My Customized Papers

[ScienceDirect Publication: Journal of Energy Storage] Polydopamine coating on garnet-type solid electrolyte for enhancing interfacial compatibility in solid-state lithium metal batteries

ScienceDirect Publication: Journal of Energy Storage — Thu, 08 Jan 2026 18:28:37 GMT

Publication date: 28 February 2026

Source: Journal of Energy Storage, Volume 148

Author(s): Lifeng Guan, Lian Wu, Xinyuan Li, Xuanshuo Zhang, Xiuqing Hao, Jinxiu Wen, Wei Zeng

[ScienceDirect Publication: Science Bulletin] Machine learning-based diagnosis of uterine myomas and sarcomas using tumor-educated platelet transcriptomics: a retrospective multicenter study

ScienceDirect Publication: Science Bulletin — Thu, 08 Jan 2026 18:28:36 GMT

Publication date: 15 January 2026

Source: Science Bulletin, Volume 71, Issue 1

Author(s): Xudong Liu, Roujie Huang, Hua Yang, Yu Dong, Lei Li, Zhe Li, Jia Zeng, Qingxia Zhang, Yun Liu, Lei Zhang, Yidi Ma, Lin Zhang, Weijie Tian, Yan You, Yaqian Li, Tianshu Sun, Xiaoyue Zhao, Wei Liu, Le Dang, Zhibo Zhang

[ScienceDirect Publication: Solid State Ionics] Enhanced ionic conductivity and dielectric performance of CaB₂O₄-doped 2-hydroxyethyl cellulose polymer electrolytes for electrical double layer capacitor applications

ScienceDirect Publication: Solid State Ionics — Thu, 08 Jan 2026 12:44:16 GMT

Publication date: 1 March 2026

Source: Solid State Ionics, Volume 436

Author(s): Ranaa M. Almarshedy, Siti Rohana Majid, Ninie Suhana Abdul Manan

[ScienceDirect Publication: Solid State Ionics] One – Step synthesis of glass ceramic Li₆PS₅Cl_1-xI_x solid electrolytes for all-solid-state batteries

ScienceDirect Publication: Solid State Ionics — Thu, 08 Jan 2026 12:44:16 GMT

Publication date: 1 March 2026

Source: Solid State Ionics, Volume 436

Author(s): Nurcemal Atmaca, Mahir Uenal, Hansen Chang, Oliver Clemens

[Wiley: Small Methods: Table of Contents] Interfacial Stability and Design Strategies for Halide Solid Electrolytes in High‐Voltage All‐Solid‐State Sodium‐Ion Batteries

Wiley: Small Methods: Table of Contents — Thu, 08 Jan 2026 06:35:51 GMT

Small Methods, EarlyView.

[cond-mat updates on arXiv.org] Physically Consistent Machine Learning for Melting Temperature Prediction of Refractory High-Entropy Alloys

RSC - Digital Discovery latest articles — Fri, 09 Jan 2026 00:00:00 GMT

Digital Discovery, 2025, Accepted Manuscript
DOI: 10.1039/D5DD00429B, Paper

Open Access

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

Xuefeng Bai, Zhiling Zheng, Xin Zhang, Hao-Tian Wang, Rui Yang, Jian-Rong Li
Large Language Models (LLMs) have potential in transforming chemical research. Nevertheless, their general-purpose design constrains scientific understanding and reasoning within specialized fields like chemistry. In this study, we introduce MOFReasoner,...
The content of this RSS Feed (c) The Royal Society of Chemistry

[ScienceDirect Publication: Journal of Energy Storage] Polydopamine coating on garnet-type solid electrolyte for enhancing interfacial compatibility in solid-state lithium metal batteries

ScienceDirect Publication: Journal of Energy Storage — Thu, 08 Jan 2026 18:28:37 GMT

Publication date: 28 February 2026

Source: Journal of Energy Storage, Volume 148

Author(s): Lifeng Guan, Lian Wu, Xinyuan Li, Xuanshuo Zhang, Xiuqing Hao, Jinxiu Wen, Wei Zeng

[ScienceDirect Publication: Science Bulletin] Machine learning-based diagnosis of uterine myomas and sarcomas using tumor-educated platelet transcriptomics: a retrospective multicenter study

ScienceDirect Publication: Science Bulletin — Thu, 08 Jan 2026 18:28:36 GMT

Publication date: 15 January 2026

Source: Science Bulletin, Volume 71, Issue 1

[ScienceDirect Publication: Solid State Ionics] Enhanced ionic conductivity and dielectric performance of CaB₂O₄-doped 2-hydroxyethyl cellulose polymer electrolytes for electrical double layer capacitor applications

ScienceDirect Publication: Solid State Ionics — Thu, 08 Jan 2026 12:44:16 GMT

Publication date: 1 March 2026

Source: Solid State Ionics, Volume 436

Author(s): Ranaa M. Almarshedy, Siti Rohana Majid, Ninie Suhana Abdul Manan

[ScienceDirect Publication: Solid State Ionics] One – Step synthesis of glass ceramic Li₆PS₅Cl_1-xI_x solid electrolytes for all-solid-state batteries

ScienceDirect Publication: Solid State Ionics — Thu, 08 Jan 2026 12:44:16 GMT

Publication date: 1 March 2026

Source: Solid State Ionics, Volume 436

Author(s): Nurcemal Atmaca, Mahir Uenal, Hansen Chang, Oliver Clemens

[Recent Articles in Phys. Rev. Lett.] Enhancing Gravitational-Wave Detection: A Machine Learning Pipeline Combination Approach with Robust Uncertainty Quantification

Recent Articles in Phys. Rev. Lett. — Thu, 08 Jan 2026 10:00:00 GMT

Author(s): Gregory Ashton, Ann-Kristin Malz, and Nicolo Colombo

Gravitational-wave data from advanced-era interferometric detectors consists of background Gaussian noise, frequent transient artifacts, and rare astrophysical signals. Multiple search algorithms exist to detect the signals from compact binary coalescences, but their varying performance complicates …

[Phys. Rev. Lett. 136, 011402] Published Thu Jan 08, 2026

[Recent Articles in Phys. Rev. B] Universal band center model for the HER activity of nonmetal sites in transition metal dichalcogenides

Recent Articles in Phys. Rev. B — Thu, 08 Jan 2026 10:00:00 GMT

Author(s): Ruixin Xu, Shiqian Cao, Tingting Bo, Yanyu Liu, and Wei Zhou

In this work, the hydrogenation performances of nonmetal sites in the transition metal dichalcogenides with the stoichiometry of $M{\mathit{X}}_{2}$ are systematically investigated using the first principles calculations. The trained machine learning model demonstrates that the ${p}_{\mathrm{z}}$ ba…

[Phys. Rev. B 113, 035305] Published Thu Jan 08, 2026

[Wiley: Small Methods: Table of Contents] Interfacial Stability and Design Strategies for Halide Solid Electrolytes in High‐Voltage All‐Solid‐State Sodium‐Ion Batteries

Wiley: Small Methods: Table of Contents — Thu, 08 Jan 2026 06:35:51 GMT

Small Methods, EarlyView.

