvents

Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents — Fri, 14 Nov 2025 14:05:17 GMT

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.

[AI for Science - latest papers] TorchSim: an efficient atomistic simulation engine in PyTorch

AI for Science - latest papers — Fri, 14 Nov 2025 00:00:00 GMT

We introduce TorchSim, an open-source atomistic simulation engine tailored for the machine learned interatomic potential (MLIP) era. By rewriting core atomistic simulation primitives in PyTorch, TorchSim can achieve orders of magnitude acceleration for popular MLIPs. Unlike existing molecular dynamics (MD) packages, which simulate one system at a time, TorchSim performs batched simulations that efficiently utilize modern GPUs by evolving multiple systems concurrently. TorchSim supports MD integrators, structural relaxation optimizers, both machine-learned and classical interatomic potentials (such as Lennard–Jones, Morse, soft-sphere), batching with automatic memory management, differentiable simulation, and integration with popular materials informatics tools.

[AI for Science - latest papers] Graph learning metallic glass discovery from Wikipedia

AI for Science - latest papers — Fri, 14 Nov 2025 00:00:00 GMT

Synthesizing new materials efficiently is highly demanded in various research fields. However, this process is usually slow and expensive, especially for metallic glasses, whose formation strongly depends on the optimal combinations of multiple elements to resist crystallization. This constraint renders only several thousands of candidates explored in the vast material space since 1960. Recently, data-driven approaches armed by advanced machine learning techniques provided alternative routes for intelligent materials design. Due to data scarcity and immature material encoding, the conventional tabular data is usually mined by statistical learning algorithms, giving limited model predictability and generalizability. Here, we propose sophisticated data learning from material network representations. The node elements are encoded from the Wikipedia by a language model. Graph neural networks with versatile architectures are designed to serve as recommendation systems to explore hidden relationships among materials. By employing Wikipedia embeddings from different languages, we assess the capability of natural languages in materials design. Our study proposes a new paradigm to harvesting new amorphous materials and beyond with artificial intelligence.

[ACS Materials Au: Latest Articles (ACS Publications)] [ASAP] Molecular Modeling-Based Machine Learning for Accurate Prediction of Gas Diffusivity and Permeability in Metal–Organic Frameworks

ACS Materials Au: Latest Articles (ACS Publications) — Wed, 12 Nov 2025 18:15:35 GMT

ACS Materials Au

DOI: 10.1021/acsmaterialsau.5c00111

[Recent Articles in PRX Energy] Dynamic Vacancy Levels in ${\mathrm{Cs}\mathrm{Pb}\mathrm{Cl}}_{3}$ Obey Equilibrium Defect Thermodynamics

Recent Articles in PRX Energy — Wed, 12 Nov 2025 10:00:00 GMT

Author(s): Irea Mosquera-Lois and Aron Walsh

This study of halide perovskites uses advanced molecular dynamics simulations with machine learning force fields to identify dynamic defect levels and their impact on the material’s optoelectronic properties.

[PRX Energy 4, 043008] Published Wed Nov 12, 2025

[Wiley: Advanced Intelligent Discovery: Table of Contents] Accelerating Primary Screening of USP8 Inhibitors from Drug Repurposing Databases with Tree‐Based Machine Learning

Wiley: Advanced Intelligent Discovery: Table of Contents — Tue, 11 Nov 2025 04:16:54 GMT

Advanced Intelligent Discovery, EarlyView.

[Joule] Entropy-guided discovery of denary trirutile antimonates for electrocatalytic chlorine evolution

Joule — Tue, 11 Nov 2025 00:00:00 GMT

Entropy-guided trirutile antimonates are developed as highly efficient catalysts for the chlorine evolution reaction. By integrating machine learning, DFT calculations, and operando experiments, this work uncovers atomic-level mechanisms governing catalytic activity and stability.

[AI for Science - latest papers] Surface stability modeling with universal machine learning interatomic potentials: a comprehensive cleavage energy benchmarking study

AI for Science - latest papers — Fri, 07 Nov 2025 00:00:00 GMT

Machine learning interatomic potentials (MLIPs) have revolutionized computational materials science by bridging the gap between quantum mechanical accuracy and classical simulation efficiency, enabling unprecedented exploration of materials properties across the periodic table. Despite their remarkable success in predicting bulk properties, no systematic evaluation has assessed how well these universal MLIPs (uMLIPs) can predict cleavage energies, a critical property governing fracture, catalysis, surface stability, and interfacial phenomena. Here, we present a comprehensive benchmark of 19 state-of-the-art uMLIPs for cleavage energy prediction using our previously established density functional theory database of 36 718 slab structures spanning elemental, binary, and ternary metallic compounds. We evaluate diverse architectural paradigms, analyzing their performance across chemical compositions, crystal systems, thickness, and surface orientations. Our zero-shot evaluation results reveal that training data composition dominates architectural sophistication: models trained on the Open Materials 2024 (OMat24) dataset, which emphasizes non-equilibrium configurations, achieve mean absolute percentage errors below 6% and correctly identify the thermodynamically most stable surface terminations in 87% of cases, without any explicit surface energy training. In contrast, architecturally identical models trained on equilibrium-only datasets show five-fold higher errors, while models trained on surface-adsorbate data fail catastrophically with a 17-fold degradation. Remarkably, simpler architectures trained on appropriate data achieve comparable accuracy to complex transformers while offering 10–100× computational speedup. These findings fundamentally reframe MLIP development priorities: highlighting that strategic training-data generation with appropriate non-equilibrium sampling deserves equal or greater attention than architectural complexity.

[Joule] Li–Si compound anodes enabling high-performance all-solid-state Li-ion batteries

Joule — Fri, 07 Nov 2025 00:00:00 GMT

Li–Si compound anodes, exemplified by Li2.33Si, overcome the degradation issues of conventional Si anodes by combining high ionic and electronic conductivity, favorable mechanical properties, and a negligible-volume-change Li-storage mechanism (Li2.33 + αSi, 0 < α < 0.92), thereby enabling high areal capacity, long cycle life, and fast rate capability in all-solid-state Li-ion batteries.

[ACS Physical Chemistry Au: Latest Articles (ACS Publications)] [ASAP] Machine Learning as a Method for Retrieving Pressure Values by Analyzing Spectral Line Parameters: The Hydrochloric Acid Case

ACS Physical Chemistry Au: Latest Articles (ACS Publications) — Tue, 04 Nov 2025 19:09:10 GMT

ACS Physical Chemistry Au

DOI: 10.1021/acsphyschemau.5c00097

[Applied Physics Reviews Current Issue] Dynamic landscape of chemiresistive breathomic nanosensors based on fifth-generation chips for complex disease diagnosis and healthcare monitoring

Applied Physics Reviews Current Issue — Tue, 04 Nov 2025 00:00:00 GMT

The growing global population is overwhelming the existing medical infrastructure, demanding a pressing need for the advancement of early-stage and point-of-care disease diagnostics. Conventional techniques are mostly invasive, time-consuming, expensive, sophisticated, and centered at urban facilities. Moreover, they are unable to address the biological complexities related to critical diseases, disorders, and pandemics, resulting in associated high morbidity and mortality. To address this gap, miniaturized fifth-generation sensing chips provide alternatives in terms of accessibility, affordability, and adaptability, being point-of-care and minimally invasive diagnostics. In this context, Breathomic chips based on nanoscale semiconductors have shown their potential for noninvasive, personalized, and on-site operation, offering the capability to identify volatile organic compounds/gases as disease biomarkers from exhaled breath and enabling early disease detection. However, the practical implementation of these sensors in real-time medical contexts remains challenging due to factors including the lack of clinical trials, dedicated data analysis, understanding of the complexities, public awareness, scalability, and accessibility. This comprehensive review critically summarizes the landscape of breath biomarkers detecting fifth-generation chemiresistive chips for human disease diagnosis, methodically outlining associated challenges, alternative strategies, and prospects for clinical implementations and commercial advancement. It details the biological origins of biomarkers, the diverse sensing modalities, and the underlying mechanisms pertaining to breathomic biomarker diagnosis. Furthermore, it highlights the integration of digital-age technologies, including nanotechnology, artificial intelligence, bioinformatics, and machine learning, for high-performance breathomic chips. These next-generation smart sensory chips have the potential to revolutionize medical healthcare facilities, improving patient outcomes, understanding prognosis, and aiding the UN's sustainable development goals.

[tandf: Materials Research Letters: Table of Contents] Machine learning-assisted design of strong and ductile BCC high-entropy alloys

tandf: Materials Research Letters: Table of Contents — Thu, 30 Oct 2025 12:22:23 GMT

Volume 13, Issue 12, December 2025, Page 1260-1268
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[Wiley: InfoMat: Table of Contents] Delicate design of lithium‐ion bridges in hybrid solid electrolyte for wide‐temperature adaptive solid‐state lithium metal batteries

Wiley: InfoMat: Table of Contents — Wed, 29 Oct 2025 00:36:10 GMT

InfoMat, EarlyView.

[APL Machine Learning Current Issue] Building an affordable self-driving lab: Practical machine learning experiments for physics education using Internet-of-Things

APL Machine Learning Current Issue — Wed, 29 Oct 2025 00:00:00 GMT

Machine learning (ML) is transforming modern physics research, but practical, hands-on experience with ML techniques remains limited due to cost and complexity barriers. To address this gap, we introduce an affordable, autonomous, Internet-of-Things (IoT)-enabled experimental platform designed specifically for applied physics education. Utilizing an Arduino microcontroller, a customizable multi-wavelength light emitting diode array, and photosensors, our setup generates diverse, real-time optical datasets ideal for training and evaluating foundational ML algorithms, including traversal methods, Bayesian inference, and deep learning. The platform facilitates a closed-loop, self-driving experimental workflow, encompassing automated data collection, preprocessing, model training, and validation. Through systematic performance comparisons, we demonstrate the superior ability of deep learning to capture complex nonlinear relationships compared to traversal and Bayesian methods. At ∼$60, this open-source IoT platform provides an accessible, practical pathway for students to master advanced ML concepts, promoting deeper conceptual insights and essential technical skills required for the next generation of physicists and engineers.

[APL Machine Learning Current Issue] Data integration and data fusion approaches in self-driving labs: A perspective

APL Machine Learning Current Issue — Wed, 29 Oct 2025 00:00:00 GMT

Self-driving laboratories (SDLs) are transforming materials discovery by combining automation, machine learning, and real-time feedback. Yet, their success depends on robust data integration and fusion methods capable of handling materials data that are heterogeneous, sparse, and multi-scale. Such data span theoretical models, simulations, and experimental techniques across diverse spatial and temporal scales, creating significant challenges for interoperability and analysis. This perspective reviews the state-of-the-art techniques, including knowledge graphs, structured pipelines, multimodal machine learning, and physics-informed models, that are enabling materials science and SDLs to unify and learn from disparate data sources, identify critical challenges, and propose forward-looking directions to enhance data readiness, interoperability, and predictive power in SDLs. We also highlight emerging methods such as transformer architectures, zero-shot learning, and real-time stream processing, and discuss the critical need for more scalable, interpretable, and adaptive solutions to fully realize autonomous materials innovation. By mapping out both the current landscape and future opportunities, we argue that next-generation data integration and fusion are not just enablers but essential pillars for achieving fully autonomous, adaptive, and intelligent SDL systems capable of addressing the complexities of hierarchical and multifunctional materials.

[Applied Physics Reviews Current Issue] Strain engineering of van Hove singularity and coupled itinerant ferromagnetism in quasi-2D oxide superlattices

Applied Physics Reviews Current Issue — Tue, 28 Oct 2025 00:00:00 GMT

Engineering van Hove singularities (vHss) near the Fermi level, if feasible, offers a powerful route to control exotic quantum phases in electronic and magnetic behaviors. However, conventional approaches rely primarily on chemical and electrical doping and focus mainly on local electrical or optical measurements, limiting their applicability to coupled functionalities. In this study, a vHs-induced insulator-metal transition coupled with a ferromagnetic phase transition was empirically achieved in atomically designed quasi-2D SrRuO₃ (SRO) superlattices via epitaxial strain engineering, which has not been observed in conventional 3D SRO systems. Theoretical calculations revealed that epitaxial strain effectively modulates the strength and energy positions of vHs of specific Ru orbitals, driving correlated phase transitions in the electronic and magnetic ground states. X-ray absorption spectroscopy confirmed the anisotropic electronic structure of quasi-2D SRO modulated by epitaxial strain. Magneto-optic Kerr effect and electrical transport measurements demonstrated modulated magnetic and electronic phases. Furthermore, magneto-electrical measurements detected significant anomalous Hall effect signals and ferromagnetic magnetoresistance, indicating the presence of magnetically coupled charge carriers in the 2D metallic regime. This study establishes strain engineering as a promising platform for tuning vHss and resultant itinerant ferromagnetism of low-dimensional correlated quantum systems.

[Wiley: Advanced Intelligent Discovery: Table of Contents] Machine Learning‐Enhanced Random Matrix Theory Design for Human Immunodeficiency Virus Vaccine Development

Wiley: Advanced Intelligent Discovery: Table of Contents — Mon, 27 Oct 2025 03:21:44 GMT

Advanced Intelligent Discovery, EarlyView.

[Applied Physics Reviews Current Issue] 3D-printed lithium-metal batteries: Multiscale architectures, hybrid technologies, and monolithic integration for next-generation energy storage

Applied Physics Reviews Current Issue — Thu, 23 Oct 2025 00:00:00 GMT

Lithium-metal anodes, with their unmatched theoretical capacity (3860 mAh g⁻¹) and ultra-low electrochemical potential (−3.04 V vs standard hydrogen electrode), are pivotal for next-generation high-energy-density batteries. However, their practical deployment is hindered by persistent challenges—dendritic growth, unstable solid electrolyte interphases (SEIs), and severe volumetric expansion. Emerging as a transformative solution, three-dimensional (3D) printing enables the rational design of multiscale architectures (e.g., micro-lattice anodes and gradient-porous cathodes) and hybrid solid-state electrolytes to address these limitations. This review presents a pioneering synthesis of 3D printing's role in lithium-metal battery engineering, focusing on its capacity to regulate lithium-ion flux, stabilize SEIs, and suppress dendrite proliferation through hierarchical structural control. We systematically analyze four key additive manufacturing technologies (inkjet printing, direct ink writing, fused deposition modeling, and stereolithography), delineating their unique advantages in tailoring ion transport pathways and mechanical robustness. Furthermore, we propose multi-material co-printing strategies to resolve interfacial incompatibilities in monolithic lithium-metal batteries, a critical barrier in current research. By bridging additive manufacturing with electrochemical fundamentals, this work outlines a roadmap to harness 3D printing's full potential, addressing scalability challenges and advancing applications in aerospace, wearables, and biomedical devices where energy density and safety are paramount.

[Wiley: Advanced Intelligent Discovery: Table of Contents] Harnessing Large Language Models to Advance Microbiome Research: From Sequence Analysis to Clinical Applications

Wiley: Advanced Intelligent Discovery: Table of Contents — Tue, 21 Oct 2025 05:48:44 GMT

Advanced Intelligent Discovery, EarlyView.

[AI for Science - latest papers] AInstein: numerical Einstein metrics via machine learning

AI for Science - latest papers — Thu, 16 Oct 2025 23:00:00 GMT

A new semi-supervised machine learning package is introduced which successfully solves the Euclidean vacuum Einstein equations with a cosmological constant, without any symmetry assumptions. The model architecture contains subnetworks for each patch in the manifold-defining atlas. Each subnetwork predicts the components of a metric in its associated patch, with the relevant Einstein conditions of the form being used as independent loss components (here , where n is the dimension of the Riemannian manifold, and the Einstein constant ). To ensure the consistency of the global structure of the manifold, another loss component is introduced across the patch subnetworks which enforces the coordinate transformation between the patches, , for an appropriate analytically known Jacobian J. We test our method for the case of spheres represented by a pair of patches in dimensions 2, 3, 4, and 5. In dimensions 2 and 3, the geometries have been fully classified. However, it is unknown whether a Ricci-flat metric can exist on spheres in dimensions 4 and 5. This work hints against the existence of such a metric.

