My Customized Papers (Auto-Filtered)

[cond-mat updates on arXiv.org] Atomic-scale visualization of d-wave altermagnetism

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

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 — Wed, 07 Jan 2026 05:00:00 GMT

arXiv:2601.02424v1 Announce Type: new +Abstract: The synthesis of nanocrystals has been highly dependent on trial-and-error, due to the complex correlation between synthesis parameters and physicochemical properties. Although deep learning offers a potential methodology to achieve generative inverse design, it is still hindered by the scarcity of high-quality datasets that align nanocrystal synthesis routes with their properties. Here, we present the construction of a large-scale, aligned Nanocrystal Synthesis-Property (NSP) database and demonstrate its capability for generative inverse design. To extract structured synthesis routes and their corresponding product properties from literature, we develop NanoExtractor, a large language model (LLM) enhanced by well-designed augmentation strategies. NanoExtractor is validated against human experts, achieving a weighted average score of 88% on the test set, significantly outperforming chemistry-specialized (3%) and general-purpose LLMs (38%). The resulting NSP database contains nearly 160,000 aligned entries and serves as training data for our NanoDesigner, an LLM for inverse synthesis design. The generative capability of NanoDesigner is validated through the successful design of viable synthesis routes for both well-established PbSe nanocrystals and rarely reported MgF2 nanocrystals. Notably, the model recommends a counter-intuitive, non-stoichiometric precursor ratio (1:1) for MgF2 nanocrystals, which is experimentally confirmed as critical for suppressing byproducts. Our work bridges the gap between unstructured literature and data-driven synthesis, and also establishes a powerful human-AI collaborative paradigm for accelerating nanocrystal discovery.

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

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

arXiv:2601.02699v1 Announce Type: new +Abstract: Heat dissipation is ubiquitous in living systems, which constantly convert distinct forms of energy into each other. The transport of thermal energy in liquids and even within proteins is well understood but kinetic energy transfer across a heterogeneous molecular boundary provides additional challenges. Here, we use atomistic molecular dynamics simulations under steady-state conditions to analyze how a protein dissipates surplus thermal energy into the surrounding solvent. We specifically focus on collective degrees of freedom that govern the dynamics of the system from the diffusive regime to mid-infrared frequencies. Using a fully anharmonic analysis of molecular vibrations, we analyzed their vibrational spectra, temperatures, and heat transport efficiencies. We find that the most efficient energy transfer mechanisms are associated with solvent-mediated friction. However, this mechanism only applies to a small number of degrees of freedom of a protein. Instead, less efficient vibrational energy transfer in the far-infrared dominates heat transfer overall due to a large number of vibrations in this frequency range. A notable by-product of this work is a highly sensitive measure of deviations from energy equi-partition in equilibrium systems, which can be used to analyze non-ergodic properties.

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

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

arXiv:2601.02869v1 Announce Type: new +Abstract: Solid-state batteries (SSB) are emerging as next-generation electrochemical energy storage devices. Achieving high energy density in SSB relies on solid polymer electrolytes (SPE) that are electrochemically stable against both lithium metal and high-potential positive electrodes, two conditions that are difficult to satisfy without chemical degradation. In this work, molecular dynamics simulations are employed to investigate the relationship between structure and dynamics in carbonate-based SPE composed of polypropylene carbonate and lithium hexafluorophosphate (LiPF$_6$), at salt concentrations ranging from 0.32 to 1.21 mol$/$kg. Structural properties are analyzed under ambient pressure at the experimentally relevant temperature $T = 353$ K. Since the slow dynamical processes governing ion transport in these systems are inaccessible to direct molecular dynamics, transport properties are simulated at elevated temperatures up to 900 K and extrapolated to $T = 353$ K using Arrhenius behavior. The results reveal strong ionic correlations, a limited fraction of free ions, and a predominance of negatively charged clusters, especially at high salt concentration. At high temperature, the self-diffusion coefficient of Li$^+$ exceeds that of PF$_6^-$ due to weaker Li$^+$-carbonate and ion-ion interactions. However, at $T = 353$ K, Li$^+$ mobility becomes lower than that of the anion, consistent with typical experimental observations in SPE. As expected, the ionic conductivity $\sigma$ increases with temperature, while at $T = 353$ K it exhibits a maximum for salt concentrations between 1.0 and 1.1 mol$/$kg. Overall, the estimated physico-chemical parameters highlight the key role of ion correlations in SPE and suggest strategies to optimize electrolyte performance. The Arrhenius extrapolation approach used here provides valuable insight into ion transport mechanisms in solid polymer electrolytes.

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

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

arXiv:2601.02938v1 Announce Type: new +Abstract: In computational physics and materials science, first-principles methods, particularly density functional theory, have become central tools for electronic structure prediction and materials design. Recently, rapid advances in artificial intelligence (AI) have begun to reshape the research landscape, giving rise to the emerging field of deep-learning electronic structure calculations. Despite numerous pioneering studies, the field remains in its early stages; existing software implementations are often fragmented, lacking unified frameworks and standardized interfaces required for broad community adoption. Here we present DeepH-pack, a comprehensive and unified software package that integrates first-principles calculations with deep learning. By incorporating fundamental physical principles into neural-network design, such as the nearsightedness principle and the equivariance principle, DeepH-pack achieves robust cross-scale and cross-material generalizability. This allows models trained on small-scale structures to generalize to large-scale and previously unseen materials. The toolkit preserves first-principles accuracy while accelerating electronic structure calculations by several orders of magnitude, establishing an efficient and intelligent computational paradigm for large-scale materials simulation, high-throughput materials database construction, and AI-driven materials discovery.

[cond-mat updates on arXiv.org] Novel fast Li-ion conductors for solid-state electrolytes from first-principles

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

arXiv:2601.03151v1 Announce Type: new +Abstract: We present a high-throughput computational screening for fast lithium-ion conductors to identify promising materials for application in all solid-state electrolytes. Starting from more than 30,000 Li-containing experimental structures sourced from Crystallography Open Database, Inorganic Crystal Structure Database and Materials Platform for Data Science, we perform highly automated calculations to identify electronic insulators. On these ~1000 structures, we use molecular dynamics simulations to estimate Li-ion diffusivities using the pinball model, which describes the potential energy landscape of diffusing lithium with accuracy similar to density functional theory while being 200-500 times faster. Then we study the ~60 most promising and previously unknown fast conductors with full first-principles molecular dynamics simulations at several temperatures to estimate their activation barriers. The results are discussed in detail for the 9 fastest conductors, including $Li_7NbO_6$ which shows a remarkable ionic conductivity of ~5 mS/cm at room temperature. We further present the entire screening protocol, including the workflows where the accuracy of the pinball model is improved self-consistently, necessary to automatically running the required calculations and analysing their results.

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

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

arXiv:2601.03207v1 Announce Type: new +Abstract: Ion exchange kinetic flux equations have been extensively investigated since the mid-twentieth century and continue to provide a fundamental framework for describing mass transport phenomena in solid materials. Despite the maturity of this field, inconsistencies remain in the literature concerning the definition, dimensional consistency, and physical interpretation of the parameters involved. A rigorous and unified treatment of these equations is therefore essential to ensure the reproducibility and comparability of theoretical and experimental studies. The present study aims to establish a coherent and systematic development of ion exchange kinetic flux equations, with particular emphasis on the consistent definition and dimensional formulation of the relevant physical quantities. Beyond refining the theoretical foundations, this study extends the classical formulation by incorporating the influence of mechanical stress on ion transport and considering cross-term interactions within the framework of linear irreversible thermodynamics. These developments provide a more comprehensive description of ion exchange kinetics, particularly as applied to silicate glasses, where coupling between chemical and mechanical effects plays a crucial role in determining transport behavior and performance.