[cond-mat updates on arXiv.org] Physically Consistent Machine Learning for Melting Temperature Prediction of Refractory High-Entropy Alloys

cond-mat updates on arXiv.org — Thu, 08 Jan 2026 05:00:00 GMT

arXiv:2601.03801v1 Announce Type: new Abstract: Predicting the melting temperature (Tm) of multi-component and high-entropy alloys (HEAs) is critical for high-temperature applications but computationally expensive using traditional CALPHAD or DFT methods. In this work, we develop a gradient-boosted decision tree (XGBoost) model to predict Tm for complex alloys based on elemental properties. To ensure physical consistency, we address the issue of data leakage by excluding temperature-dependent thermodynamic descriptors (such as Gibbs free energy of mixing) and instead rely on physically motivated elemental features. The optimized model achieves a coefficient of determination (R2) of 0.948 and a Mean Squared Error (MSE) of 9928 which is about 5% relative error for HEAs on a validation set of approximately 1300 compositions. Crucially, we validate the model using the Valence Electron Concentration (VEC) rule. Without explicit constraints during training, the model successfully captures the known stability transition between BCC and FCC phases at a VEC of approximately 6.87. These results demonstrate that data-driven models, when properly feature-engineered, can capture fundamental metallurgical principles for rapid alloy screening.

[cond-mat updates on arXiv.org] Material exploration through active learning -- METAL

cond-mat updates on arXiv.org — Thu, 08 Jan 2026 05:00:00 GMT

arXiv:2601.03933v1 Announce Type: new Abstract: The discovery and design of new materials are paramount in the development of green technologies. High entropy oxides represent one such group that has only been tentatively explored, mainly due to the inherent problem of navigating vast compositional spaces. Thanks to the emergence of machine learning, however, suitable tools are now readily available. Here, the task of finding oxygen carriers for chemical looping processes has been tackled by leveraging active learning-based strategies combined with first-principles calculations. High efficiency and efficacy have, moreover, been achieved by exploiting the power of recently developed machine learning interatomic potentials. Firstly, the proposed approaches were validated based on an established computational framework for identifying high entropy perovskites that can be used in chemical looping air separation and dry reforming. Chief among the insights thus gained was the identification of the best performing strategies, in the form of greedy or Thompson-based sampling based on uncertainty estimates obtained from Gaussian processes. Building on this newfound knowledge, the concept was applied to a more complex problem, namely the discovery of high entropy oxygen carriers for chemical looping oxygen uncoupling. This resulted in both qualitative as well as quantitative outcomes, including lists of specific materials with high oxygen transfer capacities and configurational entropies. Specifically, the best candidates were based on the known oxygen carrier CaMnO3 but also contained a variety of additional species, of which some, e.g., Ti; Co; Cu; and Ti, were expected while others were not, e.g., Y and Sm. The results suggest that adopting active learning approaches is critical in materials discovery, given that these methods are already shifting research practice and soon will be the norm.

[cond-mat updates on arXiv.org] Transport properties in a model of confined granular mixtures at moderate densities

cond-mat updates on arXiv.org — Thu, 08 Jan 2026 05:00:00 GMT

arXiv:2601.04026v1 Announce Type: new Abstract: This work derives the Navier--Stokes hydrodynamic equations for a model of a confined, quasi-two-dimensional, $s$-component mixture of inelastic, smooth, hard spheres. Using the inelastic version of the revised Enskog theory, macroscopic balance equations for mass, momentum, and energy are obtained, and constitutive equations for the fluxes are determined through a first-order Chapman--Enskog expansion. As for elastic collisions, the transport coefficients are given in terms of the solutions of a set of coupled linear integral equations. Approximate solutions to these equations for diffusion transport coefficients and shear viscosity are achieved by assuming steady-state conditions and considering leading terms in a Sonine polynomial expansion. These transport coefficients are expressed in terms of the coefficients of restitution, concentration, the masses and diameters of the mixture's components, and the system's density. The results apply to moderate densities and are not limited to particular values of the coefficients of restitution, concentration, mass, and/or diameter ratios. As an application, the thermal diffusion factor is evaluated to analyze segregation driven by temperature gradients and gravity, providing criteria that distinguish whether larger particles accumulate near the hotter or colder boundaries.

[cond-mat updates on arXiv.org] libMobility: A Python library for hydrodynamics at the Smoluchowski level

cond-mat updates on arXiv.org — Thu, 08 Jan 2026 05:00:00 GMT

arXiv:2510.02135v2 Announce Type: replace Abstract: Effective hydrodynamic modeling is crucial for accurately predicting fluid-particle interactions in diverse fields such as biophysics and materials science. Developing and implementing hydrodynamic algorithms is challenging due to the complexity of fluid dynamics, necessitating efficient management of large-scale computations and sophisticated boundary conditions. Furthermore, adapting these algorithms for use on massively parallel architectures like GPUs adds an additional layer of complexity. This paper presents the libMobility software library, which offers a suite of CUDA-enabled solvers for simulating hydrodynamic interactions in particulate systems at the Rotne-Prager-Yamakawa (RPY) level. The library facilitates precise simulations of particle displacements influenced by external forces and torques, including both the deterministic and stochastic components. Notable features of libMobility include its ability to handle linear and angular displacements, thermal fluctuations, and various domain geometries effectively. With an interface in Python, libMobility provides comprehensive tools for researchers in computational fluid dynamics and related fields to simulate particle mobility efficiently. This article details the technical architecture, functionality, and wide-ranging applications of libMobility. libMobility is available at https://github.com/stochasticHydroTools/libMobility.

[cond-mat updates on arXiv.org] Kinetic Flux Equations for Ion Exchange in Silicate Glasses

cond-mat updates on arXiv.org — Thu, 08 Jan 2026 05:00:00 GMT

arXiv:2601.03207v2 Announce Type: replace Abstract: Ion exchange kinetic flux equations have been extensively investigated since the mid-twentieth century and continue to provide a fundamental framework for describing mass transport phenomena in solid materials. Despite the maturity of this field, inconsistencies remain in the literature concerning the definition, dimensional consistency, and physical interpretation of the parameters involved. A rigorous and unified treatment of these equations is therefore essential to ensure the reproducibility and comparability of theoretical and experimental studies. The present study aims to establish a coherent and systematic development of ion exchange kinetic flux equations, with particular emphasis on the consistent definition and dimensional formulation of the relevant physical quantities. Beyond refining the theoretical foundations, this study extends the classical formulation by incorporating the influence of mechanical stress on ion transport and considering cross-term interactions within the framework of linear irreversible thermodynamics. These developments provide a more comprehensive description of ion exchange kinetics, particularly as applied to silicate glasses, where coupling between chemical and mechanical effects plays a crucial role in determining transport behavior and performance.

[cond-mat updates on arXiv.org] Agentic Exploration of Physics Models

cond-mat updates on arXiv.org — Thu, 08 Jan 2026 05:00:00 GMT

arXiv:2509.24978v4 Announce Type: replace-cross Abstract: The process of scientific discovery relies on an interplay of observations, analysis, and hypothesis generation. Machine learning is increasingly being adopted to address individual aspects of this process. However, it remains an open challenge to fully automate the heuristic, iterative loop required to discover the laws of an unknown system by exploring it through experiments and analysis, without tailoring the approach to the specifics of a given task. Here, we introduce SciExplorer, an agent that leverages large language model tool-use capabilities to enable exploration of systems without any domain-specific blueprints, and apply it to physical systems that are initially unknown to the agent. We test SciExplorer on a broad set of models spanning mechanical dynamical systems, wave evolution, and quantum many-body physics. Despite using a minimal set of tools, primarily based on code execution, we observe impressive performance on tasks such as recovering equations of motion from observed dynamics and inferring Hamiltonians from expectation values. The demonstrated effectiveness of this setup opens the door towards similar scientific exploration in other domains, without the need for finetuning or task-specific instructions.