[Matter] SpectroGen: A physically informed generative artificial intelligence for accelerated cross-modality spectroscopic materials characterization

Matter — Tue, 14 Oct 2025 00:00:00 GMT

SpectroGen seamlessly couples physics-driven distribution models with a variable autoencoder to generate synthetic spectra indistinguishable from real data. By speeding up high-throughput screening, it closes the gap between AI-based materials discovery and experimental confirmation. Its flexible architecture accommodates diverse spectroscopic techniques, extending its utility across multiple scientific domains. The synergy of rapid AI-driven design and swift AI-enabled characterization expedites validation of innovative materials, bridging lab-based discovery and industry-ready applications to address urgent societal needs.

[Chem] Precisely modulating Li2CO3 coverage on Ni-rich cathode boosts sulfide solid-state lithium battery performance

Chem — Tue, 14 Oct 2025 00:00:00 GMT

It is of great importance to address the issues of stability and charge transfer at the cathode/electrolyte interface in all-solid-state lithium batteries (ASSLBs). We proposed a CO2 atmosphere treatment to precisely modulate Li2CO3 coverage on Ni-rich layered oxide cathodes (NRLOs) with minimal damage, effectively utilizing the advantageous effect while avoiding the harmful effect of surficial Li2CO3 on the interface. This fundamental mechanism offers insights for optimizing the NRLO/sulfide solid electrolyte interface and advancing high-energy-density ASSLBs.

[Matter] Dynamic pressure mapping of infant cervical spines using a wearable magnetoelastic patch

Matter — Fri, 10 Oct 2025 00:00:00 GMT

To enable continuous monitoring of infant cervical motion, we present a kirigami-inspired soft magnetoelastic patch that conforms intimately to the cervical position without compromising comfort. By leveraging passive magnetic sensing and flexible structural design, the patch captures subtle biomechanical changes during cervical movements. Integrated with machine learning classification, it enables intelligent recognition of stress patterns, providing a non-invasive and adaptive solution for early assessment of infant cervical motion.

[Matter] Machine learning-driven ligand engineering decodes and controls structural distortions in 2D perovskites

Matter — Fri, 10 Oct 2025 00:00:00 GMT

This work deciphers how ligand molecular descriptors (i.e., nitrogen content, hydrogen bonding, and π-conjugation) govern structural distortions and optoelectronic properties in 2D perovskites. The authors demonstrate that machine learning can quantitatively correlate these descriptors with octahedral distortions (92.6% prediction accuracy) and enable the targeted synthesis of six new perovskites with tunable band gaps (1.91–2.39 eV). The established structure-property relationships and machine learning-driven design paradigm represent a transformative approach for accelerating the discovery of functional perovskites, bridging computational prediction with experimental validation for optoelectronic applications.

[Applied Physics Reviews Current Issue] The enduring legacy of scanning spreading resistance microscopy: Overview, advancements, and future directions

Applied Physics Reviews Current Issue — Wed, 08 Oct 2025 00:00:00 GMT

Scanning spreading resistance microscopy (SSRM) has recently celebrated 30 years of existence when counting from the original patent of 1994. In this time, the technique has experienced an incredible journey with substantial evolutions that transformed SSRM from a small-scale experiment into a staple for chip manufacturing laboratories for physical analysis of materials, failure analysis, and process development of integrated circuits. As the nanoelectronics industry is ready for a new inflection point, with the introduction of nanosheet field-effect transistor to replace FinFETs and cell track scaling architectures such as the complementary field-effect transistors, SSRM is once again at a turning point. This review aims to highlight the state-of-the-art while discussing the emerging challenges introduced by the ever-increasing complexity in complementary metal–oxide–semiconductor (CMOS) manufacturing. We start by illustrating the unique capability of the SSRM technique, its origin, and its evolution. Next, we continue by showing the considerable research effort that enabled SSRM to transition to a tomographic sensing method in support of FinFET transistors. Here, the high aspect ratio fin geometry and the complex contacts technology have imposed important modifications to the original method. Later, we elaborate on some of the key challenges introduced by the upcoming device transition from three-sided channel FinFETs into nanosheet FETs, i.e., offering a four-sided electrostatic control of the channel. Finally, we present the use of machine learning for automation in carrier calibration with increased accuracy. We close by introducing some of the concepts that we consider promising for further extension of SSRM to obtain sub-nm structural information and doping profiles in the area of advanced FinFETs and nanosheet FET technologies, including (a) correlative analysis flow, (b) liquid-assisted probing, and (c) top–down and bottom–up multi-probe sensing schemes to merge low- and high-pressure SSRM scans.

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Oxygen Vacancy-Modulated Molybdenum Oxide/Montmorillonite Heterointerface Nanoarchitectures for Plasmon-Enhanced Solar-Osmotic Energy Harvesting

Chinese Chemical Society: CCS Chemistry: Table of Contents — Mon, 06 Oct 2025 03:22:16 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202506132

Two-dimensional (2D) nanofluidic membranes with precise ion transport kinetics hold transformative potential for next-generation ion-selective membranes, yet their practical implementation in osmotic energy conversion remains constrained by inherent ...

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Oxygen Vacancy-Modulated Molybdenum Oxide/Montmorillonite Heterointerface Nanoarchitectures for Plasmon-Enhanced Solar-Osmotic Energy Harvesting

Chinese Chemical Society: CCS Chemistry: Table of Contents — Mon, 06 Oct 2025 03:22:16 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202506132

[APL Machine Learning Current Issue] Deep learning model of myofilament cooperative activation and cross-bridge cycling in cardiac muscle

APL Machine Learning Current Issue — Fri, 03 Oct 2025 00:00:00 GMT

Cardiac muscle contraction is driven by the cross-bridge cycle, where myosin heads generate force by cyclically attaching to and pulling on actin filaments using energy from ATP. Modeling this process is central to understanding cardiac sarcomere mechanics. In this study, we developed supervised machine learning (ML) models using artificial neural networks (ANNs) to simulate cross-bridge cycling and muscle behavior under isosarcometric, isometric, and isotonic conditions. Trained on synthetic data, the ANN captured nonlinear dependencies among calcium concentration, stiffness, sarcomere length, temperature, and force output. Error analysis through histograms and unity-line scatterplots validated prediction accuracy and identified underfitting and overfitting patterns. Comparisons across ANN architectures showed how hidden layer complexity affects model generalization. The present deep learning models accurately reproduced key physiological behaviors, including steady-state force–Ca²⁺ relations, sarcomere length changes, and force–velocity relations, and matched theoretical results. This work demonstrates the potential of ML tools to enhance cardiac muscle modeling and exploit existing experimental datasets for improved prediction of cardiac muscle diseases.

[Wiley: Advanced Intelligent Discovery: Table of Contents] Discovery of Novel Materials with Giant Dielectric Constants via First‐Principles Phonon Calculations and Machine Learning

Wiley: Advanced Intelligent Discovery: Table of Contents — Tue, 30 Sep 2025 06:30:24 GMT

Advanced Intelligent Discovery, EarlyView.

[AI for Science - latest papers] FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential

AI for Science - latest papers — Mon, 29 Sep 2025 23:00:00 GMT

We introduce a rapid, accurate framework for computing atomic migration barriers in crystals by combining universal machine‐learning force fields (MLFFs) with 3D potential‐energy‐surface sampling and interpolation. Our method suppresses periodic self‐interactions via supercell expansion, builds a continuous potential energy surface (PES) from MLFF energies on a spatial grid, and extracts minimum‐energy pathways without predefined nudged elastic band (NEB) images. For a benchmark set of twelve electrode and electrolyte materials, including LiCoO2, LiFePO4, and Li10GeP2S12, our MLFF‐derived barriers lie within tens of meV of density functional theory (DFT) and experiment values, while achieving a ∼100-fold speedup over standard DFT‐NEB calculations. We benchmark GPTFF, CHGNet, and MACE, showing that fine‐tuning on PBE/PBE + U data further enhances accuracy. Ultimately, we introduce an open‐source package for high‐throughput materials screening and interactive PES visualization.

[Wiley: Advanced Intelligent Discovery: Table of Contents] Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Wiley: Advanced Intelligent Discovery: Table of Contents — Wed, 24 Sep 2025 13:21:08 GMT

Advanced Intelligent Discovery, EarlyView.

[Recent Articles in PRX Energy] Reconstructions and Dynamics of $β$-Lithium Thiophosphate Surfaces

Recent Articles in PRX Energy — Tue, 26 Aug 2025 10:00:00 GMT

Author(s): Hanna Türk, Davide Tisi, and Michele Ceriotti

Machine-learning-based molecular dynamics simulations of the solid electrolyte $β$ -Li $_{3}$ PS $_{4}$ under realistic conditions reveal dynamic surface structure and reactivity.

[PRX Energy 4, 033010] Published Tue Aug 26, 2025

[Matter] CGformer: Transformer-enhanced crystal graph network with global attention for material property prediction

Matter — Wed, 20 Aug 2025 00:00:00 GMT

Designing new materials for better batteries is a major challenge, especially for complex “high-entropy” materials with countless atomic combinations. We developed a novel AI model, CGformer, that looks at the entire crystal structure to accurately predict material properties. By screening nearly 150,000 candidates, our AI identified promising new sodium-ion solid electrolytes. We successfully synthesized these materials, and they showed excellent performance, validating our AI-driven discovery pipeline. This work provides a powerful tool to accelerate the design of next-generation energy materials.

[Recent Articles in PRX Energy] Large-Scale Simulation Unveiled Superior Potassium-Based Solid Electrolyte with High Ionic Conductivity and Excellent Electrochemical Stability in ${M}_{5}{\mathrm{YSi}}_{4}{\mathrm{O}}_{12}$ ($M=\mathrm{Li},\mathrm{K}$)

Recent Articles in PRX Energy — Thu, 14 Aug 2025 10:00:00 GMT

Author(s): Zhao Li, Jiaxiang Li, Congwei Xie, Keith Butler, Fei Du, and Yu Xie

Advanced computational modeling predicts the ionic conductivity and electrochemical stability of a promising potassium-based solid electrolyte. The approach highlights the importance of longer length and time scales during simulations, achievable with machine learning potentials.

[PRX Energy 4, 033007] Published Thu Aug 14, 2025

[Wiley: Advanced Intelligent Discovery: Table of Contents] Interpretable Machine Learning for Solvent‐Dependent Carrier Mobility in Solution‐Processed Organic Thin Films

Wiley: Advanced Intelligent Discovery: Table of Contents — Fri, 08 Aug 2025 09:54:45 GMT

Advanced Intelligent Discovery, EarlyView.

[Wiley: Advanced Intelligent Discovery: Table of Contents] Macrophage Phenotype Detection Methodology on Textured Surfaces via Nuclear Morphology Using Machine Learning

Wiley: Advanced Intelligent Discovery: Table of Contents — Fri, 01 Aug 2025 08:40:28 GMT

Advanced Intelligent Discovery, EarlyView.

[Wiley: Advanced Intelligent Discovery: Table of Contents] Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation

Wiley: Advanced Intelligent Discovery: Table of Contents — Thu, 24 Jul 2025 10:45:19 GMT

Advanced Intelligent Discovery, EarlyView.

[Recent Articles in PRX Energy] Origin of Intrinsically Low Thermal Conductivity in a Garnet-Type Solid Electrolyte: Linking Lattice and Ionic Dynamics with Thermal Transport

Recent Articles in PRX Energy — Thu, 17 Jul 2025 10:00:00 GMT

Author(s): Yitian Wang, Yaokun Su, Jesús Carrete, Huanyu Zhang, Nan Wu, Yutao Li, Hongze Li, Jiaming He, Youming Xu, Shucheng Guo, Qingan Cai, Douglas L. Abernathy, Travis Williams, Kostiantyn V. Kravchyk, Maksym V. Kovalenko, Georg K.H. Madsen, Chen Li, and Xi Chen

Using a comprehensive experimental and computational approach, this work analyzes the intrinsically low thermal conductivity of solid ionic conductor Li $_{6.5}$ La $_{3}$ Zr $_{1.5}$ Ta $_{0.5}$ O $_{12}$ , a promising electrolyte for all-solid-state batteries.

[PRX Energy 4, 033004] Published Thu Jul 17, 2025

[Recent Articles in PRX Energy] A Comparative Study of Solid Electrolyte Interphase Evolution in Ether and Ester-Based Electrolytes for $\mathrm{Na}$-ion Batteries

Recent Articles in PRX Energy — Tue, 15 Jul 2025 10:00:00 GMT

Author(s): Liang Zhao, Sara I.R. Costa, Yue Chen, Jack R. Fitzpatrick, Andrew J. Naylor, Oleg Kolosov, and Nuria Tapia-Ruiz

Diglyme-based electrolytes promote a thin, uniform, and stable solid electrolyte interphase that can extend the lifespan of sodium-ion batteries, as shown using advanced spectroscopic and electrochemical techniques.

[PRX Energy 4, 033002] Published Tue Jul 15, 2025

[Wiley: Advanced Intelligent Discovery: Table of Contents] Autonomous Machine Learning‐Based Classification and Arrangement of Submillimeter Objects Using a Capillary Force Gripper

Wiley: Advanced Intelligent Discovery: Table of Contents — Wed, 09 Jul 2025 08:01:30 GMT

Advanced Intelligent Discovery, EarlyView.

[Wiley: Advanced Intelligent Discovery: Table of Contents] Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials

Wiley: Advanced Intelligent Discovery: Table of Contents — Wed, 09 Jul 2025 07:56:18 GMT

Advanced Intelligent Discovery, EarlyView.

[Wiley: Advanced Intelligent Discovery: Table of Contents] A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

Wiley: Advanced Intelligent Discovery: Table of Contents — Fri, 27 Jun 2025 08:27:19 GMT

Advanced Intelligent Discovery, EarlyView.

[Wiley: Advanced Intelligent Discovery: Table of Contents] Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review

Wiley: Advanced Intelligent Discovery: Table of Contents — Fri, 27 Jun 2025 08:15:35 GMT

Advanced Intelligent Discovery, EarlyView.

[ACS Materials Au: Latest Articles (ACS Publications)] [ASAP] Historical Data Mining Deep Dive into Machine Learning-Aided 2D Materials Research in Electrochemical Applications

ACS Materials Au: Latest Articles (ACS Publications) — Mon, 23 Jun 2025 15:22:16 GMT

ACS Materials Au

DOI: 10.1021/acsmaterialsau.5c00030

[Wiley: Advanced Intelligent Discovery: Table of Contents] Machine Learning‐Assisted Infectious Disease Detection in Low‐Income Areas: Toward Rapid Triage of Dengue and Zika Virus Using Open‐Source Hardware

Wiley: Advanced Intelligent Discovery: Table of Contents — Mon, 23 Jun 2025 08:20:28 GMT

Advanced Intelligent Discovery, EarlyView.

[Wiley: Advanced Intelligent Discovery: Table of Contents] What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Wiley: Advanced Intelligent Discovery: Table of Contents — Fri, 20 Jun 2025 08:36:19 GMT

Advanced Intelligent Discovery, EarlyView.

[Wiley: Advanced Intelligent Discovery: Table of Contents] Predicting High‐Resolution Spatial and Spectral Features in Mass Spectrometry Imaging with Machine Learning and Multimodal Data Fusion

Wiley: Advanced Intelligent Discovery: Table of Contents — Wed, 18 Jun 2025 08:10:58 GMT

Advanced Intelligent Discovery, EarlyView.

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Efficient Pure Organic Near-Infrared Room-Temperature Phosphorescence Based on n/π Orbital Decoupling

Chinese Chemical Society: CCS Chemistry: Table of Contents — Sat, 14 Jun 2025 05:08:51 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202405319

Developing pure organic room-temperature phosphorescence (RTP) materials remains an enormous challenge, especially for efficient near-infrared (NIR) RTP materials. Herein, a functional unit combination strategy is employed to design a series of pure ...

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Efficient Pure Organic Near-Infrared Room-Temperature Phosphorescence Based on n/π Orbital Decoupling

Chinese Chemical Society: CCS Chemistry: Table of Contents — Sat, 14 Jun 2025 05:08:51 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202405319

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Identification of Cu-N2 Sites for Zn-Air Batteries in Harsh Electrolytes: Computer Virtual Screening, Machine Learning, and Practical Application

Chinese Chemical Society: CCS Chemistry: Table of Contents — Sat, 14 Jun 2025 04:39:17 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202505577

Nonprecious-metal catalysts possess great potential to replace noble metals in catalysis; however, selecting efficient candidates through experiments is time-consuming and costly. Herein, we employed a data-driven virtual screening (VS) method to discover ...