[cond-mat updates on arXiv.org] Machine Learning H-theorem

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

arXiv:2508.14003v3 Announce Type: replace +Abstract: H-theorem provides a microscopic foundation of the Second Law of Thermodynamics and is therefore essential to establishing statistical physics, but at the same time, H-theorem has been subject to controversy that in part persists till this day. To better understand H-theorem and its relation to the arrow of time, we study the equilibration of randomly oriented and positioned hard disks with periodic boundary conditions. Using a model based on the DeepSets architecture, which imposes permutation invariance of the particle labels, we train a model to capture the irreversibility of the H-functional.

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

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

arXiv:2509.14873v2 Announce Type: replace +Abstract: We consider helicoidal spin-orbit coupled Bose-Einstein condensates in deep optical lattice and study the dynamics of Bloch oscillation. We show that the variation of helicoidal gauge potential with spin-orbit coupling is different in zero-momentum and plane-wave phases. The characteristics of Bloch oscillation are different in the two phases. In the zero-momentum phase, the Bloch oscillation is harmonic while it is anharmonic in the plane-wave phase. The amplitude of Bloch oscillation is found to be affected by the helicoidal gauge potential and spin-orbit coupling. We examine that the decay of Bloch oscillation caused by mean-field interaction can be managed by helicoidal spin-orbit coupling.

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

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

arXiv:2512.19449v2 Announce Type: replace +Abstract: Aqueous zinc (Zn) batteries (AZBs) face significant challenges due to the limited compatibility of Zn anodes with conventional separators, leading to dendrite growth, hydrogen evolution reaction (HER), and poor cycling stability. While separator design is crucial for optimizing battery performance, its potential remains underexplored. The commonly used glass fiber (GF) filters were not originally designed as battery separators. To address their limitations, nanochitin derived from waste shrimp shells was used to fabricate separators with varying concentrations of amine and carboxylic functional groups. This study investigates how the type and concentration of these groups influence the separator's properties and performance. In a mild acidic electrolyte that protonates the amine groups, the results showed that the density of both ammonium and carboxylic groups in the separators significantly affected water structure and ionic conductivity. Quasi-Elastic Neutron Scattering (QENS) revealed that low-functionalized chitin, particularly with only ammonium groups, promotes strongly bound water with restricted mobility, thereby enhancing Zn plating and stripping kinetics. These separators exhibit exceptional Zn stability over 2000 hours at low current densities (0.5 mA/cm2), maintaining low overpotentials and stable polarization. Additionally, the full cell consisting of Zn||NaV3O8.1.5H2O showed a cycle life of over 2000 cycles at 2 A/g, demonstrating the compatibility of the nanochitin-based separators with low concentrations of functional surface groups. These results demonstrate the importance of a simple separator design for improving the overall performance of AZBs.

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

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

arXiv:2512.24822v2 Announce Type: replace-cross +Abstract: Floquet engineering offers an unparalleled platform for realizing novel non-equilibrium topological phases. However, the unique structure of Floquet systems, which includes multiple quasienergy gaps, poses a significant challenge to classification using conventional analytical methods. We propose a novel unsupervised machine learning framework that employs a kernel defined in momentum-time ($\boldsymbol{k},t$) space, constructed directly from Floquet-Bloch eigenstates. This approach is intrinsically data-driven and requires no prior knowledge of the underlying topological invariants, providing a fundamental advantage over prior methods that rely on abstract concepts like the micromotion operator or homotopic transformations. Crucially, this work successfully reveals the intrinsic topological characteristics encoded within the Floquet eigenstates themselves. We demonstrate that our method robustly and simultaneously identifies the topological invariants associated with both the $0$-gap and the $\pi$-gap across various symmetry classes (1D AIII, 1D D, and 2D A), establishing a robust methodology for the systematic classification and discovery of complex non-equilibrium topological matter.

[tandf: Materials Research Letters: Table of Contents] Physical-information machine learning for strength and ductility prediction of metastable β titanium alloys

tandf: Materials Research Letters: Table of Contents — Wed, 07 Jan 2026 02:19:21 GMT

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

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

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

In this work, Chang et al. report a thermotropic liquid additive for perovskite solar cells that enables dynamic interface management, simultaneously passivating defects and suppressing ion migration to deliver high efficiency and substantially enhanced operational stability.

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

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

Computational blind challenges offer critical, unbiased assessment opportunities to assess and accelerate scientific progress, as demonstrated by a breadth of breakthroughs over the last decade. We report the outcomes and key insights from an open science community blind challenge focused on computational methods in drug discovery, using lead optimization data from the AI-driven Structure-enabled Antiviral Platform (ASAP) Discovery Consortium’s pan-coronavirus antiviral discovery program, in partnership with Polaris and the OpenADMET project. This collaborative initiative invited global participants from both academia and industry to develop and apply computational methods to predict the biochemical potency and crystallographic ligand poses of small molecules against key coronavirus targets, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) main protease (Mpro), as well as multiple ADMET assay endpoints, using previously undisclosed comprehensive experimental drug discovery datasets as benchmarks. By evaluating submissions across multiple tasks and compounds, we established performance leaderboards and conducted meta-analyses to assess methodological strengths, common pitfalls, and areas for improvement. This analysis provides a foundation for best practices in real-world machine learning evaluation, grounded in community-driven benchmarking. We also highlight how next-generation platforms, such as Polaris, enable rigorous challenge design, embedded evaluation frameworks, and broad community engagement. This paper reports the collective findings of the challenge, offering a high-level overview of the data, evaluation infrastructure, and top- performing strategies. We further provide context and support for the accompanying papers authored by the challenge participants in this special issue, which explore individual approaches in greater depth. Together, these contributions aim to advance reproducible, trustworthy, and high-impact computational methods in drug discovery, and to explore best practices and pitfalls in future blind challenge design and execution, including planned initiatives for the OpenADMET project.

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

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

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

Uncertainty-aware machine learning models predict human toxicity for more than 100,000 chemicals, highlighting potency and uncertainty hotspots to guide safer use and to focus efforts to improve prediction confidence.

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

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

Extended X-ray absorption fine structure (EXAFS) provides element-specific access to local atomic environments and is widely used to relate structure and reactivity across chemical systems. However, quantitative EXAFS interpretation still relies on manually constructed structural models and extensive parameter tuning, creating a growing bottleneck as experimental datasets increase in size and complexity. Addressing this bottleneck requires a direct and systematic mapping between atomic structure and EXAFS response. Here we introduce AI-EXAFS, a physics-informed graph neural network that predicts full EXAFS spectra directly from three-dimensional atomic coordinates. By formulating the learning problem around the physical principles governing EXAFS signal formation, the model learns transferable structure–spectrum relationships and eliminates the need for user-defined parameter selection at inference. Trained on 86,000 transition-metal complexes, AI-EXAFS reproduces reference theoretical spectra with accuracy consistent with established EXAFS analysis practice and generalizes to experimentally relevant systems, including platinum single-atom catalysts. AI-EXAFS provides an accurate and readily deployable forward model for EXAFS, enabling standardized first-pass structural screening and offering a scalable foundation for future extensions toward more realistic and data-rich EXAFS analysis.