[cond-mat updates on arXiv.org] Masgent: An AI-assisted Materials Simulation Agent

cond-mat updates on arXiv.org — Thu, 08 Jan 2026 05:00:00 GMT

arXiv:2512.23010v2 Announce Type: replace-cross -Abstract: Density functional theory (DFT) and machine learning potentials (MLPs) are essential for predicting and understanding materials properties, yet preparing, executing, and analyzing these simulations typically requires extensive scripting, multi-step procedures, and significant high-performance computing (HPC) expertise. These challenges hinder reproducibility and slow down discovery. Here, we introduce Masgent, an AI-assisted materials simulation agent that unifies structure manipulation, automated VASP input generation, DFT workflow construction and analysis, fast MLP-based simulations, and lightweight machine learning (ML) utilities within a single platform. Powered by large language models (LLMs), Masgent enables researchers to perform complex simulation tasks through natural-language interaction, eliminating most manual scripting and reducing setup time from hours to seconds. By standardizing protocols and integrating advanced simulation and data-driven tools, Masgent lowers the barrier to performing state-of-the-art computational methodologies, enabling faster hypothesis testing, pre-screening, and exploratory research for both new and experienced practitioners.

[ChemRxiv] The unique example of approximation of the electronic term of diatomic molecules by Morse potential. HF, DF, TF.

ChemRxiv — Thu, 08 Jan 2026 00:00:00 GMT

The Morse approximations M1(r) and M2(r) of the 1Σ+ ground state potential curves of three hydrofluoride isotopologues are analyzed. Qualitative differences between HF and heavy isotopologues were found. The result of the HF approximation is a function mainly described by the characteristics of a simple term. For HF, the anharmonicity M1(r) is lower than for M2(r), ωехе<ωехе' and De>D', therefore the curve M1(r) lies above U(r). The M2(r) model is constructed from the values of ωе and De, using the equation De=ωе2/4ωехе, so that its potential curve lies below U(r). For DF and TF, the anharmonicity of M1(r) is greater than of M2(r), ωехе>ωехе' and De

[ChemRxiv] Machine-Learning-Accelerated Simulations of Vibrational Activation for Controlled Photoisomerization in a Molecular Motor

ChemRxiv — Thu, 08 Jan 2026 00:00:00 GMT

The precise manipulation of photochemical reactions across broad configurational spaces requires sophisticated design of external control fields. Using the photoisomerization of a molecular motor as a prototype, this study integrates enhanced sampling and active learning to construct accurate machine-learned multi-state potential energy surfaces (PESs). By combining active-learning trajectories with enhanced sampling, our approach efficiently covers substantial reaction regions, enabling trajectory propagation extending to tens of picoseconds at a low computational cost within the machine learning framework. Furthermore, local control theory (LCT) is employed to selectively activate specific vibrational motions, leading to accelerated access to reactive regions, enhanced nonadiabatic transitions, and significantly improved selectivity toward the dominant photoproduct. This combined strategy of machine-learning potentials and LCT offers an efficient and generalizable framework for controlling excited-state dynamics in complex systems.

[RSC - Chem. Sci. latest articles] Dynamic Protein Structures in Solution: Decoding the Amide I Band with 2D-IR Spectral Libraries and Machine Learning

RSC - Chem. Sci. latest articles — Thu, 08 Jan 2026 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC09973K, Edge Article

Open Access

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

Amy Farmer, Kelly Brown, Sophie E.T. Kendall-Price, Partha Malakar, Gregory M Greetham, Neil Hunt
The dynamic three-dimensional structures of proteins dictate their function, but accessing structures in solution at physiological temperatures is challenging. Ultrafast 2D-IR spectroscopy of the protein amide I band produces a...
The content of this RSS Feed (c) The Royal Society of Chemistry

[ChemRxiv] Atomistic insights into the chemical stability and ionic transport at Li-metal/Li-Argyrodite interfaces

ChemRxiv — Thu, 08 Jan 2026 00:00:00 GMT

All solid-state batteries (ASSBs) based on solid-state electrolytes (SSEs) are a novel Li-ion battery technology with the potential of enhanced safety, longer lifetimes, and increased energy density when coupled with the Li-metal anode. Li-Argyrodite (Li6PS5Cl) is a promising SSE with high ionic conductivity, produced using cheap and sustainable precursors, and therefore of interest to both academia and industry. Like many other sulfide-based SSEs, it is however unstable against Li-metal. Using ab-initio and machine-learning methods, we simulate three representative Li-metal/Li-Argyrodite interface models to investigate whether the exact surface termination affects the chemical stability and ion transport capability. We present a systematic approach to create low-energy interfaces by screening 28 low Miller-index surface terminations of Li-argyrodite and coupling them with Li-metal. Custom-made machine-learned interatomic potentials trained on ab-initio data enable the simulation of large interface models with over 2000 atoms for 5 ns. We find that all three interfaces decompose into an amorphous solid-electrolyte interphase (SEI) layer, consisting of Li3P, Li2S and LiCl, which then crystallizes into an antifluorite phase Li2S{1-x-y}P{x}Cl{y}; {x,y = 0.14-0.15}. A two orders of magnitude decrease in Li-ion flux shows that the crystalline SEI layer is a sluggish ion conductor, similar to Li2S. While all three interfaces form the same crystalline SEI layer, the exact rates of the decomposition and crystallisation depend on the actual surface composition. These atomic-level insights could potentially be used to control the SEI formation in sulphide-based SSEs and others.

[ScienceDirect Publication: Journal of Energy Storage] Optimization of porous electrode configuration for organic redox flow battery by machine learning based on back propagation neural network based on firefly

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

Publication date: 28 February 2026

Source: Journal of Energy Storage, Volume 148

Author(s): Fengming Chu, Yongzhuo Wang, Xi Liu, Tong Liu

[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: Advanced Functional Materials: Table of Contents] Dynamic Li‐S Coordination Boosted Superionic Conduction in Cubic LiBS2 Solid Electrolyte

Wiley: Advanced Functional Materials: Table of Contents — Wed, 07 Jan 2026 15:35:52 GMT

Advanced Functional Materials, EarlyView.

[Wiley: Advanced Science: Table of Contents] Sulfonated Cellulose Acetate Nanofibers Induced Zincophilic‐Hydrophobic Interface to Regulate Ion Transport for Long‐Lifespan Zinc‐Iodine Batteries

Wiley: Advanced Science: Table of Contents — Wed, 07 Jan 2026 15:22:47 GMT

Advanced Science, EarlyView.

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Machine Learning Prediction of Two-Dimensional Polymerization of Nitrogen in FeNx

The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications) — Wed, 07 Jan 2026 15:15:41 GMT

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03557

[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 — Thu, 08 Jan 2026 05:00:00 GMT

arXiv:2601.02424v1 Announce Type: new +Abstract: Density functional theory (DFT) and machine learning potentials (MLPs) are essential for predicting and understanding materials properties, yet preparing, executing, and analyzing these simulations typically requires extensive scripting, multi-step procedures, and significant high-performance computing (HPC) expertise. These challenges hinder reproducibility and slow down discovery. Here, we introduce Masgent, an AI-assisted materials simulation agent that unifies structure manipulation, automated VASP input generation, DFT workflow construction and analysis, fast MLP-based simulations, and lightweight machine learning (ML) utilities within a single platform. Powered by large language models (LLMs), Masgent enables researchers to perform complex simulation tasks through natural-language interaction, eliminating most manual scripting and reducing setup time from hours to seconds. By standardizing protocols and integrating advanced simulation and data-driven tools, Masgent lowers the barrier to performing state-of-the-art computational methodologies, enabling faster hypothesis testing, pre-screening, and exploratory research for both new and experienced practitioners.

[ChemRxiv] The unique example of approximation of the electronic term of diatomic molecules by Morse potential. HF, DF, TF.