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Identification of Cu-N2 Sites for Zn-Air Batteries in Harsh Electrolytes: Computer Virtual Screening, Machine Learning, and Practical Application

Chinese Chemical Society: CCS Chemistry: Table of Contents — Sat, 14 Jun 2025 04:39:17 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202505577

[Recent Articles in PRX Energy] Correlating Local Morphology and Charge Dynamics via Kelvin Probe Force Microscopy to Explain Photoelectrode Performance

Recent Articles in PRX Energy — Mon, 09 Jun 2025 10:00:00 GMT

Author(s): Maryam Pourmahdavi, Mauricio Schieda, Ragle Raudsepp, Steffen Fengler, Jiri Kollmann, Yvonne Pieper, Thomas Dittrich, Thomas Klassen, and Francesca M. Toma

Charge transport in photoelectrodes for photoelectrochemical cells is influenced by microstructural variations; here, the authors use Kelvin Probe Force Microscopy to correlate local morphology with optoelectronic properties toward optimizing materials toward material optimization.

[PRX Energy 4, 023010] Published Mon Jun 09, 2025

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Minimal Sulfur-Grafted Graphite Anode with Accelerated Interfacial Kinetics for Fast-Charging Lithium-Ion Batteries

Chinese Chemical Society: CCS Chemistry: Table of Contents — Wed, 28 May 2025 08:32:07 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202505705

The significant capacity decay and undesired metallic Li plating of graphite anode resulting from sluggish Li-ion diffusion kinetics at the graphite/electrolyte interface have largely hindered the fast-charging capability of lithium-ion batteries (LIBs). ...

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Minimal Sulfur-Grafted Graphite Anode with Accelerated Interfacial Kinetics for Fast-Charging Lithium-Ion Batteries

Chinese Chemical Society: CCS Chemistry: Table of Contents — Wed, 28 May 2025 08:32:07 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202505705

[Recent Articles in PRX Energy] Resolving the Solvation Structure and Transport Properties of Aqueous Zinc Electrolytes from Salt-in-Water to Water-in-Salt Using Neural Network Potential

Recent Articles in PRX Energy — Fri, 11 Apr 2025 10:00:00 GMT

Author(s): Chuntian Cao, Arun Kingan, Ryan C. Hill, Jason Kuang, Lei Wang, Chunyi Zhang, Matthew R. Carbone, Hubertus van Dam, Shinjae Yoo, Shan Yan, Esther S. Takeuchi, Kenneth J. Takeuchi, Xifan Wu, AM Milinda Abeykoon, Amy C. Marschilok, and Deyu Lu

A neural network potential model is developed for ZnCl $_{2}$ electrolytes that provides atomic scale insights into the solvation structure and ionic conductivity. The results agree well with experiment and shed light on the performance of aqueous zinc-ion batteries across a wide concentration range of ZnCl $_{2}$ electrolytes.

[PRX Energy 4, 023004] Published Fri Apr 11, 2025

[Recent Articles in PRX Energy] Crystal Structure Prediction and Phase Stability in Highly Anharmonic Silver-Based Chalcohalide Antiperovskites

Recent Articles in PRX Energy — Thu, 03 Apr 2025 10:00:00 GMT

Author(s): Pol Benítez, Cibrán López, Cong Liu, Ivan Caño, Josep-Lluís Tamarit, Edgardo Saucedo, and Claudio Cazorla

Using theoretical first-principles methods, stable and metastable phases of silver-based chalcohalide anti-perovskites are predicted, offering insight into their stability for potential energy and optoelectronic applications.

[PRX Energy 4, 023002] Published Thu Apr 03, 2025

[Recent Articles in PRX Energy] Unraveling Temperature-Induced Vacancy Clustering in Tungsten: From Direct Microscopy to Atomistic Insights via Data-Driven Bayesian Sampling

Recent Articles in PRX Energy — Tue, 25 Feb 2025 10:00:00 GMT

Author(s): Anruo Zhong, Clovis Lapointe, Alexandra M. Goryaeva, Kazuto Arakawa, Manuel Athènes, and Mihai-Cosmin Marinica

This study reveals how anharmonic, entropy-driven stabilization of di-vacancies at elevated temperatures reconcile theoretical predictions with experimental observations of vacancy clustering in tungsten, a prime candidate material for fusion reactors.

[PRX Energy 4, 013008] Published Tue Feb 25, 2025

[Recent Articles in PRX Energy] Constant-Current Nonequilibrium Molecular Dynamics Approach for Accelerated Computation of Ionic Conductivity Including Ion-Ion Correlation

Recent Articles in PRX Energy — Wed, 19 Feb 2025 10:00:00 GMT

Author(s): Ryoma Sasaki, Yoshitaka Tateyama, and Debra J. Searles

A new “constant-current” nonequilibrium molecular dynamics simulation method accelerates ionic conductivity calculations by up to two orders of magnitude while accurately capturing ion-ion correlations, enabling more efficient screening of solid electrolytes and revealing important low-temperature conduction behaviors.

[PRX Energy 4, 013005] Published Wed Feb 19, 2025

[Recent Articles in PRX Energy] Detecting Attacks and Estimating States of Power Grids from Partial Observations with Machine Learning

Recent Articles in PRX Energy — Tue, 04 Feb 2025 10:00:00 GMT

Author(s): Zheng-Meng Zhai, Mohammadamin Moradi, and Ying-Cheng Lai

Using data-driven machine learning models, this work demonstrates a method to map the dynamical state of a full power grid from limited observations, enabling the user to locate disruptions with information only from other areas of the grid.

[PRX Energy 4, 013003] Published Tue Feb 04, 2025

[Recent Articles in PRX Energy] 3D Reconstruction of a Nuclear Reactor by Muon Tomography: Structure Validation and Anomaly Detection

Recent Articles in PRX Energy — Tue, 28 Jan 2025 10:00:00 GMT

Author(s): Baptiste Lefevre, Julien Vogel, Héctor Gomez, David Attié, Laurent Gallego, Philippe Gonzales, Bertrand Lesage, Philippe Mas, and Daniel Pomarède

Muon tomography, a non-invasive technique that can be used to image large, inaccessible structures, is combined with machine learning to create a 3D reconstruction of a historical nuclear reactor that reveals material density variations, including potential anomalies in the graphite core.

[PRX Energy 4, 013002] Published Tue Jan 28, 2025

[Recent Articles in PRX Energy] Determining Parameters of Metal-Halide Perovskites Using Photoluminescence with Bayesian Inference

Recent Articles in PRX Energy — Tue, 14 Jan 2025 10:00:00 GMT

Author(s): Manuel Kober-Czerny, Akash Dasgupta, Seongrok Seo, Florine M. Rombach, David P. McMeekin, Heon Jin, and Henry J. Snaith

Experiment and machine learning are combined to extract key material parameters and insight into charge carrier transport in metal halide perovskites for solar cell applications.

[PRX Energy 4, 013001] Published Tue Jan 14, 2025

[Recent Articles in PRX Energy] Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network

Recent Articles in PRX Energy — Wed, 12 Jun 2024 10:00:00 GMT

Author(s): Hengrui Zhang (张恒睿), Tianxing Lai (来天行), Jie Chen, Arumugam Manthiram, James M. Rondinelli, and Wei Chen

MolSets, a machine learning model that integrates graph neural networks with permutation invariant architecture, addresses multilevel complexity for effective prediction of molecular mixture properties, thus accelerating lithium battery electrolyte design.

[PRX Energy 3, 023006] Published Wed Jun 12, 2024

[Recent Articles in PRX Energy] Temperature Impact on Lithium Metal Morphology in Lithium Reservoir-Free Solid-State Batteries

Recent Articles in PRX Energy — Fri, 17 May 2024 10:00:00 GMT

Author(s): Min-Gi Jeong, Kelsey B. Hatzell, Sourim Banerjee, Bairav S. Vishnugopi, and Partha P. Mukherjee

Toward enhancing performance in reservoir-free solid-state batteries, confocal imaging experiments are combined with meso-scale modeling to unveil vertical and horizontal growth mechanisms at varying temperatures of lithium metal at an agyrodite solid electrolyte|stainless steel interface.

[PRX Energy 3, 023003] Published Fri May 17, 2024

[Recent Articles in Rev. Mod. Phys.] Colloquium: Advances in automation of quantum dot devices control

Recent Articles in Rev. Mod. Phys. — Fri, 17 Feb 2023 10:00:00 GMT

Author(s): Justyna P. Zwolak and Jacob M. Taylor

A promising platform for quantum computing consists of arrays of quantum dots. However, operating these devices presents a challenging control problem, since the location of the dots and the charges they contain must be reliably and reproducibly matched with the gate voltages. This Colloquium explains how automated control protocols that make use of machine learning techniques can succeed in systems where heuristic control is not feasible.

[Rev. Mod. Phys. 95, 011006] Published Fri Feb 17, 2023

[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Hydrogen as promoter and inhibitor of superionicity: A case study on Li-N-H systems

PRB: Dynamics, dynamical systems, lattice effects, quantum solids — Mon, 26 Jul 2010 10:00:00 GMT

Author(s): Andreas Blomqvist, C. Moysés Araújo, Ralph H. Scheicher, Pornjuk Srepusharawoot, Wen Li, Ping Chen, and Rajeev Ahuja

Materials which possess a high lithium ion conductivity are very attractive for battery and fuel cell applications. Hydrogenation of the fast-ion conductor lithium nitride $({\text{Li}}_{3}\text{N})$ leads to the formation of lithium imide $({\text{Li}}_{2}\text{NH})$ and subsequently of lithium ami…

[Phys. Rev. B 82, 024304] Published Mon Jul 26, 2010

[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Nonadiabatic effects of rattling phonons and $4f$ excitations in $\text{Pr}{({\text{Os}}_{1−x}{\text{Ru}}_{x})}_{4}{\text{Sb}}_{12}$

PRB: Dynamics, dynamical systems, lattice effects, quantum solids — Fri, 18 Jun 2010 10:00:00 GMT

Author(s): Peter Thalmeier

In the skutterudite compounds the anharmonic “rattling” oscillations of $4f$-guest ions in the surrounding ${\text{Sb}}_{12}$ host cages are found to have significant influence on the low-temperature properties. Recently specific-heat analysis of $\text{Pr}{({\text{Os}}_{1−x}{\text{Ru}}_{x})}_{4}{\t…

[Phys. Rev. B 81, 224305] Published Fri Jun 18, 2010

[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Ionic conductivity of nanocrystalline yttria-stabilized zirconia: Grain boundary and size effects

PRB: Dynamics, dynamical systems, lattice effects, quantum solids — Mon, 10 May 2010 10:00:00 GMT

Author(s): O. J. Durá, M. A. López de la Torre, L. Vázquez, J. Chaboy, R. Boada, A. Rivera-Calzada, J. Santamaria, and C. Leon

We report on the effect of grain size on the ionic conductivity of yttria-stabilized zirconia samples synthesized by ball milling. Complex impedance measurements, as a function of temperature and frequency are performed on $10\text{ }\text{mol}\text{ }\mathrm{%}$ yttria-stabilized zirconia nanocryst…

[Phys. Rev. B 81, 184301] Published Mon May 10, 2010

[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Calculating the anharmonic free energy from first principles

PRB: Dynamics, dynamical systems, lattice effects, quantum solids — Fri, 07 May 2010 10:00:00 GMT

Author(s): Zhongqing Wu

We developed a method to calculate the anharmonic free energy without requiring any adjustable parameter. The requisite computations are first-principles quasiharmonic calculations plus an additional Canonical (NVT) ensemble first-principles molecular-dynamics simulation and, therefore, are affordab…

[Phys. Rev. B 81, 172301] Published Fri May 07, 2010

[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Phason dynamics in one-dimensional lattices

PRB: Dynamics, dynamical systems, lattice effects, quantum solids — Thu, 25 Feb 2010 10:00:00 GMT

Author(s): Hansjörg Lipp, Michael Engel, Steffen Sonntag, and Hans-Rainer Trebin

In quasicrystals, the phason degree of freedom and the inherent anharmonic potentials lead to complex dynamics, which cannot be described by the usual phonon modes of motion. We have constructed simple one-dimensional model systems, the dynamic Fibonacci chain, and approximants thereof. They allow u…

[Phys. Rev. B 81, 064302] Published Thu Feb 25, 2010

[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Ab initio construction of interatomic potentials for uranium dioxide across all interatomic distances

PRB: Dynamics, dynamical systems, lattice effects, quantum solids — Wed, 25 Nov 2009 10:00:00 GMT

Author(s): P. Tiwary, A. van de Walle, and N. Grønbech-Jensen

We provide a methodology for generating interatomic potentials for use in classical molecular-dynamics simulations of atomistic phenomena occurring at energy scales ranging from lattice vibrations to crystal defects to high-energy collisions. A rigorous method to objectively determine the shape of a…

[Phys. Rev. B 80, 174302] Published Wed Nov 25, 2009

[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] One-dimensional nanostructure-guided chain reactions: Harmonic and anharmonic interactions

PRB: Dynamics, dynamical systems, lattice effects, quantum solids — Fri, 13 Nov 2009 10:00:00 GMT

Author(s): Nitish Nair and Michael S. Strano

We have performed a parametric study of self-propagating chain reactions along a one-dimensional bead-spring array. The coupling between beads is modeled using harmonic and anharmonic Fermi-Pasta-Ulam (FPU)-$β$ and ${φ}^{4}$ potentials. The parameters that define the system are the activation energy…

[Phys. Rev. B 80, 174301] Published Fri Nov 13, 2009

[Cell Reports Physical Science] Hierarchical optimization of perovskite solar cell fabrication via step-by-step machine learning

Cell Reports Physical Science — Wed, 31 Dec 2025 00:00:00 GMT

[ACS Materials Letters: Latest Articles (ACS Publications)] [ASAP] Machine Learning Applications in Predicting Friction Properties of Bearing Steel: A Review

ACS Materials Letters: Latest Articles (ACS Publications) — Tue, 30 Dec 2025 19:59:57 GMT

ACS Materials Letters

DOI: 10.1021/acsmaterialslett.5c01047

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Machine Learning-Guided Discovery of Sterically Protected High Triplet Exciplex Hosts for Ultra-Bright Green OLEDs

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Tue, 30 Dec 2025 19:03:11 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c16369

[Wiley: Small Methods: Table of Contents] Standardization and Machine Learning Prediction of Tafel Slope of Pt‐Based Nanocatalysts for High‐Performance HER Catalyst Development

Wiley: Small Methods: Table of Contents — Tue, 30 Dec 2025 12:06:41 GMT

Small Methods, EarlyView.

[npj Computational Materials] Toward high entropy material discovery for energy applications using computational and machine learning methods

npj Computational Materials — Tue, 30 Dec 2025 00:00:00 GMT

npj Computational Materials, Published online: 30 December 2025; doi:10.1038/s41524-025-01918-6

Toward high entropy material discovery for energy applications using computational and machine learning methods

[APL Machine Learning Current Issue] AI agents for photonic integrated circuit design automation

APL Machine Learning Current Issue — Tue, 30 Dec 2025 00:00:00 GMT

[Applied Physics Letters Current Issue] Rattling-induced anharmonicity and multi-valley enhanced thermoelectric performance in layered SmZnSbO material

Applied Physics Letters Current Issue — Tue, 30 Dec 2025 00:00:00 GMT

[Applied Physics Letters Current Issue] Magneto-ionic control of perpendicular anisotropy in epitaxial Mn 4 N films

Applied Physics Letters Current Issue — Tue, 30 Dec 2025 00:00:00 GMT

[Applied Physics Letters Current Issue] Predicting anode coatings for solid-state lithium metal batteries via first-principles thermodynamic calculations and hierarchical ion-transport algorithms

Applied Physics Letters Current Issue — Tue, 30 Dec 2025 00:00:00 GMT

[APL Materials Current Issue] Lithography-free fabrication of transparent, durable surfaces with embedded functional materials in glass nanoholes

APL Materials Current Issue — Tue, 30 Dec 2025 00:00:00 GMT

[ChemRxiv] Augmenting Large Language Models for Automated Discovery of f-Element Extractants

ChemRxiv — Tue, 30 Dec 2025 00:00:00 GMT

[RSC - Chem. Sci. latest articles] Solid Dual-Salt Plastic Crystal Electrolyte Enabling Rapid Ion Transfer and Stable Interphases for High-Performance Solid-State Sodium Ion Batteries

RSC - Chem. Sci. latest articles — Tue, 30 Dec 2025 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC09186A, Edge Article

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

[Wiley: Advanced Science: Table of Contents] Intelligent Sensing: The Emerging Integration of Machine Learning and Soft Sensors Based on Hydrogels and Ionogels

Wiley: Advanced Science: Table of Contents — Mon, 29 Dec 2025 21:01:21 GMT

Advanced Science, Volume 12, Issue 48, December 29, 2025.