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

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

Protein loops harness conformational heterogeneity to perform an array of functions, ranging from catalyzing enzymatic reactions to communicating allosteric signals. Although attractive targets for small molecule modulation, these functional hubs are often considered unligandable due to their lack of well-defined binding pockets and highly dynamic structure. Recent studies, however, have demonstrated the power of covalent chemistry to selectively capture cryptic pockets formed by protein loops. Herein, we leverage machine learning to elucidate the molecular basis of covalent ligand:loop recognition in the transcriptional coactivator Med25. Key to our success was classification by ligand shape prior to model training, which led to descriptive and predictive models. The models were experimentally validated through the synthesis and in vitro testing of novel top-ranked ligands, revealing canonical structure-affinity relationships, including an activity cliff. Further feature analyses identified traditional topological and spatial parameters predictive of binding, and molecular modeling uncovered a potential binding pocket with at least two distinct conformations with high shape complementarity. Collectively, these findings reveal the hidden potential of dynamic loops as specific sites for covalent small molecule modulation, challenging the notion that protein loops are unligandable and demonstrating their capacity for exquisite, shape-based molecular recognition.

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

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

Computational modeling of enzymes provides molecular-level insight into catalysis, but the preparation of quantum mechanical (QM) calculations starting from experimental structures is a significant bottleneck for high-throughput studies. Automated tools developed to accelerate this process may fail to generalize across distinct active site chemistries and geometries. To overcome these limitations, we present QuantumPDB, a Python package that automates the generation of hierarchical coordination/interaction spheres around an active center to create QM cluster models directly from raw protein structures. The workflow integrates structure cleaning, protonation state assignment, and QM calculation setup. It uses chemically meaningful models constructed from contact-based interaction spheres derived from Voronoi tessellation, enabling accurate representation of complex active site geometries. We provide an overview of our modular code and describe how it may be employed to automate high-throughput protein screening. To demonstrate its utility, we curated a dataset of 989 holo-enzymes from the PDB and performed QM calculations on 1,673 enzyme cluster models of 842 of these enzymes. Analysis of computed properties suggests that enzyme environments simulated with density functional theory consistently modulate substrate charge toward neutrality and reduce the substrate dipole moment. This phenomenon appears to be general, even in cases where the active site consists predominantly of neutral residues. By automating and standardizing multi-sphere QM model construction, QuantumPDB provides a robust platform for large-scale, data-driven investigations of proteins.

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

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

Deep learning co-folding models for end-to-end protein–ligand structure prediction mark a major advance beyond AlphaFold2, yet their reliability for decision-making in drug discovery remains unclear. Here, we benchmark Boltz, a state-of-the-art co-folding model, using a curated set of ligand-bound human G protein-coupled receptors (GPCRs) from families unseen during training. We find that the receptor backbones are generally predicted with reasonable accuracy, but ligand poses often deviate significantly from experimental structures. We then evaluate physics-based refinement with rigid-receptor (Glide) and induced-fit docking (IFD-MD) methods, which recover more than half of the misplaced ligands to near-experimental accuracy. As conventional evaluations for co-folded structures focus on distance-based metrics such as root-mean-squared deviation (RMSD), which can miss subtle but consequential binding-site errors, we carry out a further assessment of Boltz performance using free-energy perturbation (FEP+), which is both accurate and sensitive to starting-structure quality, on curated congeneric ligand series with known binding affinities that target the GPCRs. A significant fraction of the 14 congeneric series tested in this fashion fail to reproduce experimental binding affinities via FEP+ when employing the Boltz generated complex, even when the binding-site RMSD is low in some cases. IFD-MD rescues these failures and restores retrospective FEP signals to native-like level for all of these series. Together, these results delineate current generalization and usability limits of co-folded GPCR–ligand complexes and motivate a workflow that pairs deep learning predictions with physics-based refinement and validation before high-stakes decisions in drug discovery.

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

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

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

Parameterized quantum circuits are a common tool in variational quantum algorithms and quantum machine learning. The authors design an interpolation-based coordinate descent method that reconstructs the cost landscape from a few circuit runs and achieves more efficient training than standard gradient and coordinate descent methods in our numerical tests.

[RSC - Digital Discovery latest articles] A case study on hybrid machine learning and quantum-informed modelling for solubility prediction of drug compounds in organic solvents

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

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

Open Access

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

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

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

Olfaction arises from the interaction of odorants with olfactory receptors, a process shaped by molecular geometry, electron distribution, and conformational preference. We present ConfDENSE, a Set2Set enhanced PointNet model that learns directly from Hirshfeld promolecule electron-density point clouds, preserving full 3D electronic in- formation without downsampling.Despite using no receptor structural data, ConfDENSE accurately identifies bioac- tive conformers from ensemble inputs. For the only available human odorant receptor structures, the model’s selected conformers achieve sub-angstrom RMSDs to crystallo- graphic ligand poses and frequently outperform conventional docking. Combining ConfDENSE with explainability analysis further reveals the substruc- tures most responsible for receptor engagement, aligning with experimental interaction patterns. This ligand-centric and interpretable framework naturally supports phar- macophore extraction and scaffold-based design, enabling identification of conserved binding motifs even when receptor structures are missing. ConfDENSE thus provides a compact, physics-aware approach to computational olfaction, linking electron density, conformational preference, and odorant recognition in a structurally agnostic manner.

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

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

The vast and multiscale nature of chemical knowledge—from molecular structures to material properties—presents significant challenges for both human researchers and artificial intelligence (AI) systems. While large language models (LLMs) can process chemical information, they operate as black boxes without transparent reasoning. Here, we present our multi-agent ontology system for explainable knowledge synthesis (MOSES), a framework that combines automated knowledge organization with multi-agent collaboration to create an AI system for interpretable chemical knowledge reasoning. Using supramolecular chemistry as a testbed, we automatically constructed an ontology of over 10 000 classes from 52 publications and developed a multi-agent system that enables transparent knowledge retrieval and reasoning. Evaluations by human experts and LLMs show that MOSES significantly outperforms chemistry-oriented LLMs and leading general-purpose LLMs—including GPT-4.1 and o3—as well as GraphRAG-augmented GPT-4.1 models, on complex chemical questions, achieving superior scores in both direct assessments and Elo ratings. MOSES’s traceable reasoning paths reveal how it constructs answers through iterative refinement rather than probabilistic generation. However, we observe an asymmetry in handling positive versus negative knowledge claims, underscoring fundamental challenges in open-world reasoning. Our work demonstrates a pathway toward AI systems that can reason over complex scientific knowledge in a transparent and explainable manner.

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

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

Zhang et al. review cutting-edge strategies for separating, detecting, and characterizing nanoplastics across environmental and biological systems. This work bridges advances in spectroscopy, microscopy, mass spectrometry, and machine learning to highlight analytical limitations, emerging solutions, and future opportunities for standardized nanoplastics monitoring.