ChemRxiv — Thu, 08 Jan 2026 00:00:00 GMT

[ChemRxiv] Machine-Learning-Accelerated Simulations of Vibrational Activation for Controlled Photoisomerization in a Molecular Motor

ChemRxiv — Thu, 08 Jan 2026 00:00:00 GMT

[RSC - Chem. Sci. latest articles] Dynamic Protein Structures in Solution: Decoding the Amide I Band with 2D-IR Spectral Libraries and Machine Learning

RSC - Chem. Sci. latest articles — Thu, 08 Jan 2026 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC09973K, Edge Article

Open Access

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

[ChemRxiv] Atomistic insights into the chemical stability and ionic transport at Li-metal/Li-Argyrodite interfaces

ChemRxiv — Thu, 08 Jan 2026 00:00:00 GMT

[Communications Materials] Extracting and reconstructing knowledge in materials science literature using large language models

Communications Materials — Thu, 08 Jan 2026 00:00:00 GMT

Communications Materials, Published online: 08 January 2026; doi:10.1038/s43246-025-01043-3

Reconstructing knowledge on synthesis routes and properties from inorganic science literature is crucial yet challenging, particularly in maintaining completeness and logical consistency. Here, the authors develop a generalized method based on GPT-4 to fine-tune LLMs, achieving high precision in material synthesis extraction and demonstrating broad applicability across domains, ultimately constructing a comprehensive knowledge graph for materials science optimization.

[ScienceDirect Publication: Journal of Energy Storage] Optimization of porous electrode configuration for organic redox flow battery by machine learning based on back propagation neural network based on firefly

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

Publication date: 28 February 2026

Source: Journal of Energy Storage, Volume 148

Author(s): Fengming Chu, Yongzhuo Wang, Xi Liu, Tong Liu

[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: Advanced Functional Materials: Table of Contents] Dynamic Li‐S Coordination Boosted Superionic Conduction in Cubic LiBS2 Solid Electrolyte

Wiley: Advanced Functional Materials: Table of Contents — Wed, 07 Jan 2026 15:35:52 GMT

Advanced Functional Materials, EarlyView.

[Wiley: Advanced Science: Table of Contents] Sulfonated Cellulose Acetate Nanofibers Induced Zincophilic‐Hydrophobic Interface to Regulate Ion Transport for Long‐Lifespan Zinc‐Iodine Batteries

Wiley: Advanced Science: Table of Contents — Wed, 07 Jan 2026 15:22:47 GMT

Advanced Science, EarlyView.

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Machine Learning Prediction of Two-Dimensional Polymerization of Nitrogen in FeNx

The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications) — Wed, 07 Jan 2026 15:15:41 GMT

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03557

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Single-Round Aptamer Discovery Empowered by Machine Learning: Revealing Structure–Function Principles of Target Binding

Chinese Chemical Society: CCS Chemistry: Table of Contents — Wed, 07 Jan 2026 11:28:21 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202506736

High-throughput sequencing has revolutionized aptamer discovery; however, the process is still limited by the lack of effective methods to extract structural insights from diverse sequences, crucial for aptamer truncation, optimization, and molecular ...

[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 — Tue, 06 Jan 2026 05:00:00 GMT

arXiv:2601.02938v1 Announce Type: new @@ -40,7 +40,7 @@ Abstract: Accurate prediction of radiation damage in YBa$_2$Cu$3$O${7-\delta}$ ( Abstract: We present quantum heat machines using a diatomic molecule modelled by a $q$-deformed potential as a working medium. We analyze the effect of the deformation parameter and other potential parameters on the work output and efficiency of the quantum Otto and quantum Carnot heat cycles. Furthermore, we derive the analytical expressions of work and efficiency as a function of these parameters. Interestingly, our system operates as a quantum heat engine across the range of parameters considered. In addition, the efficiency of the quantum Otto heat engine is seen to be tunable by the deformation parameter. Our findings provide useful insight for understanding the impact of anharmonicity on the design of quantum thermal machines.

[ChemRxiv] A Systematic Review of Prompt Engineering Paradigms in Organic Chemistry: Mining, Prediction, and Model Architectures

ChemRxiv — Tue, 06 Jan 2026 00:00:00 GMT

Large language models (LLMs) have emerged as transformative tools in scientific research, offering a powerful alternative to traditional, resource-intensive machine learning methods. By leveraging the vast knowledge encoded during pre-training, prompt engineering—the systematic design and optimization of input instructions—enables researchers to guide LLMs toward accurate and domain-specific outputs without updating model parameters. This review presents the first systematic examination of prompt engineering techniques within organic chemistry, focusing on two critical application areas: text mining and predictive tasks. We analyze the core paradigms of prompt engineering, including prompt design, prompt learning, and prompt tuning, and clarify terminological inconsistencies in the literature. The discussion is contextualized within the three principal LLM architectures (encoder-only, decoder-only, and encoder-decoder), with an evaluation of their respective performances on chemistry-related tasks. Furthermore, we explore practical workflows for extracting structured chemical data from texts and knowledge graphs, as well as advanced prompt strategies for reaction condition prediction, reaction optimization, and catalytic performance prediction. This review highlights the significant potential of LLM-driven prompt engineering to accelerate discovery in organic chemistry, from synthetic pathway optimization to automated literature analysis, while also addressing persistent challenges such as the limitations associated with various prompt engineering techniques and the constraints related to each related sub-task. We conclude by outlining future research directions aimed at deepening the integration of chemical knowledge with evolving AI methodologies.

[ChemRxiv] Ensemble Analyzer: An Open-Source Python Framework for Automated Conformer Ensemble Refinement

ChemRxiv — Tue, 06 Jan 2026 00:00:00 GMT

Accurate prediction of molecular properties often requires considering the full conformational ensemble rather than a single optimized structure. While modern sampling tools have revolutionized conformational sampling by enabling the rapid generation of ensembles, the subsequent refinement at higher levels of theory remains computationally demanding and technically complex. Existing workflows typically rely on ad hoc scripts and manual intervention, limiting reproducibility and accessibility. Here, we present Ensemble Analyzer (EnAn), an open-source Python framework designed to automate the refinement and analysis of conformational ensembles. Built on the Atomic Simulation Environment (ASE), EnAn integrates seamlessly with widely known quantum chemistry engines such as ORCA and Gaussian, providing a modular and extensible architecture that streamlines the entire pipeline. EnAn also supports automated generation and comparison of electronic and vibronic spectra, enabling rapid visualization and interpretation. By minimizing manual data handling and standardizing workflows, EnAn effectively manages reproducible exploration of complex conformational spaces.

[ChemRxiv] Electrostatic Patterning Controls Mineral Nucleation Inside Ferritin

ChemRxiv — Tue, 06 Jan 2026 00:00:00 GMT

Ferritin protein nanocages store iron across nearly all living organisms. In mammals, two subtypes of ferritin exist: heavy (H) chain and light (L) chain. They have very similar 3D structures, but each performs a slightly different role in iron mineral formation. How sequence differences between the two subtypes affect mineral formation within the nanocages is still unclear. Single-particle reconstruction of cryo-TEM images was used to build models of unmineralized and partially mineralized human L-chain and H-chain ferritin, which showed that subtle differences in protein structure led to changes in the location of mineral formation within ferritin. Explicit-solvent atomistic molecular dynamics (MD) simulations were used to explore how sequence-dependent electrostatics modulate ion transport, cluster formation, and mineral nucleation within the confined environment of human L- and H-chain apoferritin nanocages. Employing NaCl as a computational probe, we show that the internal charge distribution governs ion selectivity and nucleation pathways. Analysis of liquid and solid ionic clusters, combined with Markov State Models (MSMs), reveals that mineralization proceeds through a two-step mechanism involving dense liquid-like precursors that crystallize homogeneously within the cavity. These findings provide molecular insight into how ferritin sequence variability tunes confinement-driven nucleation and suggest general principles for designing biomimetic nanoreactors.