[Wiley: Advanced Science: Table of Contents] Pre‐Constructed Mechano‐Electrochemical Adaptive Solid Electrolyte Interphase to Enhance Li+ Diffusion Kinetics and Interface Stability for Chemically Prelithiated SiO Anodes

Wiley: Advanced Science: Table of Contents — Mon, 29 Dec 2025 21:01:21 GMT

Advanced Science, Volume 12, Issue 48, December 29, 2025.

[Wiley: Small: Table of Contents] Unraveling A‐Site Cation Control of Hot Carrier Relaxation in Vacancy‐Ordered Halide Perovskites Through Quantum Dynamics and Interpretable Machine Learning

Wiley: Small: Table of Contents — Mon, 29 Dec 2025 20:38:41 GMT

Small, Volume 21, Issue 52, December 29, 2025.

[Wiley: Advanced Materials: Table of Contents] Rational Design of Metal–Organic Frameworks for Pancreatic Cancer Therapy: from Machine Learning Screening to In Vivo Efficacy (Adv. Mater. 52/2025)

Wiley: Advanced Materials: Table of Contents — Mon, 29 Dec 2025 19:50:02 GMT

Advanced Materials, Volume 37, Issue 52, December 29, 2025.

[Wiley: Advanced Materials: Table of Contents] Rational Design of Metal–Organic Frameworks for Pancreatic Cancer Therapy: from Machine Learning Screening to In Vivo Efficacy

Wiley: Advanced Materials: Table of Contents — Mon, 29 Dec 2025 19:50:02 GMT

Advanced Materials, Volume 37, Issue 52, December 29, 2025.

[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Chloride-Based Solid Electrolytes from Crystal Structure to Electrochemical Performance

ACS Energy Letters: Latest Articles (ACS Publications) — Mon, 29 Dec 2025 19:20:24 GMT

ACS Energy Letters

DOI: 10.1021/acsenergylett.5c03415

[Wiley: Small: Table of Contents] Enhancing Cycling Stability and Suppressing Lithium Dendrite Formation With A Hierarchical Artificial Solid Electrolyte Interphase Layer on Lithium Anodes for High‐Voltage Lithium Metal Batteries

Wiley: Small: Table of Contents — Mon, 29 Dec 2025 09:13:44 GMT

Small, EarlyView.

[Wiley: Advanced Materials: Table of Contents] Gradient‐Heterojunction in Solid Electrolytes for Fast‐Charging Dendrite‐Free Solid‐State Lithium Metal Batteries

Wiley: Advanced Materials: Table of Contents — Mon, 29 Dec 2025 07:59:12 GMT

Advanced Materials, EarlyView.

[APL Machine Learning Current Issue] Synthetic images from generative AI for compositional analysis of dried solution patterns

APL Machine Learning Current Issue — Mon, 29 Dec 2025 00:00:00 GMT

[iScience] An Interpretable Machine Learning Model for Predicting the Effectiveness of Low-Dose Methylprednisolone in Treating Long COVID: A Retrospective Study

iScience — Mon, 29 Dec 2025 00:00:00 GMT

[iScience] River plastic hotspot detection from space

iScience — Mon, 29 Dec 2025 00:00:00 GMT

[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Machine Learning−Accelerated Discovery of Proton-Conducting 2D Materials for Proton Exchange Membranes

ACS Nano: Latest Articles (ACS Publications) — Sat, 27 Dec 2025 14:37:43 GMT

ACS Nano

DOI: 10.1021/acsnano.5c15161

[Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)] [ASAP] Automated Machine Learning Pipeline: Large Language Models-Assisted Automated Data set Generation for Training Machine-Learned Interatomic Potentials

Journal of Chemical Theory and Computation: Latest Articles (ACS Publications) — Fri, 26 Dec 2025 18:25:53 GMT

Journal of Chemical Theory and Computation

DOI: 10.1021/acs.jctc.5c01610

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Machine Learning Photodynamics Unveils a Controlled H2 Loss Channel in the Methaniminium Cation

The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications) — Fri, 26 Dec 2025 17:51:53 GMT

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03196

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Solvent Structure and Dynamics Controlled Memristive Ion Transport in Å-Scale Channels

The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications) — Fri, 26 Dec 2025 16:50:38 GMT

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03397

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Design of Open Ion Transport Channels in Thick Electrodes through Directional Freezing-Assisted 3D Printing: Enhancing the Electrochemical Performance of High-Load Electrodes

The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications) — Fri, 26 Dec 2025 16:49:57 GMT

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c02968

[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Machine Learning Analysis of Dimensionality Effects on Bandgap Prediction

The Journal of Physical Chemistry C: Latest Articles (ACS Publications) — Fri, 26 Dec 2025 16:06:02 GMT

The Journal of Physical Chemistry C

DOI: 10.1021/acs.jpcc.5c05232

[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Machine Learning-Accelerated Development of Li-Rich Layered Oxides with High Reversible Capacity and Coulombic Efficiency

ACS Nano: Latest Articles (ACS Publications) — Fri, 26 Dec 2025 09:21:05 GMT

ACS Nano

DOI: 10.1021/acsnano.5c16117

[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] A complete spatial map of mouse retinal ganglion cells reveals density and gene expression specializations

Fri, 26 Dec 2025 08:00:00 GMT

[Wiley: Advanced Science: Table of Contents] Mendelian Randomization and Double Machine Learning Modeling Reveal Brain Imaging‐Derived Phenotypes as Functional Contributors to 18 Autoimmune Inflammatory Diseases

Wiley: Advanced Science: Table of Contents — Fri, 26 Dec 2025 06:23:13 GMT

Advanced Science, EarlyView.

[Wiley: Carbon Energy: Table of Contents] Data‐Driven Design of Scalable Perovskite Film Fabrication via Machine Learning–Guided Processing

Wiley: Carbon Energy: Table of Contents — Fri, 26 Dec 2025 06:22:51 GMT

Carbon Energy, EarlyView.

[iScience] Entropy-based byte patching transformer for self-supervised pretraining of SMILES data

iScience — Fri, 26 Dec 2025 00:00:00 GMT

[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Navigating the Catholyte Landscape in All-Solid-State Batteries

ACS Energy Letters: Latest Articles (ACS Publications) — Wed, 24 Dec 2025 16:14:16 GMT

ACS Energy Letters

DOI: 10.1021/acsenergylett.5c03429

[Wiley: Advanced Functional Materials: Table of Contents] Printing Nacre‐Mimetic MXene‐Based E‐Textile Devices for Sensing and Breathing‐Pattern Recognition Using Machine Learning

Wiley: Advanced Functional Materials: Table of Contents — Wed, 24 Dec 2025 15:52:36 GMT

Advanced Functional Materials, Volume 35, Issue 52, December 23, 2025.

[Wiley: Advanced Functional Materials: Table of Contents] Role of Crosslinking and Backbone Segmental Dynamics on Ion Transport in Hydrated Anion‐Conducting Polyelectrolytes

Wiley: Advanced Functional Materials: Table of Contents — Wed, 24 Dec 2025 15:52:36 GMT

Advanced Functional Materials, Volume 35, Issue 52, December 23, 2025.

[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Conjunctive population coding integrates sensory evidence to guide adaptive behavior

Wed, 24 Dec 2025 08:00:00 GMT

[Wiley: Advanced Energy Materials: Table of Contents] Hyperquaternized Biomass‐Derived Solid Electrolytes: Architecting Superionic Conduction for Sustainable Flexible Zinc‐Air Batteries

Wiley: Advanced Energy Materials: Table of Contents — Wed, 24 Dec 2025 07:08:52 GMT

Advanced Energy Materials, EarlyView.

[npj Computational Materials] High-throughput computational framework for high-order anharmonic thermal transport in cubic and tetragonal crystals

npj Computational Materials — Wed, 24 Dec 2025 00:00:00 GMT

npj Computational Materials, Published online: 24 December 2025; doi:10.1038/s41524-025-01920-y

High-throughput computational framework for high-order anharmonic thermal transport in cubic and tetragonal crystals

[Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)] [ASAP] Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials

Journal of Chemical Theory and Computation: Latest Articles (ACS Publications) — Tue, 23 Dec 2025 19:20:50 GMT

Journal of Chemical Theory and Computation

DOI: 10.1021/acs.jctc.5c01712

[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Tuning ORR Activity of N-Doped Biphenylene-Based Single-Atom Catalysts via DFT and Machine Learning Synergy

The Journal of Physical Chemistry C: Latest Articles (ACS Publications) — Tue, 23 Dec 2025 18:21:52 GMT

The Journal of Physical Chemistry C

DOI: 10.1021/acs.jpcc.5c06757

[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Concomitant Enhancement of the Reorientational Dynamics of the BH4– Anions and Mg2+ Ionic Conductivity in Mg(BH4)2·NH3 upon Ligand Incorporation

The Journal of Physical Chemistry C: Latest Articles (ACS Publications) — Tue, 23 Dec 2025 13:34:12 GMT

The Journal of Physical Chemistry C

DOI: 10.1021/acs.jpcc.5c07031

[Wiley: Advanced Energy Materials: Table of Contents] Interpretable Machine Learning for Battery Prognosis: Retrospect and Prospect

Wiley: Advanced Energy Materials: Table of Contents — Tue, 23 Dec 2025 10:15:25 GMT

Advanced Energy Materials, Volume 15, Issue 48, December 23, 2025.

[Wiley: Advanced Energy Materials: Table of Contents] Novel Sodium‐Rare‐Earth‐Silicate‐Based Solid Electrolytes for All‐Solid‐State Sodium Batteries: Structure, Synthesis, Conductivity, and Interface

Wiley: Advanced Energy Materials: Table of Contents — Tue, 23 Dec 2025 10:15:25 GMT

Advanced Energy Materials, Volume 15, Issue 48, December 23, 2025.

[Wiley: Advanced Energy Materials: Table of Contents] Ambipolar Ion Transport Membranes Enable Stable Noble‐Metal‐Free CO2 Electrolysis in Neutral Media

Wiley: Advanced Energy Materials: Table of Contents — Tue, 23 Dec 2025 10:15:25 GMT

Advanced Energy Materials, Volume 15, Issue 48, December 23, 2025.

[Wiley: Small: Table of Contents] Supersaturation‐Driven Co‐Precipitation Enables Scalable Wet‐Chemical Synthesis of High‐Purity Na3InCl6 Solid Electrolyte for Sodium‐Ion Batteries

Wiley: Small: Table of Contents — Tue, 23 Dec 2025 07:06:10 GMT

Small, Volume 21, Issue 51, December 23, 2025.

[Wiley: Small: Table of Contents] Synergistic Co‐Optimization Strategy for Electron‐Ion Transport Kinetics in all‐Solid‐State Sulfurized Polyacrylonitrile Cathodes

Wiley: Small: Table of Contents — Tue, 23 Dec 2025 07:06:10 GMT

Small, Volume 21, Issue 51, December 23, 2025.

[RSC - Chem. Sci. latest articles] Robust Janus-Faced Quasi-Solid-State Electrolytes Mimicking Honeycomb for Fast Transport and Adequate Supply of Sodium Ions

RSC - Chem. Sci. latest articles — Tue, 23 Dec 2025 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC08536E, Edge Article

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

[RSC - Chem. Sci. latest articles] Automated Closed-Loop Continuous Flow Block Copolymer Synthesizer

RSC - Chem. Sci. latest articles — Tue, 23 Dec 2025 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC07307C, Edge Article

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

[RSC - Chem. Sci. latest articles] Node-Equivariant Message Passing for Efficient and Accurate Machine Learning Interatomic Potentials

RSC - Chem. Sci. latest articles — Tue, 23 Dec 2025 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC07248D, Edge Article

Open Access

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

[iScience] A Multicenter Multimodel Habitat Radiomics Model for Predicting Immunotherapy Response in Advanced NSCLC

iScience — Tue, 23 Dec 2025 00:00:00 GMT

[Joule] Machine learning-driven interface material design for high-performance perovskite solar cells with scalability and band-gap universality

Joule — Tue, 23 Dec 2025 00:00:00 GMT

[Cell Reports Physical Science] A global thermodynamic-kinetic model capturing the hallmarks of liquid-liquid phase separation and amyloid aggregation

Cell Reports Physical Science — Tue, 23 Dec 2025 00:00:00 GMT

[RSC - Chem. Sci. latest articles] Chemically-informed active learning enables data-efficient multi-objective optimization of self-healing polyurethanes

RSC - Chem. Sci. latest articles — Tue, 23 Dec 2025 00:00:00 GMT

Chem. Sci., 2026, Advance Article
DOI: 10.1039/D5SC07752D, Edge Article

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Regulating Solvation Structure and Ion Transport via Lewis-Base Dual-Functional Covalent Organic Polymer Separators for Dendrite-Free Li-Metal Anodes

ACS Nano: Latest Articles (ACS Publications) — Mon, 22 Dec 2025 20:52:05 GMT

ACS Nano

DOI: 10.1021/acsnano.5c14722

[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Highly Selective Lithium-Ion Separation by Regulating Ion Transport Energy Barriers of Vermiculite Membranes

ACS Nano: Latest Articles (ACS Publications) — Mon, 22 Dec 2025 18:30:41 GMT

ACS Nano

DOI: 10.1021/acsnano.5c17718

[Wiley: Advanced Intelligent Discovery: Table of Contents] A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Wiley: Advanced Intelligent Discovery: Table of Contents — Mon, 22 Dec 2025 17:43:04 GMT

Advanced Intelligent Discovery, EarlyView.

[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Multianion Synergism Boosts High-Performance All-Solid-State Lithium Batteries

ACS Nano: Latest Articles (ACS Publications) — Mon, 22 Dec 2025 14:37:35 GMT

ACS Nano

DOI: 10.1021/acsnano.5c12987

[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Density Effects on the Thermal Decomposition of LLM-105 Explored by Neural Network Potential

The Journal of Physical Chemistry C: Latest Articles (ACS Publications) — Mon, 22 Dec 2025 14:01:00 GMT

The Journal of Physical Chemistry C

DOI: 10.1021/acs.jpcc.5c06140

[Accounts of Chemical Research: Latest Articles (ACS Publications)] [ASAP] Accelerating Materials Discovery Through Sparse Gaussian Process Machine Learning Potentials

Accounts of Chemical Research: Latest Articles (ACS Publications) — Mon, 22 Dec 2025 13:59:15 GMT

Accounts of Chemical Research

DOI: 10.1021/acs.accounts.5c00667

[Recent Articles in Phys. Rev. Lett.] Gate-Tunable Spectrum and Charge Dispersion Mitigation in a Graphene Superconducting Qubit

Recent Articles in Phys. Rev. Lett. — Mon, 22 Dec 2025 10:00:00 GMT

Author(s): Nicolas Aparicio, Simon Messelot, Edgar Bonet-Orozco, Eric Eyraud, Kenji Watanabe, Takashi Taniguchi, Johann Coraux, and Julien Renard

[Phys. Rev. Lett. 135, 266001] Published Mon Dec 22, 2025

[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Maladaptive immunity to the microbiota promotes neuronal hyperinnervation and itch via IL-17A

Mon, 22 Dec 2025 08:00:00 GMT

[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] SR-LLM: An incremental symbolic regression framework driven by LLM-based retrieval-augmented generation

Mon, 22 Dec 2025 08:00:00 GMT

[Applied Physics Reviews Current Issue] Thermal conductivity limits of MoS 2 and MoSe 2 : Revisiting high-order anharmonic lattice dynamics with machine learning potentials

Applied Physics Reviews Current Issue — Mon, 22 Dec 2025 00:00:00 GMT

[iScience] Widely Targeted Metabolomics and Machine Learning Identify Succinate as a Key Metabolite in Sepsis-Associated Encephalopathy

iScience — Mon, 22 Dec 2025 00:00:00 GMT

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Interpreting X-ray Diffraction Patterns of Metal–Organic Frameworks via Generative Artificial Intelligence

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Sat, 20 Dec 2025 15:03:45 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c16416

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] A Transformative Molecular Muscle Solid Electrolyte

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Fri, 19 Dec 2025 20:12:03 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c18427

[Wiley: Small Structures: Table of Contents] Li6−xFe1−xAlxCl8 Solid Electrolytes for Cost‐Effective All‐Solid‐State LiFePO4 Batteries

Wiley: Small Structures: Table of Contents — Fri, 19 Dec 2025 18:40:34 GMT

Small Structures, EarlyView.