[iScience] Large language models for predicting one-year major adverse cardiovascular events in acute coronary syndrome

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

Effective risk stratification is crucial for managing acute coronary syndrome (ACS). This study evaluated whether general-purpose large language models (LLMs) can reliably execute the complex clinical reasoning required for cardiovascular prognosis. We quantitatively assessed three LLMs —ChatGPT 4o, DeepSeek R1, and Grok 3—for predicting one-year major adverse cardiovascular events (MACEs), using 29 guideline-recommended features from 903 participants in the LM-ACS cohort and 64 participants in the MIMIC database.

[iScience] Diverse Intracellular Trafficking of Insulin Analogs by Machine Learning-based Colocalization and Diffusion Analysis

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

Insulin signaling is vital for cellular homeostasis, with dysregulation leading to severe metabolic disorders, particularly diabetes. While insulin analogs are crucial in type-1 diabetes treatment, identifying potential variations in intracellular trafficking and sorting from endogenous insulin is challenging. Current methods rely on static imaging and bulk receptor assays in non-physiological conditions, which disrupts native signaling and masks temporal trafficking dynamics. Here, we directly recorded and compared the intracellular trafficking of ATTO655-labeled recombinant human insulin (HI655) and rapid-acting analog insulin aspart (IAsp655) in live cells.

[Applied Physics Letters Current Issue] Rigid-flexible free-standing multichannel carbon nanofiber-silicon composite anodes due to PS-induced channel ordering

Applied Physics Letters Current Issue — Wed, 07 Jan 2026 00:00:00 GMT

Silicon (Si)-carbon composite has been regarded as one of the most promising anodes for next-generation lithium-ion batteries (LIBs). However, low mechanical strength of carbon matrix is incapable of maintaining structural stability and electron/ion conductivity of Si anodes. Herein, we employ electrospinning-carbonization to construct free-standing Si@carbon nanofibers with internal ordered channels, uniformly distributed Si nanoparticles, and extraordinary elastic modulus (0.22 GPa) by introducing polystyrene as an oriented filler. The free-standing and rigid-flexible ordered multichannel carbon nanofibers (OM-CNFs) can absorb the volume variation of Si, effectively enhancing the mechanical strength and chemical stability of electrodes. The Si nanoparticles uniformly embedded into the highly conductive OM-CNFs matrix establish a bicontinuous structure and increase the contact area between Si and CNFs, thus boosting the rate capability. Consequently, the Si@OM-CNFs anode delivers an excellent reversible capacity of 939.9 mA h g⁻¹ even at 5 A g⁻¹ after 300 cycles. The assembled full-cell with a prelithiated Si@OM-CNFs anode and LiFePO₄ cathode delivers a high energy density of 341 Wh kg⁻¹. This work provides insights into the design of high mechanical strength Si/C composite anodes for high-performance LIBs.

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

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

Publication date: 20 February 2026

Source: Journal of Energy Storage, Volume 147

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

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

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

Publication date: 20 February 2026

Source: Journal of Energy Storage, Volume 147

Author(s): Hu Wang, Shala Yang, Pengfei Pang, Jiangchao Chen, Yongbo Yan, Mingjie Liao, Dazhi Pang, Zheqi Zhang, Yunyun Zhao, Wenping Liu, Huarui Xu, Guisheng Zhu, Kunpeng Jiang

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

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

Publication date: 20 February 2026

Source: Journal of Energy Storage, Volume 147

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

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

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

Publication date: 20 February 2026

Source: Journal of Energy Storage, Volume 147

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

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

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

Publication date: 20 February 2026

Source: Journal of Energy Storage, Volume 147

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

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

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

Publication date: October 2026

Source: Computational Materials Science, Volume 264

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

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

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

Author(s): Đorđe Dangić

Thermal transport in highly anharmonic, amorphous, or alloyed materials often deviates from the predictions of conventional phonon-based models. First-principles approaches have introduced a coherent contribution to account for these deviations and to explain ultralow lattice thermal conductivity, b…

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

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

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

Advanced Energy Materials, EarlyView.

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

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

arXiv:2601.00855v1 Announce Type: new +Abstract: Physical properties and functionalities of materials are dictated by global crystal structures as well as local defects. To establish a structure-property relationship, not only the crystallographic symmetry but also quantitative knowledge about defects are required. Here we present a hybrid Machine Learning framework that integrates a physically-constrained variational-autoencoder (pcVAE) with different Bayesian Optimization (BO) methods to systematically accelerate and improve crystal structure refinement with resolution of defects. We chose the pyrochlore structured Ho2Ti2O7 as a model system and employed the GSAS2 package for benchmarking crystallographic parameters from Rietveld refinement. However, the function space of these material systems is highly nonlinear, which limits optimizers like traditional Rietveld refinement, into trapping at local minima. Also, these naive methods don't provide an extensive learning about the overall function space, which is essential for large space, large time consuming explorations to identify various potential regions of interest. Thus, we present the approach of exploring the high Dimensional structure parameters of defect sensitive systems via pretrained pcVAE assisted BO and Sparse Axis Aligned BO. The pcVAE projects high-Dimensional diffraction data consisting of thousands of independently measured diffraction orders into a lowD latent space while enforcing scaling invariance and physical relevance. Then via BO methods, we aim to minimize the L2 norm based chisq errors in the real and latent spaces separately between experimental and simulated diffraction patterns, thereby steering the refinement towards potential optimum crystal structure parameters. We investigated and compared the results among different pcVAE assisted BO, non pcVAE assisted BO, and Rietveld refinement.

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

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

arXiv:2601.00886v1 Announce Type: new +Abstract: Generative models hold great promise for accelerating materials discovery, but their evaluation often overlooks the chemical validity and stability requirements crucial to real-world applications. Density Functional Theory (DFT) simulations are the gold standard for evaluating such properties but are computationally intensive and inaccessible to non-experts. We propose a chemically grounded, user-friendly evaluation framework that integrates DFT-based stability analysis with commonly used machine learning (ML) metrics. Through systematic experiments using both perturbative and generative methods, we demonstrate that conventional ML metrics can misrepresent chemical feasibility. To address this, we propose new insights on robust metrics and highlight the importance of simulation-informed evaluation for developing reliable generative models in materials science.

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

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

arXiv:2601.01010v1 Announce Type: new +Abstract: We provide an overview of high dimensional dynamical systems driven by random matrices, focusing on applications to simple models of learning and generalization in machine learning theory. Using both cavity method arguments and path integrals, we review how the behavior of a coupled infinite dimensional system can be characterized as a stochastic process for each single site of the system. We provide a pedagogical treatment of dynamical mean field theory (DMFT), a framework that can be flexibly applied to these settings. The DMFT single site stochastic process is fully characterized by a set of (two-time) correlation and response functions. For linear time-invariant systems, we illustrate connections between random matrix resolvents and the DMFT response. We demonstrate applications of these ideas to machine learning models such as gradient flow, stochastic gradient descent on random feature models and deep linear networks in the feature learning regime trained on random data. We demonstrate how bias and variance decompositions (analysis of ensembling/bagging etc) can be computed by averaging over subsets of the DMFT noise variables. From our formalism we also investigate how linear systems driven with random non-Hermitian matrices (such as random feature models) can exhibit non-monotonic loss curves with training time, while Hermitian matrices with the matching spectra do not, highlighting a different mechanism for non-monotonicity than small eigenvalues causing instability to label noise. Lastly, we provide asymptotic descriptions of the training and test loss dynamics for randomly initialized deep linear neural networks trained in the feature learning regime with high-dimensional random data. In this case, the time translation invariance structure is lost and the hidden layer weights are characterized as spiked random matrices.