[ChemRxiv] Machine Learning Prediction of Henry Coefficients of Polar and Nonpolar Gases in Covalent Organic Frameworks: Effects of Interlayer Shifts and Functionalization

ChemRxiv — Tue, 06 Jan 2026 00:00:00 GMT

Covalent organic frameworks (COFs) are promising materials for gas separation and carbon capture. Computational techniques based on Monte Carlo simulation can be used to predict the gas adsorption properties of COFs with high accuracy, however they are too inefficient to be deployed in a high-throughput manner for screening large COF databases. In this paper, we systematically train and evaluate a range of machine learning models for predicting the Henry coefficients for CO2 and CH4 gas adsorption in COF materials. To account for COF structural variability, we train our models on datasets that include both chemically functionalized frameworks and interlayer displaced stacking configurations. By comparing predictive performance across descriptor–model architecture combinations, we demonstrate how different models capture the key physical factors governing gas adsorption, including electrostatics, local atomic environments, and van der Waals interactions. Our results therefore provide a framework for building machine learning models for scalable, high-throughput screening of COF materials with targeted gas adsorption properties.

[ChemRxiv] Bridging the Gap: Aqueous Phase Organic Synthesis as a Foundation for Advanced Chemical and Biological Discovery

ChemRxiv — Tue, 06 Jan 2026 00:00:00 GMT

For over a century, synthetic chemistry demanded the rigorous exclusion of water, relying on toxic, volatile organic solvents. This paradigm, while successful, is environmentally and economically costly. This review advocates for a fundamental shift: adopting water not just as a green solvent, but as a transformative medium that reshapes our understanding of reactivity and bridges chemistry with biology. Aqueous phase organic synthesis (APOS) has evolved from accidental observations to a deliberate discipline. Water’s unique properties—its high polarity, hydrogen-bonding capacity, and the hydrophobic effect—make it an active participant. This effect drives reactant aggregation and stabilizes transition states, leading to dramatic rate enhancements in pericyclic and condensation reactions. A broad range of reactions thrive in water, including classical carbon–carbon bond-forming reactions like the aldol and Diels–Alder, and modern cross-couplings (e.g., Suzuki–Miyaura) enabled by water-tolerant catalysts. Multicomponent and click chemistries are particularly powerful. Challenges like poor solubility are addressed with micellar catalysis, water-soluble ligands, and precise control of the reaction microenvironment. Beyond sustainability, APOS drives discovery, often yielding improved selectivities, new pathways, and streamlined syntheses of complex targets like pharmaceuticals. Its greatest promise lies in interfacing with biology. Bioorthogonal reactions, such as azide–alkyne cycloadditions, enable labeling and imaging in living organisms. Aqueous compatibility is essential for in situ therapeutic strategies, chemical biology, and advanced bioconjugation techniques for modifying biomolecules. The future converges with emerging technologies: machine learning to navigate complex aqueous systems, flow chemistry for scalability, and the integration of enzymatic with synthetic catalysis. This points toward a unified chemical-biological engineering paradigm. In conclusion, APOS is a mature, versatile field. It is a cornerstone of green chemistry and a critical bridge to biology, accelerating progress in medicine and materials science. Embracing water is both an environmental imperative and a strategic pathway to the next generation of scientific discovery. Introduction: The Solvent Problem in Organic Synthesis -For generations, organic synthesis has been defined by precise control over molecular structure carried out in rigorously dried environments. Since the emergence of modern organic chemistry in the nineteenth century, water – the very medium that sustains life – has been regarded as an obstacle to chemical transformation. This long-standing assumption has shaped laboratory routines, industrial manufacturing, and chemical education, reinforcing the idea that successful synthesis depends on strict exclusion of moisture. This introduction revisits that historical mindset, evaluates its environmental and economic consequences, and presents the case for a fundamental transition toward aqueous phase organic synthesis (APOS) as a more suitable platform for future chemical and biological innovation.

[RSC - Chem. Sci. latest articles] Reactive Fluorescent Probe for Covalent Membrane-Anchoring: Enabling Real-time Imaging of Protein Aggregation Dynamics in Live Cells

RSC - Chem. Sci. latest articles — Tue, 06 Jan 2026 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC07716H, Edge Article

Open Access

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

Hongbei Wei, Liren Xu, Ke Wei, Wenhai Bian, Yifan Wen, Wanyi Yu, Hui Zhang, Tony D. James, Xiaolong Sun
Aberrant aggregation of membrane proteins is a pathological hallmark of various diseases, including neurodegenerative disorders and cancer. The visualization of membrane protein aggregation has emerged as an important approach for...
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[npj Computational Materials] Composition-dependent dislocation mobility in FeNiCrCoCu high-entropy alloys based on atomistic simulations and machine learning analysis

npj Computational Materials — Tue, 06 Jan 2026 00:00:00 GMT

npj Computational Materials, Published online: 06 January 2026; doi:10.1038/s41524-025-01942-6

Composition-dependent dislocation mobility in FeNiCrCoCu high-entropy alloys based on atomistic simulations and machine learning analysis

[npj Computational Materials] AI-assisted rapid crystal structure generation towards a target local environment

npj Computational Materials — Tue, 06 Jan 2026 00:00:00 GMT

npj Computational Materials, Published online: 06 January 2026; doi:10.1038/s41524-025-01931-9

AI-assisted rapid crystal structure generation towards a target local environment

[Applied Physics Letters Current Issue] Polarization-controlled multistate thermal conductivity in ferroelectric HfO 2 thin films

Applied Physics Letters Current Issue — Tue, 06 Jan 2026 00:00:00 GMT

While nanoscale electronic logic circuits are well established, the development of nanoscale thermal logic circuits has been slow, mainly due to the absence of efficient and controllable nonvolatile field-effect thermal transistors. In this study, we investigate polarization-dependent thermal conductivity in ferroelectric orthorhombic hafnium dioxide (o-HfO₂) thin films. Using molecular dynamics simulations with machine learning potentials, we show that a 24-nm-long o-HfO₂ film can exhibit four distinct and stable thermal conductivity states arising from different ferroelectric polarization configurations. Notably, these states achieve a maximum switching ratio of 160.8% under 2% tensile strain. Our results suggest a practical pathway toward nonvolatile field-effect thermal transistors.

[ScienceDirect Publication: Journal of Catalysis] Microkinetic modeling of methane activation in Mo/ZSM-5 with machine learning potentials

ScienceDirect Publication: Journal of Catalysis — Mon, 05 Jan 2026 18:32:11 GMT

Publication date: February 2026

Source: Journal of Catalysis, Volume 454

Author(s): Yanqi Huang, Xiang Ryan Zhou, Brandon C. Bukowski

[Wiley: Advanced Functional Materials: Table of Contents] Thermoplastic Elastomeric Bottlebrush Copolymer Glue Electrolyte Featuring Dual‐Ion Transport Channels and Strong Segregation: Experimental and Simulation Insights (Adv. Funct. Mater. 2/2026)

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Regulating Metallic Deposition Behavior by Gradient Alloy/Solid Electrolyte Interphase for Durable Na/K Anodes (Adv. Funct. Mater. 2/2026)

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Autonomous Liquid Metal Droplets Actuated by Ion Diffusion

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Microcrack‐Structured Visualizable Hydrogel Sensor for Machine Learning‐Assisted Handwriting Recognition

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Regulating Metallic Deposition Behavior by Gradient Alloy/Solid Electrolyte Interphase for Durable Na/K Anodes

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Thermoplastic Elastomeric Bottlebrush Copolymer Glue Electrolyte Featuring Dual‐Ion Transport Channels and Strong Segregation: Experimental and Simulation Insights

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Science: Table of Contents] CGRP Enhances the Regeneration of Bone Defects by Regulating Bone Marrow Mesenchymal Stem Cells Through Promoting ANGPTL4 Secretion by Bone Blood Vessels

Wiley: Advanced Science: Table of Contents — Mon, 05 Jan 2026 09:55:39 GMT

Advanced Science, EarlyView.