[Joule] Boosting ionic conductivity of fluoride electrolytes by polyanion coordination chemistry enabling 5 V-Class all-solid-state batteries

Joule — Fri, 19 Dec 2025 00:00:00 GMT

[Recent Articles in Phys. Rev. Lett.] Variational Machine Learning Model for Electronic Structure Optimization via the Density Matrix

Recent Articles in Phys. Rev. Lett. — Thu, 18 Dec 2025 10:00:00 GMT

Author(s): Luqi Dong, Shuxiang Yang, Su-Huai Wei, and Yunhao Lu

[Phys. Rev. Lett. 135, 256403] Published Thu Dec 18, 2025

[Wiley: Advanced Science: Table of Contents] Polymorphism Crystal Structure Prediction with Adaptive Space Group Diversity Control

Wiley: Advanced Science: Table of Contents — Thu, 18 Dec 2025 09:38:21 GMT

Advanced Science, Volume 12, Issue 47, December 18, 2025.

[Wiley: Advanced Science: Table of Contents] Computationally‐Guided Development of Sulfide Solid Electrolyte Powder Coatings for Enhanced Stability and Performance of Solid‐State Batteries

Wiley: Advanced Science: Table of Contents — Thu, 18 Dec 2025 09:38:21 GMT

Advanced Science, Volume 12, Issue 47, December 18, 2025.

[Proceedings of the National Academy of Sciences: Physical Sciences] Uncovering inequalities in new knowledge learning by large language models across different languages

Proceedings of the National Academy of Sciences: Physical Sciences — Thu, 18 Dec 2025 08:00:00 GMT

[AAAS: Science: Table of Contents] State-independent ionic conductivity

AAAS: Science: Table of Contents — Thu, 18 Dec 2025 07:00:11 GMT

Science, Volume 390, Issue 6779, Page 1254-1258, December 2025.

[AAAS: Science: Table of Contents] Scientific production in the era of large language models

AAAS: Science: Table of Contents — Thu, 18 Dec 2025 07:00:11 GMT

Science, Volume 390, Issue 6779, Page 1240-1243, December 2025.

[ACS Nano: Latest Articles (ACS Publications)] [ASAP] van Hove Source for Ultralow Power Field-Effect Transistors

ACS Nano: Latest Articles (ACS Publications) — Wed, 17 Dec 2025 18:12:49 GMT

ACS Nano

DOI: 10.1021/acsnano.5c17157

[Wiley: Advanced Functional Materials: Table of Contents] Machine Learning‐assisted Ultrasensitive SERS Immunoassays Across Wide Concentration Ranges Toward Clinical Ovarian Cancer Diagnosis

Wiley: Advanced Functional Materials: Table of Contents — Wed, 17 Dec 2025 14:49:25 GMT

Advanced Functional Materials, Volume 35, Issue 51, December 16, 2025.

[Wiley: Advanced Functional Materials: Table of Contents] Prediction and Fine Screening of Small Molecular Passivation Materials for High‐Efficiency Perovskite Solar Cells Via an Enhanced Machine Learning Workflow

Wiley: Advanced Functional Materials: Table of Contents — Wed, 17 Dec 2025 14:49:25 GMT

Advanced Functional Materials, Volume 35, Issue 51, December 16, 2025.

[Wiley: Advanced Materials: Table of Contents] Tailoring Graphite Interlayers with Electron‐Acceptor Bridges Raises Ion Diffusion Kinetics for Ultrafast Charging Batteries

Wiley: Advanced Materials: Table of Contents — Wed, 17 Dec 2025 14:13:34 GMT

Advanced Materials, Volume 37, Issue 50, December 17, 2025.

[Wiley: Small: Table of Contents] Interfacial Catalysis Engineering of Solid Electrolyte Interphase Toward High‐Performance Batteries

Wiley: Small: Table of Contents — Wed, 17 Dec 2025 12:51:53 GMT

Small, Volume 21, Issue 50, December 17, 2025.

[Wiley: Small: Table of Contents] In Situ Construction of Dual‐Functional UiO‐66‐NH2 Coated Li1.3Al0.3Ti1.7(PO4)3 to Achieve Lithium Metal Cells with Efficient Ion Transport in Quasi‐Solid Electrolytes

Wiley: Small: Table of Contents — Wed, 17 Dec 2025 12:51:53 GMT

Small, Volume 21, Issue 50, December 17, 2025.

[Wiley: Small: Table of Contents] 1D Lithium‐Ion Transport in a LiMn2O4 Nanowire Cathode during Charge–Discharge Cycles

Wiley: Small: Table of Contents — Wed, 17 Dec 2025 12:51:53 GMT

Small, Volume 21, Issue 50, December 17, 2025.

[Wiley: Small: Table of Contents] A Reversible Zinc Metal Anode with an Inorganic/Organic Solid Electrolyte Interphase Enriched for Epitaxial Deposition Along the Zn (101) Plane

Wiley: Small: Table of Contents — Wed, 17 Dec 2025 12:51:53 GMT

Small, Volume 21, Issue 50, December 17, 2025.

[Nature Materials] Probing frozen solid electrolyte interphases

Nature Materials — Wed, 17 Dec 2025 00:00:00 GMT

Nature Materials, Published online: 17 December 2025; doi:10.1038/s41563-025-02443-z

Probing frozen solid electrolyte interphases

[Cell Reports Physical Science] Accelerated inorganic materials design with generative AI agents

Cell Reports Physical Science — Wed, 17 Dec 2025 00:00:00 GMT

[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Fluorinated Halide Solid Electrolytes for High-Voltage All-Solid-State Sodium-Ion Batteries Enabling Reversible Oxygen Redox

ACS Energy Letters: Latest Articles (ACS Publications) — Tue, 16 Dec 2025 20:00:00 GMT

ACS Energy Letters

DOI: 10.1021/acsenergylett.5c03248

[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Ionic Liquids in Quasi-Solid-State Li–S Batteries with Sulfide-Based Solid Electrolytes: A Density Functional Theory and Ab Initio Molecular Dynamics Study

The Journal of Physical Chemistry C: Latest Articles (ACS Publications) — Tue, 16 Dec 2025 14:13:16 GMT

The Journal of Physical Chemistry C

DOI: 10.1021/acs.jpcc.5c05916

[Wiley: Advanced Energy Materials: Table of Contents] How Machine Learning Has Driven the Development of Rechargeable Ion Batteries

Wiley: Advanced Energy Materials: Table of Contents — Tue, 16 Dec 2025 10:18:19 GMT

Advanced Energy Materials, Volume 15, Issue 47, December 16, 2025.

[Wiley: Advanced Energy Materials: Table of Contents] Interplay Between the Dissolved Mn2+ and Solid Electrolyte Interphases of Graphite Anode

Wiley: Advanced Energy Materials: Table of Contents — Tue, 16 Dec 2025 10:18:19 GMT

Advanced Energy Materials, Volume 15, Issue 47, December 16, 2025.

[Wiley: Advanced Energy Materials: Table of Contents] From HF Scavenging to Li‐Ion Transport Enhancement: Multifunctional Separator Enabling Stable Li Metal Batteries in Carbonate‐Based Electrolytes

Wiley: Advanced Energy Materials: Table of Contents — Tue, 16 Dec 2025 09:58:08 GMT

Advanced Energy Materials, EarlyView.

[Wiley: Advanced Energy Materials: Table of Contents] Insight Into All‐Solid‐State Lithium‐Sulfur Batteries: Challenges and Interface Engineering at the Electrode‐Sulfide Solid Electrolyte Interface

Wiley: Advanced Energy Materials: Table of Contents — Tue, 16 Dec 2025 09:45:18 GMT

Advanced Energy Materials, EarlyView.

[Proceedings of the National Academy of Sciences: Physical Sciences] Designing strongly coupled polaritonic structures via statistical machine learning

Proceedings of the National Academy of Sciences: Physical Sciences — Tue, 16 Dec 2025 08:00:00 GMT

[iScience] What Makes a Scent Trigger a Memory? A Cognitive Decomposition of Odor-Evoked Retrieval

iScience — Tue, 16 Dec 2025 00:00:00 GMT

[iScience] Combining DNA Methylation Features and Clinical Characteristics Predicts Ketamine Treatment Response for PTSD

iScience — Tue, 16 Dec 2025 00:00:00 GMT

[Chem] In situ cryogenic X-ray photoelectron spectroscopy unveils metastable components of the solid electrolyte interphase in Li-ion batteries

Chem — Tue, 16 Dec 2025 00:00:00 GMT

[RSC - Digital Discovery latest articles] Deep learning based SEM image analysis for predicting ionic conductivity in LiZr2(PO4)3-based solid electrolytes

RSC - Digital Discovery latest articles — Mon, 15 Dec 2025 00:00:00 GMT

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00232J, Paper

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[RSC - Digital Discovery latest articles] Hierarchical Attention Graph Learning with LLMs Enhancement for Molecular Solubility Prediction

RSC - Digital Discovery latest articles — Mon, 15 Dec 2025 00:00:00 GMT

Digital Discovery, 2025, Accepted Manuscript
DOI: 10.1039/D5DD00407A, Paper

Open Access

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[iScience] Interpretable machine learning for accessible dysphagia screening and staging in older adults

iScience — Mon, 15 Dec 2025 00:00:00 GMT

Gastroenterology; Health sciences; Internal medicine; Medical specialty; Medicine

[Joule] Dendrite suppression in garnet electrolytes via thermally induced compressive stress

Joule — Mon, 15 Dec 2025 00:00:00 GMT

[RSC - Digital Discovery latest articles] Hierarchical attention graph learning with LLM enhancement for molecular solubility prediction

RSC - Digital Discovery latest articles — Mon, 15 Dec 2025 00:00:00 GMT

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00407A, Paper

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

Yangxin Fan, Yinghui Wu, Roger H. French, Danny Perez, Michael G. Taylor, Ping Yang
Solubility quantifies the concentration of a molecule that can dissolve in a given solvent.
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[Wiley: Advanced Energy Materials: Table of Contents] Machine Learning Descriptors for Mapping Structure‐Property‐Performance Relationships of Perovskite Solar Cells

Wiley: Advanced Energy Materials: Table of Contents — Sat, 13 Dec 2025 07:01:43 GMT

Advanced Energy Materials, EarlyView.

[ACS Materials Letters: Latest Articles (ACS Publications)] [ASAP] Reaction Pathway Informed Strategy for Fast Solid-State Synthesis of Garnet-Type Solid Electrolyte

ACS Materials Letters: Latest Articles (ACS Publications) — Fri, 12 Dec 2025 14:10:55 GMT

ACS Materials Letters

DOI: 10.1021/acsmaterialslett.5c01262

[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Structural Aspects, Ionic Conductivity, and Electrochemical Properties of New Bromine-Substituted Alkali-Based Crystalline Phases MTa(Nb)X6–yBry (M = Li, Na, K; X = Cl, F)

ACS Energy Letters: Latest Articles (ACS Publications) — Fri, 12 Dec 2025 13:47:45 GMT

ACS Energy Letters

DOI: 10.1021/acsenergylett.5c02904

[APL Machine Learning Current Issue] Smart detection of plant nutrient deficiencies using machine learning and image fusion

APL Machine Learning Current Issue — Fri, 12 Dec 2025 00:00:00 GMT

[RSC - Digital Discovery latest articles] PEMD: a high-throughput simulation and analysis framework for solid polymer electrolytes

RSC - Digital Discovery latest articles — Fri, 12 Dec 2025 00:00:00 GMT

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00454C, Paper

Open Access

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[AI for Science - latest papers] Investigating CO adsorption on Cu(111) and Rh(111) surfaces using machine learning exchange-correlation functionals

AI for Science - latest papers — Fri, 12 Dec 2025 00:00:00 GMT

[iScience] Consensus machine learning identifies cell death gene signature for carotid artery stenosis diagnosis

iScience — Fri, 12 Dec 2025 00:00:00 GMT

[Wiley: Advanced Science: Table of Contents] A Cost‐Effective and Scalable Machine Learning Approach for Quality Assessment of Fresh Maize Kernel Using NIR Spectroscopy

Wiley: Advanced Science: Table of Contents — Thu, 11 Dec 2025 09:23:00 GMT

Advanced Science, Volume 12, Issue 46, December 11, 2025.

[Wiley: Advanced Science: Table of Contents] High‐Performance Zinc–Bromine Rechargeable Batteries Enabled by In‐Situ Formed Solid Electrolyte Interphase

Wiley: Advanced Science: Table of Contents — Thu, 11 Dec 2025 09:23:00 GMT

Advanced Science, Volume 12, Issue 46, December 11, 2025.

[Wiley: Advanced Science: Table of Contents] Nonalcoholic Fatty Liver Disease Exacerbates the Advancement of Renal Fibrosis by Modulating Renal CCR2+PIRB+ Macrophages Through the ANGPTL8/PIRB/ALOX5AP Axis

Wiley: Advanced Science: Table of Contents — Thu, 11 Dec 2025 09:23:00 GMT

Advanced Science, Volume 12, Issue 46, December 11, 2025.

[Wiley: Advanced Science: Table of Contents] Inverse Design of Metal‐Organic Frameworks for CH4/N2 Separation Enabled by Coupled Machine Learning and Genetic Algorithms

Wiley: Advanced Science: Table of Contents — Thu, 11 Dec 2025 09:23:00 GMT

Advanced Science, Volume 12, Issue 46, December 11, 2025.

[Wiley: Advanced Science: Table of Contents] Synergistic Effect of Dual‐Functional Groups in MOF‐Modified Separators for Efficient Lithium‐Ion Transport and Polysulfide Management of Lithium‐Sulfur Batteries

Wiley: Advanced Science: Table of Contents — Thu, 11 Dec 2025 09:23:00 GMT

Advanced Science, Volume 12, Issue 46, December 11, 2025.

[Proceedings of the National Academy of Sciences: Physical Sciences] Evaluating large language models in biomedical data science challenges through a classroom experiment

Proceedings of the National Academy of Sciences: Physical Sciences — Thu, 11 Dec 2025 08:00:00 GMT

[Wiley: Advanced Functional Materials: Table of Contents] Red Phosphorus@SnSe0.5S0.5 Core‐Shell Heterostructure Enables Superior Sodium Storage via Synergistic Ion Diffusion and Polyphosphides Trapping

Wiley: Advanced Functional Materials: Table of Contents — Thu, 11 Dec 2025 06:58:45 GMT

Advanced Functional Materials, Volume 35, Issue 50, December 9, 2025.

[Wiley: Advanced Functional Materials: Table of Contents] Dual‐Site Ni Nanoparticles‐Ru Clusters Anchored on Hierarchical Carbon with Decoupled Gas and Ion Diffusion Channels Enabling Low‐Overpotential, Highly Stable Li‐CO2 Batteries

Wiley: Advanced Functional Materials: Table of Contents — Thu, 11 Dec 2025 06:58:45 GMT

Advanced Functional Materials, Volume 35, Issue 50, December 9, 2025.