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

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

arXiv:2601.01326v1 Announce Type: new +Abstract: Ruthenium-based B2 intermetallics are promising for refractory superalloys but are limited by the trade-off between high thermodynamic stability and elastic precipitation strain. We present a physics-guided machine learning framework integrating high-throughput Density Functional Theory (DFT), Random Forest screening, and Symbolic Regression to navigate this design space. This approach resolves the paradox where stoichiometric compounds like RuHf fail to achieve theoretical solvus temperatures. By deriving a closed-form physical law, we quantify the strain penalty: a 1% lattice misfit reduces the solvus temperature by approximately 200 degrees C. This finding confirms that maximizing thermodynamic driving force alone is insufficient. We demonstrate that multi-component alloying is structurally necessary, identifying ternary additions such as Al and Ti as essential lattice-tuning agents that zero out the elastic penalty. This framework establishes a rigorous, constraint-based protocol for alloy design, enabling the precise engineering of zero-misfit, high-stability microstructures.

[cond-mat updates on arXiv.org] Common sublattice-pure van Hove singularities in the kagome superconductors $\textit{A}$V$_{3}$Sb$_{5}$ ($\textit{A}$ = K, Rb, Cs)

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

arXiv:2601.01428v1 Announce Type: new +Abstract: Kagome materials offer a versatile platform for exploring correlated and topological quantum states, where van Hove singularities (VHSs) play a pivotal role in driving electronic instabilities, exhibiting distinct behaviors depending on electron filling and interaction settings. In the recently discovered kagome superconductors $\textit{A}$V$_{3}$Sb$_{5}$ ($\textit{A}$ = K, Rb, Cs), unconventional charge density wave order, superconductivity, and electronic chirality emerge, yet the nature of VHSs near the Fermi level ($\textit{E}$$_{F}$) and their connection to these exotic orders remain elusive. Here, using high-resolution polarization-dependent angle-resolved photoemission spectroscopy, we uncover a universal electronic structure across $\textit{A}$V$_{3}$Sb$_{5}$ that is distinct from density-functional theory predictions that show noticeable discrepancies. We identify multiple common sublattice-pure VHSs near $\textit{E}$$_{F}$, arising from strong V-$\textit{d}$/Sb-$\textit{p}$ hybridization, which significantly promote bond-order fluctuations and likely drive the observed charge density wave order. These findings provide direct spectroscopic evidence for hybridization-driven VHS formation in kagome metals and establish a unified framework for understanding the intertwined electronic instabilities in $\textit{A}$V$_{3}$Sb$_{5}$.

[cond-mat updates on arXiv.org] A Universal Model for the Resting Potential in Nanofluidic Systems

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

arXiv:2601.01536v1 Announce Type: new +Abstract: The resting voltage, $V$, which is the potential drop required to nullify the electrical current ($i=0$), is a key characteristic of water desalination and energy harvesting systems that utilize macroscopically large nanoporous membranes, as well as for physiological ion channels subjected to asymmetric salt concentrations. To date, existing analytical expressions for $V_{i=0}$ have been limited to simple scenarios. In this work, we derive a universal, self-consistent theoretical model, devoid of unnecessary oversimplifying assumptions, that unifies all previous models within a single framework. This new model, verified by non-approximated numerical simulations, predicts the behavior of $V_{i=0}$ for arbitrary concentration gradients and for arbitrary diffusion coefficients and ionic valences. We show how the interplay between diffusion coefficients and ionic valencies significantly varies the system response and why it is essential to account for all system parameters. Ultimately, this model can be used to improve experimental interpretation of ion transport measurements.

[cond-mat updates on arXiv.org] Anharmonic lattice dynamics study of phonon transport in layered and molecular-crystal indium iodides

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

arXiv:2601.01766v1 Announce Type: new +Abstract: Indium iodides, which adopt layered or molecular-crystal-like arrangements depending on composition, are expected to exhibit low lattice thermal conductivity because of their heavy constituent atoms and weak In-I bonding. In this work, we employed first-principles anharmonic lattice dynamics calculations to systematically investigate phonon transport in indium iodides from particle- and wave-like perspectives. The calculated lattice thermal conductivities of both materials remained below 1 W/m-K over a broad temperature range. Notably, the influence of wave-like phonon transport differed by composition: in InI3, the wave-like contribution became comparable to the particle-like Peierls contribution, whereas it remained negligible in InI. We also investigated the thermal transport properties of the experimentally reported high-pressure phase of InI3. Motivated by experimental indications of stacking faults and partial disorder in indium site occupancy within the rhombohedral phase, we constructed several ordered structural models with different stacking sequences. These stacking sequences exhibited no significant energetic preference and had similar lattice thermal conductivities, suggesting that in-plane thermal transport is largely governed by the vibrational properties of the In2I6 layers themselves rather than by the specific stacking sequence. These findings provide insight into phonon transport in layered and molecular-crystal systems with structural complexity and contribute to a broader understanding of thermal transport mechanisms in layered and molecular-crystal-like materials.

[cond-mat updates on arXiv.org] Spin-correlation Driven Ferroelectric Quantum Criticality in a Perovskite Quantum Spin-liquid System, Ba3CuSb2O9

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

arXiv:2601.01906v1 Announce Type: new +Abstract: Here we have experimentally demonstrated spin-correlation-driven ferroelectric quantum criticality in a prototype quantum spin-liquid system, Ba3CuSb2O9, a quantum phenomenon rarely observed. The dielectric constant follows a clear T2 scaling, showing that the material behaves as a quantum paraelectric without developing ferroelectric order. Magnetically, the system avoids long-range order down to 1.8 K and instead displays a T3/2 dependence in its inverse susceptibility, a hallmark of antiferromagnetic quantum critical fluctuations. Together with known spin-orbital-lattice entanglement in this compound, these signatures point to a strong interplay between spin dynamics and the polar lattice. Our results place this perovskite spin-liquid family at the forefront of this domain and suggest the flexibility of this family in a suitable environment by tuning chemical/ external pressure.

[cond-mat updates on arXiv.org] Score-based diffusion models for accurate crystal-structure inpainting and reconstruction of hydrogen positions

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

arXiv:2601.01959v1 Announce Type: new +Abstract: Generative AI models, such as score-based diffusion models, have recently advanced the field of computational materials science by enabling the generation of new materials with desired properties. In addition, these models could also be leveraged to reconstruct crystal structures for which partial information is available. One relevant example is the reliable determination of atomic positions occupied by hydrogen atoms in hydrogen-containing crystalline materials. While crucial to the analysis and prediction of many materials properties, the identification of hydrogen positions can however be difficult and expensive, as it is challenging in X-ray scattering experiments and often requires dedicated neutron scattering measurements. As a consequence, inorganic crystallographic databases frequently report lattice structures where hydrogen atoms have been either omitted or inserted with heuristics or by chemical intuition. Here, we combine diffusion models from the field of materials science with techniques originally developed in computer vision for image inpainting. We present how this knowledge transfer across domains enables a much faster and more accurate completion of host structures, compared to unconditioned diffusion models or previous approaches solely based on DFT. Overall, our approach exceeds a success rate of 97% in terms of finding a structural match or predicting a more stable configuration than the initial reference, when starting both from structures that were already relaxed with DFT, or directly from the experimentally determined host structures.