[Wiley: Carbon Energy: Table of Contents] Strategies to Enhance Ionic Conductivity of Na3Zr2Si2O12 Solid Electrolyte for Advanced Solid‐State Sodium Batteries

Wiley: Carbon Energy: Table of Contents — Mon, 05 Jan 2026 07:00:12 GMT

Carbon Energy, EarlyView.

[Wiley: Advanced Science: Table of Contents] Diffusion‐MRI‐Based Estimation of Cortical Architecture via Machine Learning (DECAM) in Primate Brains

Wiley: Advanced Science: Table of Contents — Mon, 05 Jan 2026 05:33:28 GMT

Advanced Science, EarlyView.

[cond-mat updates on arXiv.org] Automated electrostatic characterization of quantum dot devices in single- and bilayer heterostructures

ChemRxiv — Tue, 06 Jan 2026 00:00:00 GMT

arXiv:2601.00067v1 Announce Type: new +For generations, organic synthesis has been defined by precise control over molecular structure carried out in rigorously dried environments. Since the emergence of modern organic chemistry in the nineteenth century, water – the very medium that sustains life – has been regarded as an obstacle to chemical transformation. This long-standing assumption has shaped laboratory routines, industrial manufacturing, and chemical education, reinforcing the idea that successful synthesis depends on strict exclusion of moisture. This introduction revisits that historical mindset, evaluates its environmental and economic consequences, and presents the case for a fundamental transition toward aqueous phase organic synthesis (APOS) as a more suitable platform for future chemical and biological innovation.

[RSC - Chem. Sci. latest articles] Reactive Fluorescent Probe for Covalent Membrane-Anchoring: Enabling Real-time Imaging of Protein Aggregation Dynamics in Live Cells

RSC - Chem. Sci. latest articles — Tue, 06 Jan 2026 00:00:00 GMT

Chem. Sci., 2026, Accepted Manuscript
DOI: 10.1039/D5SC07716H, Edge Article

Open Access

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

[npj Computational Materials] Composition-dependent dislocation mobility in FeNiCrCoCu high-entropy alloys based on atomistic simulations and machine learning analysis

npj Computational Materials — Tue, 06 Jan 2026 00:00:00 GMT

npj Computational Materials, Published online: 06 January 2026; doi:10.1038/s41524-025-01942-6

Composition-dependent dislocation mobility in FeNiCrCoCu high-entropy alloys based on atomistic simulations and machine learning analysis

[npj Computational Materials] AI-assisted rapid crystal structure generation towards a target local environment

npj Computational Materials — Tue, 06 Jan 2026 00:00:00 GMT

npj Computational Materials, Published online: 06 January 2026; doi:10.1038/s41524-025-01931-9

AI-assisted rapid crystal structure generation towards a target local environment

[Applied Physics Letters Current Issue] Polarization-controlled multistate thermal conductivity in ferroelectric HfO 2 thin films

Applied Physics Letters Current Issue — Tue, 06 Jan 2026 00:00:00 GMT

[ScienceDirect Publication: Journal of Catalysis] Microkinetic modeling of methane activation in Mo/ZSM-5 with machine learning potentials

ScienceDirect Publication: Journal of Catalysis — Mon, 05 Jan 2026 18:32:11 GMT

Publication date: February 2026

Source: Journal of Catalysis, Volume 454

Author(s): Yanqi Huang, Xiang Ryan Zhou, Brandon C. Bukowski

[Wiley: Advanced Functional Materials: Table of Contents] Thermoplastic Elastomeric Bottlebrush Copolymer Glue Electrolyte Featuring Dual‐Ion Transport Channels and Strong Segregation: Experimental and Simulation Insights (Adv. Funct. Mater. 2/2026)

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Regulating Metallic Deposition Behavior by Gradient Alloy/Solid Electrolyte Interphase for Durable Na/K Anodes (Adv. Funct. Mater. 2/2026)

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Autonomous Liquid Metal Droplets Actuated by Ion Diffusion

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Microcrack‐Structured Visualizable Hydrogel Sensor for Machine Learning‐Assisted Handwriting Recognition

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Regulating Metallic Deposition Behavior by Gradient Alloy/Solid Electrolyte Interphase for Durable Na/K Anodes

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Functional Materials: Table of Contents] Thermoplastic Elastomeric Bottlebrush Copolymer Glue Electrolyte Featuring Dual‐Ion Transport Channels and Strong Segregation: Experimental and Simulation Insights

Wiley: Advanced Functional Materials: Table of Contents — Mon, 05 Jan 2026 15:13:34 GMT

Advanced Functional Materials, Volume 36, Issue 2, 5 January 2026.

[Wiley: Advanced Science: Table of Contents] CGRP Enhances the Regeneration of Bone Defects by Regulating Bone Marrow Mesenchymal Stem Cells Through Promoting ANGPTL4 Secretion by Bone Blood Vessels

Wiley: Advanced Science: Table of Contents — Mon, 05 Jan 2026 09:55:39 GMT

Advanced Science, EarlyView.

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Pd/Cu Dual Catalysis for Stereodivergent Allylic Alkylation of α-F-Substituted Azaaryl Acetates and Acetamides with Morita–Baylis–Hillman Carbonates

Chinese Chemical Society: CCS Chemistry: Table of Contents — Mon, 05 Jan 2026 07:20:22 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202506840

A palladium/copper dual catalytic system has been developed for the asymmetric allylic alkylation of Morita–Baylis–Hillman carbonates with α-fluoro-2-azaaryl acetates. This system delivers a series of chiral fluorinated compounds featuring an azaaryl ...

[Wiley: Carbon Energy: Table of Contents] Strategies to Enhance Ionic Conductivity of Na3Zr2Si2O12 Solid Electrolyte for Advanced Solid‐State Sodium Batteries

Wiley: Carbon Energy: Table of Contents — Mon, 05 Jan 2026 07:00:12 GMT

Carbon Energy, EarlyView.

[Wiley: Advanced Science: Table of Contents] Diffusion‐MRI‐Based Estimation of Cortical Architecture via Machine Learning (DECAM) in Primate Brains

Wiley: Advanced Science: Table of Contents — Mon, 05 Jan 2026 05:33:28 GMT

Advanced Science, EarlyView.

[cond-mat updates on arXiv.org] Automated electrostatic characterization of quantum dot devices in single- and bilayer heterostructures

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

arXiv:2601.00067v1 Announce Type: new Abstract: As quantum dot (QD)-based spin qubits advance toward larger, more complex device architectures, rapid, automated device characterization and data analysis tools become critical. The orientation and spacing of transition lines in a charge stability diagram (CSD) contain a fingerprint of a QD device's capacitive environment, making these measurements useful tools for device characterization. However, manually interpreting these features is time-consuming, error-prone, and impractical at scale. Here, we present an automated protocol for extracting underlying capacitive properties from CSDs. Our method integrates machine learning, image processing, and object detection to identify and track charge transitions across large datasets without manual labeling. We demonstrate this method using experimentally measured data from a strained-germanium single-quantum-well (planar) and a strained-germanium double-quantum-well (bilayer) QD device. Unlike for planar QD devices, CSDs in bilayer germanium heterostructure exhibit a larger set of transitions, including interlayer tunneling and distinct loading lines for the vertically stacked QDs, making them a powerful testbed for automation methods. By analyzing the properties of many CSDs, we can statistically estimate physically relevant quantities, like relative lever arms and capacitive couplings. Thus, our protocol enables rapid extraction of useful, nontrivial information about QD devices.