[RSC - Digital Discovery latest articles] Toward smart CO2 capture by the synthesis of metal organic frameworks using large language models

RSC - Digital Discovery latest articles — Thu, 11 Dec 2025 00:00:00 GMT

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00446B, Paper

Open Access

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[Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents] Tailoring Thermophysical Properties and Multiscale Machine Learning Modeling of 2D Nanomaterial‐Infused Beeswax as a Green NePCM for Sustainable Thermal Management Systems

Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents — Wed, 10 Dec 2025 09:54:56 GMT

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.

[RSC - Digital Discovery latest articles] Optimizing Data Extraction from Materials Science Literature: A Study of Tools Using Large Language Models

RSC - Digital Discovery latest articles — Wed, 10 Dec 2025 00:00:00 GMT

Digital Discovery, 2025, Accepted Manuscript
DOI: 10.1039/D5DD00482A, Paper

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

[RSC - Digital Discovery latest articles] Optimizing data extraction from materials science literature: a study of tools using large language models

RSC - Digital Discovery latest articles — Wed, 10 Dec 2025 00:00:00 GMT

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00482A, Paper

Open Access

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence.

[RSC - Chem. Sci. latest articles] Anion-based electrolyte chemistry for sodium-ion batteries: fundamentals, advances and perspectives

RSC - Chem. Sci. latest articles — Tue, 09 Dec 2025 00:00:00 GMT

Chem. Sci., 2026, 17,137-150
DOI: 10.1039/D5SC08154H, Review Article

Open Access

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Shu-Yu Li, Yong-Li Heng, Zhen-Yi Gu, Xiao-Tong Wang, Yan Liu, Xin-Ru Zhang, Zhong-Hui Sun, Dai-Huo Liu, Bao Li, Xing-Long Wu
This review examines anion-regulated electrolytes for sodium-ion batteries, including solvation structure and mechanism to enhance interfacial stability, ion transport, and extreme-temperature performance, while also outlining future directions.
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[RSC - Chem. Sci. latest articles] A solid composite electrolyte based on three-dimensional structured zeolite networks for high-performance solid-state lithium metal batteries

RSC - Chem. Sci. latest articles — Sun, 07 Dec 2025 00:00:00 GMT

Chem. Sci., 2026, Advance Article
DOI: 10.1039/D5SC05786H, Edge Article

Open Access

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[Proceedings of the National Academy of Sciences: Physical Sciences] Local equations describe unreasonably efficient stochastic algorithms in random K-SAT

Proceedings of the National Academy of Sciences: Physical Sciences — Fri, 05 Dec 2025 08:00:00 GMT

[ACS Materials Letters: Latest Articles (ACS Publications)] [ASAP] Beyond Conventional Sodium Superionic Conductor: Fe-Substituted Na3V2(PO4)2F3 Cathodes with Accelerated Charge Transport via Polyol Reflux for Sodium-Ion Batteries

ACS Materials Letters: Latest Articles (ACS Publications) — Thu, 04 Dec 2025 13:33:58 GMT

ACS Materials Letters

DOI: 10.1021/acsmaterialslett.5c01502

[Wiley: Advanced Science: Table of Contents] Non‐Monotonic Ion Conductivity in Lithium‐Aluminum‐Chloride Glass Solid‐State Electrolytes Explained by Cascading Hopping

Wiley: Advanced Science: Table of Contents — Thu, 04 Dec 2025 08:00:00 GMT

Advanced Science, Volume 12, Issue 45, December 4, 2025.

[Wiley: Advanced Science: Table of Contents] Re‐Purposing a Modular Origami Manipulator Into an Adaptive Physical Computer for Machine Learning and Robotic Perception

Wiley: Advanced Science: Table of Contents — Thu, 04 Dec 2025 08:00:00 GMT

Advanced Science, Volume 12, Issue 45, December 4, 2025.

[Wiley: Advanced Materials: Table of Contents] Ultrastable Calcium Metal Anodes Enabled by a Strongly Coordinated Electrolyte Derived Bilayer Solid Electrolyte Interphase

Wiley: Advanced Materials: Table of Contents — Thu, 04 Dec 2025 07:04:36 GMT

Advanced Materials, Volume 37, Issue 48, December 3, 2025.

[RSC - Digital Discovery latest articles] Understanding and mitigating distribution shifts for universal machine learning interatomic potentials

RSC - Digital Discovery latest articles — Thu, 04 Dec 2025 00:00:00 GMT

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00260E, Paper

Open Access

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[iScience] Physical Cognition in Altered Gravity: Link Between Sensorimotor and Cognitive Adaptability

iScience — Thu, 04 Dec 2025 00:00:00 GMT

[Wiley: Small: Table of Contents] Label‐Free Detection of Nuclear Envelope Nucleoporation using 2D Morphological Embeddings and Machine Learning

Wiley: Small: Table of Contents — Wed, 03 Dec 2025 15:24:49 GMT

Small, Volume 21, Issue 48, December 3, 2025.

[Wiley: Advanced Functional Materials: Table of Contents] Machine Learning‐Enabled Polymer Discovery for Enhanced Pulmonary siRNA Delivery

Wiley: Advanced Functional Materials: Table of Contents — Wed, 03 Dec 2025 08:00:00 GMT

Advanced Functional Materials, Volume 35, Issue 49, December 2, 2025.

[Wiley: Advanced Functional Materials: Table of Contents] Enhanced Potassium Ion Diffusion and Interface Stability Enabled by Potassiophilic rGO/CNTs/NaF Micro‐Lattice Aerogel for High‐Performance Potassium Metal Batteries

Wiley: Advanced Functional Materials: Table of Contents — Wed, 03 Dec 2025 08:00:00 GMT

Advanced Functional Materials, Volume 35, Issue 49, December 2, 2025.

[Nature Reviews Physics] Predicting high-entropy alloy phases with machine learning

Nature Reviews Physics — Wed, 03 Dec 2025 00:00:00 GMT

Nature Reviews Physics, Published online: 03 December 2025; doi:10.1038/s42254-025-00903-8

Omokhuwele Umoru explains how generative adversarial networks can help to predict the phases of high-entropy alloys.

[iScience] AI enhancing differential diagnosis of acute chronic obstructive pulmonary disease and acute heart failure

iScience — Wed, 03 Dec 2025 00:00:00 GMT

Cardiovascular medicine; Respiratory medicine; Machine learning

[iScience] United multi-omics and machine learning refine regulatory T cell-defined hepatocellular carcinoma subtypes

iScience — Wed, 03 Dec 2025 00:00:00 GMT

[Matter] Unknowium, beyond the banana, and AI discovery in materials science

Matter — Wed, 03 Dec 2025 00:00:00 GMT

[Wiley: Advanced Energy Materials: Table of Contents] Taming Metal–Solid Electrolyte Interface Instability via Metal Strain Hardening

Wiley: Advanced Energy Materials: Table of Contents — Tue, 02 Dec 2025 13:55:59 GMT

Advanced Energy Materials, Volume 15, Issue 45, December 2, 2025.

[Wiley: Advanced Energy Materials: Table of Contents] Self‐Liquefying Conformal Nanocoatings via Phase‐Convertible Ion Conductors for Stable All‐Solid‐State Batteries (Adv. Energy Mater. 45/2025)

Wiley: Advanced Energy Materials: Table of Contents — Tue, 02 Dec 2025 13:55:59 GMT

Advanced Energy Materials, Volume 15, Issue 45, December 2, 2025.

[Wiley: Advanced Energy Materials: Table of Contents] Multiscale Design Strategies of Interface‐Stabilized Solid Electrolytes and Dynamic Interphase Decoding from Atomic‐to‐Macroscopic Perspectives

Wiley: Advanced Energy Materials: Table of Contents — Tue, 02 Dec 2025 13:55:59 GMT

Advanced Energy Materials, Volume 15, Issue 45, December 2, 2025.

[Wiley: Advanced Energy Materials: Table of Contents] Self‐Liquefying Conformal Nanocoatings via Phase‐Convertible Ion Conductors for Stable All‐Solid‐State Batteries

Wiley: Advanced Energy Materials: Table of Contents — Tue, 02 Dec 2025 13:55:59 GMT

Advanced Energy Materials, Volume 15, Issue 45, December 2, 2025.

[Chinese Chemical Society: CCS Chemistry: Table of Contents] LoRA-Chem: Modular Machine Learning for Multitask Prediction in Organic Reactions

Chinese Chemical Society: CCS Chemistry: Table of Contents — Tue, 02 Dec 2025 04:48:31 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202506542

[Chinese Chemical Society: CCS Chemistry: Table of Contents] LoRA-Chem: Modular Machine Learning for Multitask Prediction in Organic Reactions

Chinese Chemical Society: CCS Chemistry: Table of Contents — Tue, 02 Dec 2025 04:48:31 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202506542

[iScience] Dimensionality modulated generative AI for safe biomedical dataset augmentation

iScience — Tue, 02 Dec 2025 00:00:00 GMT

[Wiley: Advanced Intelligent Discovery: Table of Contents] Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Wiley: Advanced Intelligent Discovery: Table of Contents — Mon, 01 Dec 2025 22:39:43 GMT

Advanced Intelligent Discovery, EarlyView.

[APL Machine Learning Current Issue] RTNinja : A generalized machine learning framework for analyzing random telegraph noise signals in nanoelectronic devices

APL Machine Learning Current Issue — Mon, 01 Dec 2025 00:00:00 GMT

[iScience] A pilot study: Incorporating Treponema pallidum antigens into machine learning models for accurate syphilis treatment outcome assessment

iScience — Mon, 01 Dec 2025 00:00:00 GMT

Health informatics; disease; artificial intelligence applications

[iScience] Bathymetry of the Philippine sea with convolution neural network from multisource marine geodetic data

iScience — Fri, 28 Nov 2025 00:00:00 GMT

Earth sciences; oceanography; geodesy; machine learning

[iScience] Interpretable machine learning for urothelial cells classification and risk scoring in urine cytology

iScience — Thu, 27 Nov 2025 00:00:00 GMT

Health sciences; Cancer; Machine learning

[Wiley: Advanced Intelligent Discovery: Table of Contents] Machine Learning‐Assisted Second‐Order Perturbation Theory for Chemical Potential Correction Toward Hubbard U Determination

Wiley: Advanced Intelligent Discovery: Table of Contents — Wed, 26 Nov 2025 03:49:32 GMT

Advanced Intelligent Discovery, EarlyView.

[RSC - Chem. Sci. latest articles] Data-driven approach to elucidate the correlation between photocatalytic activity and rate constants from excited states

RSC - Chem. Sci. latest articles — Tue, 25 Nov 2025 00:00:00 GMT

Chem. Sci., 2026, 17,176-186
DOI: 10.1039/D5SC06465A, Edge Article

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Ryuga Kunisada, Manami Hayashi, Tabea Rohlfs, Taiki Nagano, Koki Sano, Naoto Inai, Naoki Noto, Takuya Ogaki, Yasunori Matsui, Hiroshi Ikeda, Olga García Mancheño, Takeshi Yanai, Susumu Saito
A data-driven framework integrating machine learning and quantum chemical calculations enables elucidation of how rate constants from excited states govern the photocatalytic activity of organic photosensitizers.
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[Wiley: Chinese Journal of Chemistry: Table of Contents] Locked Coplanar Conformation Boosts Rapid Electron/Ion Transport in Linear Polyimide Cathodes for Sodium‐Ion Storage

Wiley: Chinese Journal of Chemistry: Table of Contents — Mon, 24 Nov 2025 07:33:36 GMT

Chinese Journal of Chemistry, EarlyView.

[APL Machine Learning Current Issue] A hybrid neural architecture: Online attosecond x-ray characterization

APL Machine Learning Current Issue — Fri, 21 Nov 2025 00:00:00 GMT

[Joule] Accelerated discovery of CO2-to-C3-hydrocarbon electrocatalysts with human-in-the-loop

Joule — Fri, 21 Nov 2025 00:00:00 GMT

[Joule] Redox-mediated solid-state doping of Spiro-OMeTAD for efficient and robust perovskite photovoltaics

Joule — Fri, 21 Nov 2025 00:00:00 GMT

[AI for Science - latest papers] Learning to be simple

AI for Science - latest papers — Thu, 20 Nov 2025 00:00:00 GMT

[Wiley: Advanced Intelligent Discovery: Table of Contents] Taguchi–Bayesian Sampling: A Roadmap for Polymer Database Construction Toward Small Representative Machine Learning

Wiley: Advanced Intelligent Discovery: Table of Contents — Wed, 19 Nov 2025 05:00:22 GMT

Advanced Intelligent Discovery, EarlyView.

[iScience] An explainable machine learning model predicts 30-day readmission after vertebral augmentation

iScience — Wed, 19 Nov 2025 00:00:00 GMT

Orthopedics; Machine learning

[RSC - Chem. Sci. latest articles] The agentic age of predictive chemical kinetics

RSC - Chem. Sci. latest articles — Wed, 19 Nov 2025 00:00:00 GMT

Chem. Sci., 2026, 17,27-35
DOI: 10.1039/D5SC07692G, Perspective

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Alon Grinberg Dana
From LLM reasoning to action: specialized agents coordinate kinetic modeling to produce transparent, uncertainty-aware, reproducible mechanisms.
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[Wiley: SmartMat: Table of Contents] Machine Learning Design of Soft Magnetoresponsive Materials With Multidirectional Fields

Wiley: SmartMat: Table of Contents — Tue, 18 Nov 2025 08:00:00 GMT

SmartMat, Volume 6, Issue 6, December 2025.

[RSC - Digital Discovery latest articles] Advancing metal organic framework and covalent organic framework design via the digital-intelligent paradigm

RSC - Digital Discovery latest articles — Tue, 18 Nov 2025 00:00:00 GMT

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00401B, Review Article

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[Applied Physics Reviews Current Issue] Synergistic integration of metasurfaces and quantum photonics: Pathways to next-generation technologies

Applied Physics Reviews Current Issue — Mon, 17 Nov 2025 00:00:00 GMT

[AI for Science - latest papers] Universal machine learning potentials for systems with reduced dimensionality

AI for Science - latest papers — Mon, 17 Nov 2025 00:00:00 GMT

[Cell Reports Physical Science] Conjugated polyelectrolyte-aptamer hybrid for organic-electrochemical-transistor-based sensing

Cell Reports Physical Science — Mon, 17 Nov 2025 00:00:00 GMT

[Cell Reports Physical Science] CatBench framework for benchmarking machine learning interatomic potentials in adsorption energy predictions for heterogeneous catalysis

Cell Reports Physical Science — Mon, 17 Nov 2025 00:00:00 GMT

[Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents] Liquid‐Phase Synthesis of Halide Solid Electrolytes for All‐Solid‐State Batteries Using Organic Solvents

Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents — Fri, 14 Nov 2025 14:05:17 GMT

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.

[AI for Science - latest papers] TorchSim: an efficient atomistic simulation engine in PyTorch

AI for Science - latest papers — Fri, 14 Nov 2025 00:00:00 GMT

[AI for Science - latest papers] Graph learning metallic glass discovery from Wikipedia

AI for Science - latest papers — Fri, 14 Nov 2025 00:00:00 GMT

[ACS Materials Au: Latest Articles (ACS Publications)] [ASAP] Molecular Modeling-Based Machine Learning for Accurate Prediction of Gas Diffusivity and Permeability in Metal–Organic Frameworks

ACS Materials Au: Latest Articles (ACS Publications) — Wed, 12 Nov 2025 18:15:35 GMT

ACS Materials Au

DOI: 10.1021/acsmaterialsau.5c00111

[Recent Articles in PRX Energy] Dynamic Vacancy Levels in ${\mathrm{Cs}\mathrm{Pb}\mathrm{Cl}}_{3}$ Obey Equilibrium Defect Thermodynamics

Recent Articles in PRX Energy — Wed, 12 Nov 2025 10:00:00 GMT

Author(s): Irea Mosquera-Lois and Aron Walsh

[PRX Energy 4, 043008] Published Wed Nov 12, 2025

[Wiley: Advanced Intelligent Discovery: Table of Contents] Accelerating Primary Screening of USP8 Inhibitors from Drug Repurposing Databases with Tree‐Based Machine Learning

Wiley: Advanced Intelligent Discovery: Table of Contents — Tue, 11 Nov 2025 04:16:54 GMT

Advanced Intelligent Discovery, EarlyView.