[cond-mat updates on arXiv.org] New RVE concept and FFT methods in micromechanics of composites subjected to body force with compact support

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

arXiv:2601.00822v1 Announce Type: cross +Abstract: We consider static linear elastic composite materials (CMs) with periodic structure. The core of the proposed methodology is the generation of a novel dataset using specially designed body force fields with compact support (BFCS), enabling a new RVE concept that reduces the infinite periodic medium to a finite domain without boundary artifacts. This functionally reduced RVE is used for translated averaging of direct numerical simulations (DNS) results, efficiently computed via a newly developed FFT-based solver for BFCS loading. The resulting dataset captures localized field responses and is used to train machine learning (ML) and neural networks (NN) models to learn effective nonlocal surrogate operators. These operators accurately predict macroscopic responses while reflecting microstructural features and nonlocal interactions. By accounting for field localization while simultaneously eliminating influences from finite sample size and boundary effects, it provides a physically grounded and data-driven framework for constructing accurate surrogate models for the homogenization of complex materials.

[cond-mat updates on arXiv.org] AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials

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

arXiv:2601.01185v1 Announce Type: cross +Abstract: Machine-learning potentials (MLIPs) have been a breakthrough for computational physics in bringing the accuracy of quantum mechanics to atomistic modeling. To achieve near-quantum accuracy, it is necessary that neighborhoods contained in the training set are rather close to the ones encountered during a simulation. Yet, constructing a single training set that works well for all applications is, and likely will remain, infeasible, so, one strategy is to supplement training protocols for MLIPs with additional learning methods, such as active learning, or fine-tuning. This strategy, however, yields very complex training protocols that are difficult to implement efficiently, and cumbersome to interpret, analyze, and reproduce. + To address the above difficulties, we propose AutoPot, a software for automating the construction and archiving of MLIPs. AutoPot is based on BlackDynamite, a software operating parametric tasks, e.g., running simulations, or single-point ab initio calculations, in a highly-parallelized fashion, and Motoko, an event-based workflow manager for orchestrating interactions between the tasks. The initial version of AutoPot supports selection of training configurations from large training candidate sets, and on-the-fly selection from molecular dynamics simulations, using Moment Tensor Potentials as implemented in MLIP-2, and single-point calculations of the selected training configurations using VASP. Another strength of AutoPot is its flexibility: BlackDynamite tasks and orchestrators are Python functions to which own existing code can be easily added and manipulated without writing complex parsers. Therefore, it will be straightforward to add other MLIP and ab initio codes, and manipulate the Motoko orchestrators to implement other training protocols.

[cond-mat updates on arXiv.org] Quantum Extreme Reservoir Computing for Phase Classification of Polymer Alloy Microstructures

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

arXiv:2601.02150v1 Announce Type: cross +Abstract: Quantum machine learning (QML) is expected to offer new opportunities to process high-dimensional data efficiently by exploiting the exponentially large state space of quantum systems. In this work, we apply quantum extreme reservoir computing (QERC) to the classification of microstructure images of polymer alloys generated using self-consistent field theory (SCFT). While previous QML efforts have primarily focused on benchmark datasets such as MNIST, our work demonstrates the applicability of QERC to engineering data with direct materials relevance. Through numerical experiments, we examine the influence of key computational parameters-including the number of qubits, sampling cost (the number of measurement shots), and reservoir configuration-on classification performance. The resulting phase classifications are depicted as phase diagrams that illustrate the phase transitions in polymer morphology, establishing an understandable connection between quantum model outputs and material behavior. These results illustrate QERC performance on realistic materials datasets and suggest practical guidelines for quantum encoder design and model generalization. This work establishes a foundation for integrating quantum learning techniques into materials informatics.

[cond-mat updates on arXiv.org] Projected branes as platforms for crystalline, superconducting, and higher-order topological phases

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

arXiv:2507.23783v2 Announce Type: replace +Abstract: Projected branes are constituted by only a small subset of sites of a higher-dimensional crystal, otherwise placed on a hyperplane oriented at an irrational or a rational slope therein, for which the effective Hamiltonian is constructed by systematically integrating out the sites of the parent lattice that fall outside such branes [Commun. Phys. 5, 230 (2022)]. Specifically, when such a brane is constructed from a square lattice, it gives rise to an aperiodic Fibonacci quasi-crystal or its rational approximant in one dimension. In this work, starting from square lattice-based models for topological crystalline insulators, protected by the discrete four-fold rotational ($C_4$) symmetry, we show that the resulting one-dimensional projected topological branes encode all the salient signatures of such phases in terms of robust endpoint zero-energy modes, quantized local topological markers, and mid-gap modes bound to dislocation lattice defects, despite such linear branes being devoid of the $C_4$ symmetry of the original lattice. Furthermore, we show that such branes can also feature all the hallmarks of two-dimensional strong and weak topological superconductors through Majorana zero-energy bound states residing near their endpoints and at the core of dislocation lattice defects, besides possessing suitable quantized local topological markers. Finally, we showcase a successful incarnation of a square lattice-based second-order topological insulator with the characteristic corner-localized zero modes in its geometric descendant one-dimensional quasi-crystalline or crystalline branes that feature a quantized localizer index and endpoint zero-energy modes only when one of its end points passes through a corner of the parent crystal. Possible designer quantum and meta material-based platforms to experimentally harness our theoretically proposed topological branes are discussed.

[cond-mat updates on arXiv.org] Efficient Identification of Critical Transitions via Flow Matching: A Scalable Generative Approach for Many-Body Systems

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

arXiv:2508.15318v4 Announce Type: replace +Abstract: We propose a machine learning framework based on Flow Matching (FM) to identify critical properties in many-body systems efficiently. Using the 2D XY model as a benchmark, we demonstrate that a single network, trained only on configurations from a small ($32\times 32$) lattice at sparse temperature points, effectively generalizes across both temperature and system size. This dual generalization enables two primary applications for large-scale computational physics: (i) a rapid "train-small, predict-large" strategy to locate phase transition points for significantly larger systems ($128\times 128$) without retraining, facilitating efficient finite-size scaling analysis; and (ii) the fast generation of high-fidelity, decorrelated initial spin configurations for large-scale Monte Carlo simulations, providing a robust starting point that bypasses the long thermalization times of traditional samplers. These capabilities arise from the combination of the Flow Matching framework, which learns stable probability-flow vector fields, and the inductive biases of the U-Net architecture that capture scale-invariant local correlations. Our approach offers a scalable and efficient tool for exploring the thermodynamic limit, serving as both a rapid explorer for phase boundaries and a high-performance initializer for high-precision studies.

[cond-mat updates on arXiv.org] Graph atomic cluster expansion for foundational machine learning interatomic potentials

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

arXiv:2508.17936v2 Announce Type: replace +Abstract: Foundational machine learning interatomic potentials that can accurately and efficiently model a vast range of materials are critical for accelerating atomistic discovery. We introduce universal potentials based on the graph atomic cluster expansion (GRACE) framework, trained on several of the largest available materials datasets. Through comprehensive benchmarks, we demonstrate that the GRACE models establish a new Pareto front for accuracy versus efficiency among foundational interatomic potentials. We further showcase their exceptional versatility by adapting them to specialized tasks and simpler architectures via fine-tuning and knowledge distillation, achieving high accuracy while preventing catastrophic forgetting. This work establishes GRACE as a robust and adaptable foundation for the next generation of atomistic modeling, enabling high-fidelity simulations across the periodic table.