[cond-mat updates on arXiv.org] Atomic-Scale Mechanisms of Li-Ion Transport Mediated by Li10GeP2S12 in Composite Solid Polyethylene Oxide Electrolytes

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

arXiv:2601.00112v1 Announce Type: new Abstract: Polymer electrolytes incorporating Li$_{10}$GeP$_{2}$S$_{12}$ (LGPS) nanoparticles show promise for solid-state lithium batteries owing to their enhanced ionic conductivity, though the governing mechanisms remain unclear. We combine molecular dynamics (MD) simulations, experimental ionic conductivity measurements, and density functional theory (DFT) calculations to elucidate the effect of LGPS loading on polyethylene oxide (PEO) structure and Li-ion transport. MD and experimental results agree up to 10\% LGPS, showing a volcano-shaped conductivity trend driven by polymer segmental dynamics and interfacial effects. Beyond 10\%, experiments reveal additional conductivity enhancement unexplained by MD, suggesting a distinct transport regime. DFT calculations indicate that Li-ion migration at the PEO|LGPS interface proceeds via vacancy-mediated hopping, with low barriers favored by S-rich interfacial sites and hindered by Ge. These findings link interfacial chemistry and microstructure to Li-ion dynamics, offering guidelines for designing high-performance composite polymer electrolytes.

[cond-mat updates on arXiv.org] Engineering Ideal 2D Type-II Nodal Line Semimetals via Stacking and Intercalation of van der Waals Layers

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

arXiv:2601.00407v1 Announce Type: new Abstract: Two-dimensional type-II topological semimetals (TSMs), characterized by strongly tilted Dirac cones, have attracted intense interest for their unconventional electronic properties and exotic transport behaviors. However, rational design remains challenging due to the sensitivity of band tilting to lattice geometry, atomic coordination, and symmetry constraints. Here, we present a bottom-up approach to engineer ideal type-II nodal line semimetals (NLSMs) in van der Waals bilayers via atomic intercalation. Using monolayer $h$-AlN as a prototype, we show that fluorine-intercalated bilayer AlN (F@BL-AlN) hosts a symmetry-protected type-II nodal loop precisely at the Fermi level, enabled by preserved mirror symmetry ($\mathcal{M}_z$) and tailored interlayer hybridization. First-principles calculations reveal that fluorine not only tunes interlayer coupling but also aligns the Fermi energy with the nodal line, stabilizing the type-II NLSM phase. The system exhibits tunable electronic properties under external electric and strain fields and features a van Hove singularity that induces spontaneous ferromagnetism, realizing a ferromagnetic topological semimetal state. This work provides a versatile platform for designing type-II NLSMs and offers practical guidance for their experimental realization.

[cond-mat updates on arXiv.org] Kinetic Turing Instability and Emergent Spectral Scaling in Chiral Active Turbulence

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

arXiv:2508.21012v5 Announce Type: cross @@ -101,4 +101,4 @@ 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...
The content of this RSS Feed (c) The Royal Society of Chemistry

[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.
To cite this article before page numbers are assigned, use the DOI form of citation above.
The content of this RSS Feed (c) The Royal Society of Chemistry

[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.

[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

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] 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

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

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.
The content of this RSS Feed (c) The Royal Society of Chemistry

[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.

[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

Open Access

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

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.
The content of this RSS Feed (c) The Royal Society of Chemistry

[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

[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

Open Access

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

Alon Grinberg Dana
From LLM reasoning to action: specialized agents coordinate kinetic modeling to produce transparent, uncertainty-aware, reproducible mechanisms.
The content of this RSS Feed (c) The Royal Society of Chemistry

[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.
To cite this article before page numbers are assigned, use the DOI form of citation above.
The content of this RSS Feed (c) The Royal Society of Chemistry

[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

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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

[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

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[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

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[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

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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

[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

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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

[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

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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

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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

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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.

[Chinese Chemical Society: CCS Chemistry: Table of Contents] IR-Bot: An Autonomous Robotic System for Real-Time Chemical Mixture Analysis via Infrared Spectroscopy and Machine Learning

Chinese Chemical Society: CCS Chemistry: Table of Contents — Thu, 11 Dec 2025 04:43:30 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202505768

Rapid and accurate quantification of chemical mixtures is vital in autonomous chemical experimentation, providing real-time feedback that guides decision-making and reduces resource consumption. Here, we present IR-Bot, an intelligent system that ...

[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.

[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

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

[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.

[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

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

[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

Open Access

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

[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

Open Access

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

[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.

[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
.

[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

[ScienceDirect Publication: Journal of Energy Storage] Polydopamine coating on garnet-type solid electrolyte for enhancing interfacial compatibility in solid-state lithium metal batteries

[ScienceDirect Publication: Science Bulletin] Machine learning-based diagnosis of uterine myomas and sarcomas using tumor-educated platelet transcriptomics: a retrospective multicenter study

[ScienceDirect Publication: Solid State Ionics] Enhanced ionic conductivity and dielectric performance of CaB₂O₄-doped 2-hydroxyethyl cellulose polymer electrolytes for electrical double layer capacitor applications

[ScienceDirect Publication: Solid State Ionics] One – Step synthesis of glass ceramic Li6PS5Cl1-xIx solid electrolytes for all-solid-state batteries

[Wiley: Small Methods: Table of Contents] Interfacial Stability and Design Strategies for Halide Solid Electrolytes in High‐Voltage All‐Solid‐State Sodium‐Ion Batteries

[cond-mat updates on arXiv.org] Physically Consistent Machine Learning for Melting Temperature Prediction of Refractory High-Entropy Alloys

[ScienceDirect Publication: Journal of Energy Storage] Polydopamine coating on garnet-type solid electrolyte for enhancing interfacial compatibility in solid-state lithium metal batteries

[ScienceDirect Publication: Science Bulletin] Machine learning-based diagnosis of uterine myomas and sarcomas using tumor-educated platelet transcriptomics: a retrospective multicenter study

[ScienceDirect Publication: Solid State Ionics] Enhanced ionic conductivity and dielectric performance of CaB₂O₄-doped 2-hydroxyethyl cellulose polymer electrolytes for electrical double layer capacitor applications

[ScienceDirect Publication: Solid State Ionics] One – Step synthesis of glass ceramic Li6PS5Cl1-xIx solid electrolytes for all-solid-state batteries

[Recent Articles in Phys. Rev. Lett.] Enhancing Gravitational-Wave Detection: A Machine Learning Pipeline Combination Approach with Robust Uncertainty Quantification

[Recent Articles in Phys. Rev. B] Universal band center model for the HER activity of nonmetal sites in transition metal dichalcogenides

[Wiley: Small Methods: Table of Contents] Interfacial Stability and Design Strategies for Halide Solid Electrolytes in High‐Voltage All‐Solid‐State Sodium‐Ion Batteries

[cond-mat updates on arXiv.org] Physically Consistent Machine Learning for Melting Temperature Prediction of Refractory High-Entropy Alloys

[cond-mat updates on arXiv.org] Material exploration through active learning -- METAL

[cond-mat updates on arXiv.org] Transport properties in a model of confined granular mixtures at moderate densities

[cond-mat updates on arXiv.org] libMobility: A Python library for hydrodynamics at the Smoluchowski level

[cond-mat updates on arXiv.org] Kinetic Flux Equations for Ion Exchange in Silicate Glasses

[cond-mat updates on arXiv.org] Agentic Exploration of Physics Models

[cond-mat updates on arXiv.org] Masgent: An AI-assisted Materials Simulation Agent

[ChemRxiv] The unique example of approximation of the electronic term of diatomic molecules by Morse potential. HF, DF, TF.