[Joule] Entropy-guided discovery of denary trirutile antimonates for electrocatalytic chlorine evolution

Joule — Tue, 11 Nov 2025 00:00:00 GMT

[AI for Science - latest papers] Surface stability modeling with universal machine learning interatomic potentials: a comprehensive cleavage energy benchmarking study

AI for Science - latest papers — Fri, 07 Nov 2025 00:00:00 GMT

[Joule] Li–Si compound anodes enabling high-performance all-solid-state Li-ion batteries

Joule — Fri, 07 Nov 2025 00:00:00 GMT

[ACS Physical Chemistry Au: Latest Articles (ACS Publications)] [ASAP] Machine Learning as a Method for Retrieving Pressure Values by Analyzing Spectral Line Parameters: The Hydrochloric Acid Case

ACS Physical Chemistry Au: Latest Articles (ACS Publications) — Tue, 04 Nov 2025 19:09:10 GMT

ACS Physical Chemistry Au

DOI: 10.1021/acsphyschemau.5c00097

[Applied Physics Reviews Current Issue] Dynamic landscape of chemiresistive breathomic nanosensors based on fifth-generation chips for complex disease diagnosis and healthcare monitoring

Applied Physics Reviews Current Issue — Tue, 04 Nov 2025 00:00:00 GMT

[tandf: Materials Research Letters: Table of Contents] Machine learning-assisted design of strong and ductile BCC high-entropy alloys

tandf: Materials Research Letters: Table of Contents — Thu, 30 Oct 2025 12:22:23 GMT

Volume 13, Issue 12, December 2025, Page 1260-1268
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[Wiley: InfoMat: Table of Contents] Delicate design of lithium‐ion bridges in hybrid solid electrolyte for wide‐temperature adaptive solid‐state lithium metal batteries

Wiley: InfoMat: Table of Contents — Wed, 29 Oct 2025 00:36:10 GMT

InfoMat, EarlyView.

[APL Machine Learning Current Issue] Building an affordable self-driving lab: Practical machine learning experiments for physics education using Internet-of-Things

APL Machine Learning Current Issue — Wed, 29 Oct 2025 00:00:00 GMT

[APL Machine Learning Current Issue] Data integration and data fusion approaches in self-driving labs: A perspective

APL Machine Learning Current Issue — Wed, 29 Oct 2025 00:00:00 GMT

[Applied Physics Reviews Current Issue] Strain engineering of van Hove singularity and coupled itinerant ferromagnetism in quasi-2D oxide superlattices

Applied Physics Reviews Current Issue — Tue, 28 Oct 2025 00:00:00 GMT

[Wiley: Advanced Intelligent Discovery: Table of Contents] Machine Learning‐Enhanced Random Matrix Theory Design for Human Immunodeficiency Virus Vaccine Development

Wiley: Advanced Intelligent Discovery: Table of Contents — Mon, 27 Oct 2025 03:21:44 GMT

Advanced Intelligent Discovery, EarlyView.

[Applied Physics Reviews Current Issue] 3D-printed lithium-metal batteries: Multiscale architectures, hybrid technologies, and monolithic integration for next-generation energy storage

Applied Physics Reviews Current Issue — Thu, 23 Oct 2025 00:00:00 GMT

[Wiley: Advanced Intelligent Discovery: Table of Contents] Harnessing Large Language Models to Advance Microbiome Research: From Sequence Analysis to Clinical Applications

Wiley: Advanced Intelligent Discovery: Table of Contents — Tue, 21 Oct 2025 05:48:44 GMT

Advanced Intelligent Discovery, EarlyView.

[AI for Science - latest papers] AInstein: numerical Einstein metrics via machine learning

AI for Science - latest papers — Thu, 16 Oct 2025 23:00:00 GMT

[Matter] SpectroGen: A physically informed generative artificial intelligence for accelerated cross-modality spectroscopic materials characterization

Matter — Tue, 14 Oct 2025 00:00:00 GMT

[Chem] Precisely modulating Li2CO3 coverage on Ni-rich cathode boosts sulfide solid-state lithium battery performance

Chem — Tue, 14 Oct 2025 00:00:00 GMT

[Matter] Dynamic pressure mapping of infant cervical spines using a wearable magnetoelastic patch

Matter — Fri, 10 Oct 2025 00:00:00 GMT

[Matter] Machine learning-driven ligand engineering decodes and controls structural distortions in 2D perovskites

Matter — Fri, 10 Oct 2025 00:00:00 GMT

[Applied Physics Reviews Current Issue] The enduring legacy of scanning spreading resistance microscopy: Overview, advancements, and future directions

Applied Physics Reviews Current Issue — Wed, 08 Oct 2025 00:00:00 GMT

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Oxygen Vacancy-Modulated Molybdenum Oxide/Montmorillonite Heterointerface Nanoarchitectures for Plasmon-Enhanced Solar-Osmotic Energy Harvesting

Chinese Chemical Society: CCS Chemistry: Table of Contents — Mon, 06 Oct 2025 03:22:16 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202506132

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Oxygen Vacancy-Modulated Molybdenum Oxide/Montmorillonite Heterointerface Nanoarchitectures for Plasmon-Enhanced Solar-Osmotic Energy Harvesting

Chinese Chemical Society: CCS Chemistry: Table of Contents — Mon, 06 Oct 2025 03:22:16 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202506132

[APL Machine Learning Current Issue] Deep learning model of myofilament cooperative activation and cross-bridge cycling in cardiac muscle

APL Machine Learning Current Issue — Fri, 03 Oct 2025 00:00:00 GMT

[Wiley: Advanced Intelligent Discovery: Table of Contents] Discovery of Novel Materials with Giant Dielectric Constants via First‐Principles Phonon Calculations and Machine Learning

Wiley: Advanced Intelligent Discovery: Table of Contents — Tue, 30 Sep 2025 06:30:24 GMT

Advanced Intelligent Discovery, EarlyView.

[AI for Science - latest papers] FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential

AI for Science - latest papers — Mon, 29 Sep 2025 23:00:00 GMT

[Wiley: Advanced Intelligent Discovery: Table of Contents] Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Wiley: Advanced Intelligent Discovery: Table of Contents — Wed, 24 Sep 2025 13:21:08 GMT

Advanced Intelligent Discovery, EarlyView.

[Recent Articles in PRX Energy] Reconstructions and Dynamics of $β$-Lithium Thiophosphate Surfaces

Recent Articles in PRX Energy — Tue, 26 Aug 2025 10:00:00 GMT

Author(s): Hanna Türk, Davide Tisi, and Michele Ceriotti

Machine-learning-based molecular dynamics simulations of the solid electrolyte $β$ -Li $_{3}$ PS $_{4}$ under realistic conditions reveal dynamic surface structure and reactivity.

[PRX Energy 4, 033010] Published Tue Aug 26, 2025

[Matter] CGformer: Transformer-enhanced crystal graph network with global attention for material property prediction

Matter — Wed, 20 Aug 2025 00:00:00 GMT

[Recent Articles in PRX Energy] Large-Scale Simulation Unveiled Superior Potassium-Based Solid Electrolyte with High Ionic Conductivity and Excellent Electrochemical Stability in ${M}_{5}{\mathrm{YSi}}_{4}{\mathrm{O}}_{12}$ ($M=\mathrm{Li},\mathrm{K}$)

Recent Articles in PRX Energy — Thu, 14 Aug 2025 10:00:00 GMT

Author(s): Zhao Li, Jiaxiang Li, Congwei Xie, Keith Butler, Fei Du, and Yu Xie

[PRX Energy 4, 033007] Published Thu Aug 14, 2025

[Wiley: Advanced Intelligent Discovery: Table of Contents] Interpretable Machine Learning for Solvent‐Dependent Carrier Mobility in Solution‐Processed Organic Thin Films

Wiley: Advanced Intelligent Discovery: Table of Contents — Fri, 08 Aug 2025 09:54:45 GMT

Advanced Intelligent Discovery, EarlyView.

[Wiley: Advanced Intelligent Discovery: Table of Contents] Macrophage Phenotype Detection Methodology on Textured Surfaces via Nuclear Morphology Using Machine Learning

Wiley: Advanced Intelligent Discovery: Table of Contents — Fri, 01 Aug 2025 08:40:28 GMT

Advanced Intelligent Discovery, EarlyView.

[Wiley: Advanced Intelligent Discovery: Table of Contents] Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation

Wiley: Advanced Intelligent Discovery: Table of Contents — Thu, 24 Jul 2025 10:45:19 GMT

Advanced Intelligent Discovery, EarlyView.

[Recent Articles in PRX Energy] Origin of Intrinsically Low Thermal Conductivity in a Garnet-Type Solid Electrolyte: Linking Lattice and Ionic Dynamics with Thermal Transport

Recent Articles in PRX Energy — Thu, 17 Jul 2025 10:00:00 GMT

[PRX Energy 4, 033004] Published Thu Jul 17, 2025

[Recent Articles in PRX Energy] A Comparative Study of Solid Electrolyte Interphase Evolution in Ether and Ester-Based Electrolytes for $\mathrm{Na}$-ion Batteries

Recent Articles in PRX Energy — Tue, 15 Jul 2025 10:00:00 GMT

Author(s): Liang Zhao, Sara I.R. Costa, Yue Chen, Jack R. Fitzpatrick, Andrew J. Naylor, Oleg Kolosov, and Nuria Tapia-Ruiz

[PRX Energy 4, 033002] Published Tue Jul 15, 2025

[Wiley: Advanced Intelligent Discovery: Table of Contents] Autonomous Machine Learning‐Based Classification and Arrangement of Submillimeter Objects Using a Capillary Force Gripper

Wiley: Advanced Intelligent Discovery: Table of Contents — Wed, 09 Jul 2025 08:01:30 GMT

Advanced Intelligent Discovery, EarlyView.

[Wiley: Advanced Intelligent Discovery: Table of Contents] Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials

Wiley: Advanced Intelligent Discovery: Table of Contents — Wed, 09 Jul 2025 07:56:18 GMT

Advanced Intelligent Discovery, EarlyView.

[Wiley: Advanced Intelligent Discovery: Table of Contents] A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

Wiley: Advanced Intelligent Discovery: Table of Contents — Fri, 27 Jun 2025 08:27:19 GMT

Advanced Intelligent Discovery, EarlyView.

[Wiley: Advanced Intelligent Discovery: Table of Contents] Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review

Wiley: Advanced Intelligent Discovery: Table of Contents — Fri, 27 Jun 2025 08:15:35 GMT

Advanced Intelligent Discovery, EarlyView.

[ACS Materials Au: Latest Articles (ACS Publications)] [ASAP] Historical Data Mining Deep Dive into Machine Learning-Aided 2D Materials Research in Electrochemical Applications

ACS Materials Au: Latest Articles (ACS Publications) — Mon, 23 Jun 2025 15:22:16 GMT

ACS Materials Au

DOI: 10.1021/acsmaterialsau.5c00030

[Wiley: Advanced Intelligent Discovery: Table of Contents] Machine Learning‐Assisted Infectious Disease Detection in Low‐Income Areas: Toward Rapid Triage of Dengue and Zika Virus Using Open‐Source Hardware

Wiley: Advanced Intelligent Discovery: Table of Contents — Mon, 23 Jun 2025 08:20:28 GMT

Advanced Intelligent Discovery, EarlyView.

[Wiley: Advanced Intelligent Discovery: Table of Contents] What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Wiley: Advanced Intelligent Discovery: Table of Contents — Fri, 20 Jun 2025 08:36:19 GMT

Advanced Intelligent Discovery, EarlyView.

[Wiley: Advanced Intelligent Discovery: Table of Contents] Predicting High‐Resolution Spatial and Spectral Features in Mass Spectrometry Imaging with Machine Learning and Multimodal Data Fusion

Wiley: Advanced Intelligent Discovery: Table of Contents — Wed, 18 Jun 2025 08:10:58 GMT

Advanced Intelligent Discovery, EarlyView.

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Efficient Pure Organic Near-Infrared Room-Temperature Phosphorescence Based on n/π Orbital Decoupling

Chinese Chemical Society: CCS Chemistry: Table of Contents — Sat, 14 Jun 2025 05:08:51 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202405319

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Efficient Pure Organic Near-Infrared Room-Temperature Phosphorescence Based on n/π Orbital Decoupling

Chinese Chemical Society: CCS Chemistry: Table of Contents — Sat, 14 Jun 2025 05:08:51 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202405319

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Identification of Cu-N2 Sites for Zn-Air Batteries in Harsh Electrolytes: Computer Virtual Screening, Machine Learning, and Practical Application

Chinese Chemical Society: CCS Chemistry: Table of Contents — Sat, 14 Jun 2025 04:39:17 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202505577

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Identification of Cu-N2 Sites for Zn-Air Batteries in Harsh Electrolytes: Computer Virtual Screening, Machine Learning, and Practical Application

Chinese Chemical Society: CCS Chemistry: Table of Contents — Sat, 14 Jun 2025 04:39:17 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202505577

[Recent Articles in PRX Energy] Correlating Local Morphology and Charge Dynamics via Kelvin Probe Force Microscopy to Explain Photoelectrode Performance

Recent Articles in PRX Energy — Mon, 09 Jun 2025 10:00:00 GMT

Author(s): Maryam Pourmahdavi, Mauricio Schieda, Ragle Raudsepp, Steffen Fengler, Jiri Kollmann, Yvonne Pieper, Thomas Dittrich, Thomas Klassen, and Francesca M. Toma

[PRX Energy 4, 023010] Published Mon Jun 09, 2025

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Minimal Sulfur-Grafted Graphite Anode with Accelerated Interfacial Kinetics for Fast-Charging Lithium-Ion Batteries

Chinese Chemical Society: CCS Chemistry: Table of Contents — Wed, 28 May 2025 08:32:07 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202505705

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Minimal Sulfur-Grafted Graphite Anode with Accelerated Interfacial Kinetics for Fast-Charging Lithium-Ion Batteries

Chinese Chemical Society: CCS Chemistry: Table of Contents — Wed, 28 May 2025 08:32:07 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202505705

[Recent Articles in PRX Energy] Resolving the Solvation Structure and Transport Properties of Aqueous Zinc Electrolytes from Salt-in-Water to Water-in-Salt Using Neural Network Potential

Recent Articles in PRX Energy — Fri, 11 Apr 2025 10:00:00 GMT

[PRX Energy 4, 023004] Published Fri Apr 11, 2025

[Recent Articles in PRX Energy] Crystal Structure Prediction and Phase Stability in Highly Anharmonic Silver-Based Chalcohalide Antiperovskites

Recent Articles in PRX Energy — Thu, 03 Apr 2025 10:00:00 GMT

Author(s): Pol Benítez, Cibrán López, Cong Liu, Ivan Caño, Josep-Lluís Tamarit, Edgardo Saucedo, and Claudio Cazorla

[PRX Energy 4, 023002] Published Thu Apr 03, 2025

[Recent Articles in PRX Energy] Unraveling Temperature-Induced Vacancy Clustering in Tungsten: From Direct Microscopy to Atomistic Insights via Data-Driven Bayesian Sampling

Recent Articles in PRX Energy — Tue, 25 Feb 2025 10:00:00 GMT

Author(s): Anruo Zhong, Clovis Lapointe, Alexandra M. Goryaeva, Kazuto Arakawa, Manuel Athènes, and Mihai-Cosmin Marinica

[PRX Energy 4, 013008] Published Tue Feb 25, 2025

[Recent Articles in PRX Energy] Constant-Current Nonequilibrium Molecular Dynamics Approach for Accelerated Computation of Ionic Conductivity Including Ion-Ion Correlation

Recent Articles in PRX Energy — Wed, 19 Feb 2025 10:00:00 GMT

Author(s): Ryoma Sasaki, Yoshitaka Tateyama, and Debra J. Searles

[PRX Energy 4, 013005] Published Wed Feb 19, 2025

[Recent Articles in PRX Energy] Detecting Attacks and Estimating States of Power Grids from Partial Observations with Machine Learning

Recent Articles in PRX Energy — Tue, 04 Feb 2025 10:00:00 GMT

Author(s): Zheng-Meng Zhai, Mohammadamin Moradi, and Ying-Cheng Lai

[PRX Energy 4, 013003] Published Tue Feb 04, 2025

[Recent Articles in PRX Energy] 3D Reconstruction of a Nuclear Reactor by Muon Tomography: Structure Validation and Anomaly Detection

Recent Articles in PRX Energy — Tue, 28 Jan 2025 10:00:00 GMT

Author(s): Baptiste Lefevre, Julien Vogel, Héctor Gomez, David Attié, Laurent Gallego, Philippe Gonzales, Bertrand Lesage, Philippe Mas, and Daniel Pomarède

[PRX Energy 4, 013002] Published Tue Jan 28, 2025

[Recent Articles in PRX Energy] Determining Parameters of Metal-Halide Perovskites Using Photoluminescence with Bayesian Inference

Recent Articles in PRX Energy — Tue, 14 Jan 2025 10:00:00 GMT

Author(s): Manuel Kober-Czerny, Akash Dasgupta, Seongrok Seo, Florine M. Rombach, David P. McMeekin, Heon Jin, and Henry J. Snaith

Experiment and machine learning are combined to extract key material parameters and insight into charge carrier transport in metal halide perovskites for solar cell applications.