[cond-mat updates on arXiv.org] DeFecT-FF: Accelerated Modeling of Defects in Cd-Zn--Te-Se-S Compounds Combining High-Throughput DFT and Machine Learning Force Fields

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

arXiv:2510.23514v2 Announce Type: replace +Abstract: We developed DeFecT-FF, a framework for predicting the energies and ground-state configurations of native point defects, extrinsic dopants, impurities, and defect complexes in zincblende-phase Cd/Zn-Te/Se/S compounds relevant to CdTe-based solar cells. The framework combines high-throughput DFT data with crystal graph-based machine learning force fields (MLFFs) trained to reproduce DFT energies and forces. Alloying at Cd or Te sites offers a route to tune the electronic and defect properties of CdTe absorbers for improved solar efficiency. Given the vast number of possible defect types, charge states, and symmetry-breaking configurations, traditional DFT approaches are computationally prohibitive. Our dataset includes GGA-PBE and HSE06-optimized structures for bulk, alloyed, interface, and grain-boundary systems. Using active learning, we expanded the dataset and trained MLFFs to accurately predict energies across charge states. The model enabled rapid screening and discovery of new low-energy defect configurations, validated through HSE06 calculations with spin-orbit coupling. The DeFecT-FF framework is publicly available as a nanoHUB tool, allowing users to upload crystallographic files, automatically generate defects, and compute defect formation energies versus Fermi level and chemical potentials, greatly reducing the need for expensive DFT simulations.

[cond-mat updates on arXiv.org] Time-Temperature-Transformation (TTT) Diagrams to rationalize the nucleation and quenchability of metastable $\alpha$-Li$_3$PS$_4$

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

arXiv:2512.05841v2 Announce Type: replace +Abstract: $\alpha$-Li$_3$PS$_4$ is a promising solid-state electrolyte with the highest ionic conductivity among its polymorphs. However, its formation presents a thermodynamic paradox: the $\alpha$-phase is the equilibrium phase at high temperature and transforms to the stable $\gamma$-Li$_3$PS$_4$ polymorph when cooled to room temperature; however, $\alpha$-Li$_3$PS$_4$ can be synthesized and quenched in a metastable state via rapid heating at relatively low temperatures. The origin of this synthesizability and anomalous stability has remained elusive. Here, we resolve this paradox by establishing a comprehensive time-temperature-transformation (TTT) diagram, constructed from a computational temperature-size phase diagram and experimental high-time-resolution isothermal measurements. Our density functional theory calculations reveal that at the nanoscale, the $\alpha$-phase is stabilized by its low surface energy, which drastically lowers the nucleation barrier across a wide temperature range. This size-dependent stabilization is directly visualized using in-situ synchrotron X-ray diffraction and electron microscopy, capturing the rapid nucleation of nano-sized $\alpha$-phase and its subsequent slow transformation. This work presents a generalizable framework that integrates thermodynamic and kinetic factors for understanding nucleation and phase transformation mechanisms, providing a rational strategy for the targeted synthesis of functional metastable materials.

[cond-mat updates on arXiv.org] Linear magnetoresistance of two-dimensional massless Dirac fermions in the quantum limit

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

arXiv:2512.13475v2 Announce Type: replace +Abstract: Linear magnetoresistance is a hallmark of 3D Weyl metals in the quantum limit. Recently, a pronounced linear magnetoresistance has also been observed in 2D graphene [Xin et al., Nature 616, 270 (2023)]. However, a comprehensive theoretical understanding remains elusive. By employing the self-consistent Born approximation, we derive the analytical expressions for the magnetoresistivity of 2D massless Dirac fermions in the quantum limit. Notably, our result recovers the minimum conductivity in the clean limit and reveals a linear dependence of resistivity on the magnetic field for Gaussian impurity potentials, in quantitative agreement with experiments. These findings shed light on the magnetoresistance behavior of 2D Dirac fermions under ultra-high magnetic fields.

[cond-mat updates on arXiv.org] Insights Into Radiation Damage in YBa$_2$Cu$_3$O$_{7-\delta}$ From Machine-Learned Interatomic Potentials

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

arXiv:2512.24430v2 Announce Type: replace +Abstract: Accurate prediction of radiation damage in YBa$_2$Cu$3$O${7-\delta}$ (YBCO) is essential for assessing the performance of high-temperature superconducting (HTS) tapes in compact fusion reactors. Existing empirical interatomic potentials have been used to model radiation damage in stoichiometric YBCO, but fail to describe oxygen-deficient compositions, which are ubiquitous in industrial Rare-Earth Barium Copper Oxide conductors and strongly influence superconducting properties. In this work, we demonstrate that modern machine-learned interatomic potentials enable predictive modelling of radiation damage in YBCO across a wide range of oxygen stoichiometries, with higher fidelity than previous empirical models. We employ two recently developed approaches: an Atomic Cluster Expansion (ACE) potential and a tabulated Gaussian Approximation Potential (tabGAP). Both models accurately reproduce Density Functional Theory (DFT) energies, forces, and threshold displacement energy distributions, providing a reliable description of atomic-scale collision processes. Molecular dynamics simulations of 5 keV cascades predict enhanced peak defect production and recombination relative to a widely used empirical potential, indicating different cascade evolution. By explicitly varying oxygen deficiency, we show that total defect production depends only weakly on stoichiometry, offering insight into the robustness of radiation damage processes in oxygen-deficient YBCO. Finally, fusion-relevant 300 keV cascade simulations reveal amorphous regions with dimensions comparable to the superconducting coherence length, consistent with electron microscopy observations of neutron-irradiated HTS tapes. These results establish machine-learned interatomic potentials as efficient and predictive tools for investigating radiation damage in YBCO across relevant compositions and irradiation conditions.

[cond-mat updates on arXiv.org] Controllable diatomic molecular quantum thermodynamic machines

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

arXiv:2504.03131v2 Announce Type: replace-cross +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

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

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

arXiv:2508.21012v5 Announce Type: cross +Abstract: The spontaneous emergence of coherent structures from chaotic backgrounds is a hallmark of active biological swarms. We investigate this self-organization by simulating an ensemble of polar chiral active agents that couple locally via a Kuramoto interaction. We demonstrate that the system's transition from chaos to active turbulence is characterized by quantized loop phase currents and coherent clustering, and that this transition is strictly governed by a kinetic Turing instability. By deriving the continuum kinetic theory for the model, we identify that the competition between local phase-locking and active agent motility selects a critical structural wavenumber. The instability then drives the system into a state of developed, active turbulence that exhibits stable, robust power-laws in spectral density, suggestive of universality and consistent with observations from a broad range of turbulent phenomena. Our results bridge the gap between discrete chimera states and continuous fluid turbulence, suggesting that the statistical scaling laws of active turbulence can arise from fundamental kinetic instability criteria.