[ChemRxiv] Machine-Learning-Accelerated Simulations of Vibrational Activation for Controlled Photoisomerization in a Molecular Motor

[RSC - Chem. Sci. latest articles] Dynamic Protein Structures in Solution: Decoding the Amide I Band with 2D-IR Spectral Libraries and Machine Learning

[ChemRxiv] Atomistic insights into the chemical stability and ionic transport at Li-metal/Li-Argyrodite interfaces

[ScienceDirect Publication: Journal of Energy Storage] Optimization of porous electrode configuration for organic redox flow battery by machine learning based on back propagation neural network based on firefly

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

[Wiley: Advanced Functional Materials: Table of Contents] Dynamic Li‐S Coordination Boosted Superionic Conduction in Cubic LiBS2 Solid Electrolyte

[Wiley: Advanced Science: Table of Contents] Sulfonated Cellulose Acetate Nanofibers Induced Zincophilic‐Hydrophobic Interface to Regulate Ion Transport for Long‐Lifespan Zinc‐Iodine Batteries

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Machine Learning Prediction of Two-Dimensional Polymerization of Nitrogen in FeNx

[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

[ChemRxiv] The unique example of approximation of the electronic term of diatomic molecules by Morse potential. HF, DF, TF.

[ChemRxiv] Machine-Learning-Accelerated Simulations of Vibrational Activation for Controlled Photoisomerization in a Molecular Motor

[RSC - Chem. Sci. latest articles] Dynamic Protein Structures in Solution: Decoding the Amide I Band with 2D-IR Spectral Libraries and Machine Learning

[ChemRxiv] Atomistic insights into the chemical stability and ionic transport at Li-metal/Li-Argyrodite interfaces

[Communications Materials] Extracting and reconstructing knowledge in materials science literature using large language models

[ScienceDirect Publication: Journal of Energy Storage] Optimization of porous electrode configuration for organic redox flow battery by machine learning based on back propagation neural network based on firefly

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

[Wiley: Advanced Functional Materials: Table of Contents] Dynamic Li‐S Coordination Boosted Superionic Conduction in Cubic LiBS2 Solid Electrolyte

[Wiley: Advanced Science: Table of Contents] Sulfonated Cellulose Acetate Nanofibers Induced Zincophilic‐Hydrophobic Interface to Regulate Ion Transport for Long‐Lifespan Zinc‐Iodine Batteries

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Machine Learning Prediction of Two-Dimensional Polymerization of Nitrogen in FeNx

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Single-Round Aptamer Discovery Empowered by Machine Learning: Revealing Structure–Function Principles of Target Binding

[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

[ChemRxiv] A Systematic Review of Prompt Engineering Paradigms in Organic Chemistry: Mining, Prediction, and Model Architectures

[ChemRxiv] Ensemble Analyzer: An Open-Source Python Framework for Automated Conformer Ensemble Refinement

[ChemRxiv] Electrostatic Patterning Controls Mineral Nucleation Inside Ferritin

[ChemRxiv] Machine Learning Prediction of Henry Coefficients of Polar and Nonpolar Gases in Covalent Organic Frameworks: Effects of Interlayer Shifts and Functionalization

[ChemRxiv] Bridging the Gap: Aqueous Phase Organic Synthesis as a Foundation for Advanced Chemical and Biological Discovery

[RSC - Chem. Sci. latest articles] Reactive Fluorescent Probe for Covalent Membrane-Anchoring: Enabling Real-time Imaging of Protein Aggregation Dynamics in Live Cells

[npj Computational Materials] Composition-dependent dislocation mobility in FeNiCrCoCu high-entropy alloys based on atomistic simulations and machine learning analysis

[npj Computational Materials] AI-assisted rapid crystal structure generation towards a target local environment

[Applied Physics Letters Current Issue] Polarization-controlled multistate thermal conductivity in ferroelectric HfO 2 thin films

[ScienceDirect Publication: Journal of Catalysis] Microkinetic modeling of methane activation in Mo/ZSM-5 with machine learning potentials

[Wiley: Advanced Functional Materials: Table of Contents] Thermoplastic Elastomeric Bottlebrush Copolymer Glue Electrolyte Featuring Dual‐Ion Transport Channels and Strong Segregation: Experimental and Simulation Insights (Adv. Funct. Mater. 2/2026)

[Wiley: Advanced Functional Materials: Table of Contents] Regulating Metallic Deposition Behavior by Gradient Alloy/Solid Electrolyte Interphase for Durable Na/K Anodes (Adv. Funct. Mater. 2/2026)

[Wiley: Advanced Functional Materials: Table of Contents] Autonomous Liquid Metal Droplets Actuated by Ion Diffusion

[Wiley: Advanced Functional Materials: Table of Contents] Microcrack‐Structured Visualizable Hydrogel Sensor for Machine Learning‐Assisted Handwriting Recognition

[Wiley: Advanced Functional Materials: Table of Contents] Regulating Metallic Deposition Behavior by Gradient Alloy/Solid Electrolyte Interphase for Durable Na/K Anodes

[Wiley: Advanced Functional Materials: Table of Contents] Thermoplastic Elastomeric Bottlebrush Copolymer Glue Electrolyte Featuring Dual‐Ion Transport Channels and Strong Segregation: Experimental and Simulation Insights

[Wiley: Advanced Science: Table of Contents] CGRP Enhances the Regeneration of Bone Defects by Regulating Bone Marrow Mesenchymal Stem Cells Through Promoting ANGPTL4 Secretion by Bone Blood Vessels

[Wiley: Carbon Energy: Table of Contents] Strategies to Enhance Ionic Conductivity of Na3Zr2Si2O12 Solid Electrolyte for Advanced Solid‐State Sodium Batteries

[Wiley: Advanced Science: Table of Contents] Diffusion‐MRI‐Based Estimation of Cortical Architecture via Machine Learning (DECAM) in Primate Brains

[cond-mat updates on arXiv.org] Automated electrostatic characterization of quantum dot devices in single- and bilayer heterostructures

[RSC - Chem. Sci. latest articles] Reactive Fluorescent Probe for Covalent Membrane-Anchoring: Enabling Real-time Imaging of Protein Aggregation Dynamics in Live Cells

[npj Computational Materials] Composition-dependent dislocation mobility in FeNiCrCoCu high-entropy alloys based on atomistic simulations and machine learning analysis

[npj Computational Materials] AI-assisted rapid crystal structure generation towards a target local environment

[Applied Physics Letters Current Issue] Polarization-controlled multistate thermal conductivity in ferroelectric HfO 2 thin films

[ScienceDirect Publication: Journal of Catalysis] Microkinetic modeling of methane activation in Mo/ZSM-5 with machine learning potentials

[Wiley: Advanced Functional Materials: Table of Contents] Thermoplastic Elastomeric Bottlebrush Copolymer Glue Electrolyte Featuring Dual‐Ion Transport Channels and Strong Segregation: Experimental and Simulation Insights (Adv. Funct. Mater. 2/2026)

[Wiley: Advanced Functional Materials: Table of Contents] Regulating Metallic Deposition Behavior by Gradient Alloy/Solid Electrolyte Interphase for Durable Na/K Anodes (Adv. Funct. Mater. 2/2026)

[Wiley: Advanced Functional Materials: Table of Contents] Autonomous Liquid Metal Droplets Actuated by Ion Diffusion

[ScienceDirect Publication: Solid State Ionics] One – Step synthesis of glass ceramic Li₆PS₅Cl_1-xI_x solid electrolytes for all-solid-state batteries

[ScienceDirect Publication: Solid State Ionics] One – Step synthesis of glass ceramic Li₆PS₅Cl_1-xI_x solid electrolytes for all-solid-state batteries