[PRX Energy 4, 013001] Published Tue Jan 14, 2025

[Recent Articles in PRX Energy] Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network

Recent Articles in PRX Energy — Wed, 12 Jun 2024 10:00:00 GMT

Author(s): Hengrui Zhang (张恒睿), Tianxing Lai (来天行), Jie Chen, Arumugam Manthiram, James M. Rondinelli, and Wei Chen

[PRX Energy 3, 023006] Published Wed Jun 12, 2024

[Recent Articles in PRX Energy] Temperature Impact on Lithium Metal Morphology in Lithium Reservoir-Free Solid-State Batteries

Recent Articles in PRX Energy — Fri, 17 May 2024 10:00:00 GMT

Author(s): Min-Gi Jeong, Kelsey B. Hatzell, Sourim Banerjee, Bairav S. Vishnugopi, and Partha P. Mukherjee

[PRX Energy 3, 023003] Published Fri May 17, 2024

[Recent Articles in Rev. Mod. Phys.] Colloquium: Advances in automation of quantum dot devices control

Recent Articles in Rev. Mod. Phys. — Fri, 17 Feb 2023 10:00:00 GMT

Author(s): Justyna P. Zwolak and Jacob M. Taylor

[Rev. Mod. Phys. 95, 011006] Published Fri Feb 17, 2023

[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Hydrogen as promoter and inhibitor of superionicity: A case study on Li-N-H systems

PRB: Dynamics, dynamical systems, lattice effects, quantum solids — Mon, 26 Jul 2010 10:00:00 GMT

Author(s): Andreas Blomqvist, C. Moysés Araújo, Ralph H. Scheicher, Pornjuk Srepusharawoot, Wen Li, Ping Chen, and Rajeev Ahuja

[Phys. Rev. B 82, 024304] Published Mon Jul 26, 2010

[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Nonadiabatic effects of rattling phonons and $4f$ excitations in $\text{Pr}{({\text{Os}}_{1−x}{\text{Ru}}_{x})}_{4}{\text{Sb}}_{12}$

PRB: Dynamics, dynamical systems, lattice effects, quantum solids — Fri, 18 Jun 2010 10:00:00 GMT

Author(s): Peter Thalmeier

[Phys. Rev. B 81, 224305] Published Fri Jun 18, 2010

[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Ionic conductivity of nanocrystalline yttria-stabilized zirconia: Grain boundary and size effects

PRB: Dynamics, dynamical systems, lattice effects, quantum solids — Mon, 10 May 2010 10:00:00 GMT

Author(s): O. J. Durá, M. A. López de la Torre, L. Vázquez, J. Chaboy, R. Boada, A. Rivera-Calzada, J. Santamaria, and C. Leon

[Phys. Rev. B 81, 184301] Published Mon May 10, 2010

[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Calculating the anharmonic free energy from first principles

PRB: Dynamics, dynamical systems, lattice effects, quantum solids — Fri, 07 May 2010 10:00:00 GMT

Author(s): Zhongqing Wu

[Phys. Rev. B 81, 172301] Published Fri May 07, 2010

[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Phason dynamics in one-dimensional lattices

PRB: Dynamics, dynamical systems, lattice effects, quantum solids — Thu, 25 Feb 2010 10:00:00 GMT

Author(s): Hansjörg Lipp, Michael Engel, Steffen Sonntag, and Hans-Rainer Trebin

[Phys. Rev. B 81, 064302] Published Thu Feb 25, 2010

[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] Ab initio construction of interatomic potentials for uranium dioxide across all interatomic distances

PRB: Dynamics, dynamical systems, lattice effects, quantum solids — Wed, 25 Nov 2009 10:00:00 GMT

Author(s): P. Tiwary, A. van de Walle, and N. Grønbech-Jensen

[Phys. Rev. B 80, 174302] Published Wed Nov 25, 2009

[PRB: Dynamics, dynamical systems, lattice effects, quantum solids] One-dimensional nanostructure-guided chain reactions: Harmonic and anharmonic interactions

PRB: Dynamics, dynamical systems, lattice effects, quantum solids — Fri, 13 Nov 2009 10:00:00 GMT

Author(s): Nitish Nair and Michael S. Strano

[Phys. Rev. B 80, 174301] Published Fri Nov 13, 2009

My Customized Papers

[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

[Wiley: ENERGY & 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

[Wiley: Advanced Materials: Table of Contents] Customizing Ion Transport by Anionphilic Nanofiber‐Polymer Electrolyte for Stable Zinc Metal Batteries

[cond-mat updates on arXiv.org] A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse design

[ScienceDirect Publication: Progress in Materials Science] Advanced simulations from DFT to machine learning for solid-state hydrogen storage: fundamentals, progresses, challenges and perspectives

[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

[Wiley: ENERGY & 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

[Wiley: Advanced Materials: Table of Contents] Customizing Ion Transport by Anionphilic Nanofiber‐Polymer Electrolyte for Stable Zinc Metal Batteries

[cond-mat updates on arXiv.org] A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse design

[cond-mat updates on arXiv.org] Protein-Water Energy Transfer via Anharmonic Low-Frequency Vibrations

[cond-mat updates on arXiv.org] Interplay of Structure and Dynamics in Solid Polymer Electrolytes: a Molecular Dynamics Study of LiPF6/polypropylene carbonate

[cond-mat updates on arXiv.org] DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations

[cond-mat updates on arXiv.org] Bloch oscillations of helicoidal spin-orbit coupled Bose-Einstein condensates in deep optical lattices

[cond-mat updates on arXiv.org] Tuning Separator Chemistry: Improving Zn Anode Compatibility via Functionalized Chitin Nanofibers

[cond-mat updates on arXiv.org] Unsupervised Topological Phase Discovery in Periodically Driven Systems via Floquet-Bloch State

[Nature Communications] Thermotropic liquid-assisted interface management enables efficient and stable perovskite solar cells and modules

[ChemRxiv] A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data

[Nature Communications] Uncertainty-aware machine learning to predict non-cancer human toxicity for the global chemicals market

[ChemRxiv] Learning EXAFS from atomic structure through physics-informed machine learning

[ChemRxiv] Defined by Shape: Elucidating the Molecular Recognition of Dynamic Loops with Covalent Ligands

[ChemRxiv] QuantumPDB: A Workflow for High-Throughput Quantum Cluster Model Generation from Protein Structures

[ChemRxiv] Generalization and Usability of Co-Folded GPCR–Ligand Complexes: A Physics-Guided Assessment

[Communications Physics] Interpolation-based coordinate descent method for parameterized quantum circuits

[ScienceDirect Publication: Journal of Energy Storage] Optimizing solid electrolyte interphase with KOTF for dendrites-free and high-performance Lithium Metal Batteries

[ScienceDirect Publication: Journal of Energy Storage] A hierarchical sandwich Li6.4Ga0.2La3Zr2O12/ZIF-8@SiO2/PVDF-HFP heterostructure with high ionic conductivity for dendrite-free solid-state lithium batteries

[ScienceDirect Publication: Journal of Energy Storage] Hierarchical rose-like VS2 with sulfur vacancies for high-performance all-solid-state lithium-ion batteries

[ScienceDirect Publication: Journal of Energy Storage] Prediction of Lithium-ion battery states via combination of implantable sensors and machine learning

[ScienceDirect Publication: Journal of Energy Storage] A review on metal–organic framework-based polymer solid-state electrolytes for energy storage

[ScienceDirect Publication: Computational Materials Science] Descriptor and graph-based molecular representations in prediction of copolymer properties using machine learning

[Recent Articles in Phys. Rev. B] Signatures of coherent phonon transport in frequency-dependent lattice thermal conductivity

[Wiley: Advanced Energy Materials: Table of Contents] Accelerating the Discovery of High‐Conductivity Glass Electrolytes via Machine Learning

[cond-mat updates on arXiv.org] Physically-Constrained Autoencoder-Assisted Bayesian Optimization for Refinement of High-Dimensional Defect-Sensitive Single Crystalline Structure

[Nature Communications] Thermotropic liquid-assisted interface management enables efficient and stable perovskite solar cells and modules

[ChemRxiv] A Computational Community Blind Challenge on Pan-Coronavirus Drug Discovery Data

[Nature Communications] Uncertainty-aware machine learning to predict non-cancer human toxicity for the global chemicals market

[ChemRxiv] Learning EXAFS from atomic structure through physics-informed machine learning

[ChemRxiv] Defined by Shape: Elucidating the Molecular Recognition of Dynamic Loops with Covalent Ligands

[ChemRxiv] QuantumPDB: A Workflow for High-Throughput Quantum Cluster Model Generation from Protein Structures

[ChemRxiv] Generalization and Usability of Co-Folded GPCR–Ligand Complexes: A Physics-Guided Assessment

[Communications Physics] Interpolation-based coordinate descent method for parameterized quantum circuits

[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

[ChemRxiv] ConfDENSE: A conformer aware electron density based machine learning paradigm for navigating the odorant landscape

[AI for Science - latest papers] MOSES: combining automated ontology construction with a multi-agent system for explainable chemical knowledge reasoning

[Cell Reports Physical Science] A review of advancements and challenges in nanoplastics detection

[ScienceDirect Publication: Journal of Energy Storage] Optimizing solid electrolyte interphase with KOTF for dendrites-free and high-performance Lithium Metal Batteries

[ScienceDirect Publication: Journal of Energy Storage] A hierarchical sandwich Li6.4Ga0.2La3Zr2O12/ZIF-8@SiO2/PVDF-HFP heterostructure with high ionic conductivity for dendrite-free solid-state lithium batteries

[ScienceDirect Publication: Journal of Energy Storage] Hierarchical rose-like VS2 with sulfur vacancies for high-performance all-solid-state lithium-ion batteries

[ScienceDirect Publication: Journal of Energy Storage] Prediction of Lithium-ion battery states via combination of implantable sensors and machine learning

[ScienceDirect Publication: Journal of Energy Storage] A review on metal–organic framework-based polymer solid-state electrolytes for energy storage

[ScienceDirect Publication: Computational Materials Science] Descriptor and graph-based molecular representations in prediction of copolymer properties using machine learning

[Recent Articles in Phys. Rev. B] Signatures of coherent phonon transport in frequency-dependent lattice thermal conductivity

[Wiley: Advanced Energy Materials: Table of Contents] Accelerating the Discovery of High‐Conductivity Glass Electrolytes via Machine Learning

[cond-mat updates on arXiv.org] Physically-Constrained Autoencoder-Assisted Bayesian Optimization for Refinement of High-Dimensional Defect-Sensitive Single Crystalline Structure

[cond-mat updates on arXiv.org] A Chemically Grounded Evaluation Framework for Generative Models in Materials Discovery

[cond-mat updates on arXiv.org] Disordered Dynamics in High Dimensions: Connections to Random Matrices and Machine Learning

[cond-mat updates on arXiv.org] Predicting Coherent B2 Stability in Ru-Containing Refractory Alloys Through Thermodynamic Elastic Design Maps

[Cell Reports Physical Science] Hierarchical optimization of perovskite solar cell fabrication via step-by-step machine learning

[ACS Materials Letters: Latest Articles (ACS Publications)] [ASAP] Machine Learning Applications in Predicting Friction Properties of Bearing Steel: A Review

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Machine Learning-Guided Discovery of Sterically Protected High Triplet Exciplex Hosts for Ultra-Bright Green OLEDs

[Wiley: Small Methods: Table of Contents] Standardization and Machine Learning Prediction of Tafel Slope of Pt‐Based Nanocatalysts for High‐Performance HER Catalyst Development

[npj Computational Materials] Toward high entropy material discovery for energy applications using computational and machine learning methods

[APL Machine Learning Current Issue] AI agents for photonic integrated circuit design automation

[Applied Physics Letters Current Issue] Rattling-induced anharmonicity and multi-valley enhanced thermoelectric performance in layered SmZnSbO material

[Applied Physics Letters Current Issue] Magneto-ionic control of perpendicular anisotropy in epitaxial Mn 4 N films

[Applied Physics Letters Current Issue] Predicting anode coatings for solid-state lithium metal batteries via first-principles thermodynamic calculations and hierarchical ion-transport algorithms

[APL Materials Current Issue] Lithography-free fabrication of transparent, durable surfaces with embedded functional materials in glass nanoholes

[ChemRxiv] Augmenting Large Language Models for Automated Discovery of f-Element Extractants

[RSC - Chem. Sci. latest articles] Solid Dual-Salt Plastic Crystal Electrolyte Enabling Rapid Ion Transfer and Stable Interphases for High-Performance Solid-State Sodium Ion Batteries

[Wiley: Advanced Science: Table of Contents] Intelligent Sensing: The Emerging Integration of Machine Learning and Soft Sensors Based on Hydrogels and Ionogels

[Wiley: Advanced Science: Table of Contents] Pre‐Constructed Mechano‐Electrochemical Adaptive Solid Electrolyte Interphase to Enhance Li+ Diffusion Kinetics and Interface Stability for Chemically Prelithiated SiO Anodes

[Wiley: Small: Table of Contents] Unraveling A‐Site Cation Control of Hot Carrier Relaxation in Vacancy‐Ordered Halide Perovskites Through Quantum Dynamics and Interpretable Machine Learning

[Wiley: Advanced Materials: Table of Contents] Rational Design of Metal–Organic Frameworks for Pancreatic Cancer Therapy: from Machine Learning Screening to In Vivo Efficacy (Adv. Mater. 52/2025)

[Wiley: Advanced Materials: Table of Contents] Rational Design of Metal–Organic Frameworks for Pancreatic Cancer Therapy: from Machine Learning Screening to In Vivo Efficacy

[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Chloride-Based Solid Electrolytes from Crystal Structure to Electrochemical Performance

[Wiley: Small: Table of Contents] Enhancing Cycling Stability and Suppressing Lithium Dendrite Formation With A Hierarchical Artificial Solid Electrolyte Interphase Layer on Lithium Anodes for High‐Voltage Lithium Metal Batteries

[Wiley: Advanced Materials: Table of Contents] Gradient‐Heterojunction in Solid Electrolytes for Fast‐Charging Dendrite‐Free Solid‐State Lithium Metal Batteries

[APL Machine Learning Current Issue] Synthetic images from generative AI for compositional analysis of dried solution patterns

[iScience] An Interpretable Machine Learning Model for Predicting the Effectiveness of Low-Dose Methylprednisolone in Treating Long COVID: A Retrospective Study

[iScience] River plastic hotspot detection from space

[ScienceDirect Publication: Journal of Energy Storage] A hierarchical sandwich Li_6.4Ga_0.2La₃Zr₂O₁₂/ZIF-8@SiO₂/PVDF-HFP heterostructure with high ionic conductivity for dendrite-free solid-state lithium batteries

[ScienceDirect Publication: Journal of Energy Storage] Hierarchical rose-like VS₂ with sulfur vacancies for high-performance all-solid-state lithium-ion batteries

[ScienceDirect Publication: Journal of Energy Storage] A hierarchical sandwich Li_6.4Ga_0.2La₃Zr₂O₁₂/ZIF-8@SiO₂/PVDF-HFP heterostructure with high ionic conductivity for dendrite-free solid-state lithium batteries

[ScienceDirect Publication: Journal of Energy Storage] Hierarchical rose-like VS₂ with sulfur vacancies for high-performance all-solid-state lithium-ion batteries