[cond-mat updates on arXiv.org] New RVE concept in thermoelasticity of periodic composites subjected to compact support loading

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

arXiv:2601.00018v1 Announce Type: cross +Abstract: This paper introduces an advanced Computational Analytical Micromechanics (CAM) framework for linear thermoelastic composites (CMs) with periodic microstructures. The approach is based on an exact new Additive General Integral Equation (AGIE), formulated for compactly supported loading conditions, such as body forces and localized thermal effects (for example laser heating). In addition, new general integral equations (GIEs) are established for arbitrary mechanical and thermal loading. A unified iterative scheme is developed for solving the static AGIEs, where the compact support of loading serves as a new fundamental training parameter. At the core of the methodology lies a generalized Representative Volume Element (RVE) concept that extends Hill classical definition of the RVE. Unlike conventional RVEs, this generalized RVE is not fixed geometrically but emerges naturally from the characteristic scale of localized loading, thereby reducing the analysis of an infinite periodic medium to a finite, data-driven domain. This formulation automatically filters out nonrepresentative subsets of effective parameters while eliminating boundary effects, edge artifacts, and finite-size sample dependencies. Furthermore, the AGIE-based CAM framework integrates seamlessly with machine learning (ML) and neural network (NN) architectures, supporting the development of accurate, physics-informed surrogate nonlocal operators.

[cond-mat updates on arXiv.org] Additive general integral equations in thermoelastic micromechanics of composites

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

arXiv:2601.00019v1 Announce Type: cross +Abstract: This work presents an enhanced Computational Analytical Micromechanics (CAM) framework for the analysis of linear thermoelastic composite materials (CMs) with random microstructure. The proposed approach is grounded in an exact Additive General Integral Equation (AGIE), specifically formulated for compactly supported loading, including both body forces and localized thermal changes (such as those from laser heating). New general integral equations (GIEs) for arbitrary mechanical and thermal loading are proposed. A unified iterative solution strategy is developed for the static AGIE, applicable to CMs with both perfectly and imperfectly bonded interfaces, where the compact support of loading is introduced as a new fundamental training parameter. Central to this methodology is a generalized Representative Volume Element (RVE) concept, which extends Hill classical definition. The resulting RVE is not predefined geometrically, but rather emerges from the characteristic scale of the localized loading, effectively reducing the analysis of an infinite, randomly heterogeneous medium to a finite, data-driven domain. This generalized RVE approach enables automatic exclusion of unrepresentative subsets of effective parameters, while inherently eliminating boundary effects, edge artifacts, and finite size limitations. Moreover, the AGIE-based CAM framework is naturally compatible with machine learning (ML) and neural network (NN) architectures, facilitating the construction of accurate and physically informed surrogate nonlocal operators.

[cond-mat updates on arXiv.org] Accurate Screening of Functional Materials with Machine-Learning Potential and Transfer-Learned Regressions: Heusler Alloy Benchmark

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

arXiv:2508.20556v2 Announce Type: replace +Abstract: A machine learning-accelerated high-throughput (HTP) workflow for the discovery of magnetic materials is presented. As a test case, we screened quaternary and all-$d$ Heusler compounds for stable compounds with large magnetocrystalline anisotropy energy ($E_{\mathrm{aniso}}$). Structure optimization and evaluation of formation energy and distance to hull convex were performed using the eSEN-30M-OAM interatomic potential, while local magnetic moments, phonon stability, magnetic stability, and $E_{\mathrm{aniso}}$ were predicted by eSEM models trained on our DxMag Heusler database. A frozen transfer learning strategy was employed to improve accuracy. Candidate compounds identified by the ML-HTP workflow were validated with density functional theory, confirming high predictive precision. We also benchmark the performance of different uMLIPs, discuss the fidelity of local magnetic moment prediction, and demonstrate generalization to unseen elements via transfer learning from a universal interatomic potential.

[cond-mat updates on arXiv.org] Modulation of structural short-range order due to chemical patterning in multi-component amorphous interfacial complexions

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

arXiv:2509.06166v2 Announce Type: replace +Abstract: Amorphous interfacial complexions have been shown to restrict grain growth and improve damage tolerance in nanocrystalline alloys, with increased chemical complexity stabilizing the complexions themselves. Here, we investigate local chemical composition and structural short-range order in Cu-rich, multi-component nanocrystalline alloys to understand how dopants self-organize within these amorphous complexions and how local structure is altered. High resolution scanning transmission electron microscopy and elemental analysis are used to study both grain boundaries and interphase boundaries, with chemical partitioning observed for both. Notably, the amorphous-crystalline transition region is observed to be enriched in certain dopant species and depleted of others as compared to the interior of the amorphous complexions. This chemical patterning can be explained in terms of the elemental preference for ordered or disordered grain boundary environments. As only a qualitative measure of structural short-range order can be obtained with nanobeam electron diffraction for these specimens, atomistic simulations with a custom-built machine learning interatomic potential are then used to probe how dopant patterning affects local structural state. Increased grain boundary chemical complexity is found to result in a more disordered complexion structure, with segregation to the amorphous-crystalline transition regions driving changes in local structure that are sensitive to dopant ratios. As a whole, the intimate connection between local chemistry and order in amorphous interfacial complexions is demonstrated, opening the door for microstructural engineering within the amorphous complexions themselves.

[cond-mat updates on arXiv.org] Temperature and Pressure Dependent Vibrational Properties of Pristine and Doped Vacancy-Ordered Double Perovskite

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

arXiv:2512.21810v2 Announce Type: replace +Abstract: Understanding lattice dynamics and structural transitions in vacancy-ordered double perovskites is crucial for developing lead-free optoelectronic materials, yet the role of dopants in modulatingthese properties remains poorly understood. We investigate the vibrational and optical properties of pristine and Antimony(Sb)-doped Cs$_2$TiCl$_6$ vacancy-ordered double perovskite through temperature-dependent Raman spectroscopy (4-273 K), high-pressure studies (0- \~30 GPa), ambient powder XRD, and photoluminescence measurements. Sb doping improves phase purity, reducing impurity-related Raman modes present in pristine samples. Most notably, Sb-doped samples exhibit an anomalous Raman mode M$_1$ appearing exclusively below 100 K at 314-319 cm$^{-1}$, accompanied by changes in the temperature coefficient $\chi$ and anharmonic constant $A$ across this threshold. This behavior is absent in pristine Cs$_2$TiCl$_6$. While these observations suggest possible structural changes at low temperature, the origin of the M$_1$ mode remains unclear and may arise from disorder-activated vibrations, symmetry breaking, or dopant-induced local distortions. Low-temperature structural characterization is needed to confirm the nature of this transition. Photoluminescence shows broad self-trapped exciton emission at 448 nm with broader FWHM in Sb-doped samples (164.73 nm) compared to Bi-doped samples (138.2 nm), consistent with enhanced structural disorder. High-pressure Raman measurements reveal continuous mode hardening to 30 GPa with no phase transitions. These results demonstrate that Sb doping modulates the vibrational properties of Cs$_2$TiCl$_6$, though further investigation is required to establish the underlying mechanisms.

[cond-mat updates on arXiv.org] Support Vector Machine Kernels as Quantum Propagators

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

arXiv:2502.11153v3 Announce Type: replace-cross +Abstract: Selecting optimal kernels for regression in physical systems remains a challenge, often relying on trial-and-error with standard functions. In this work, we establish a mathematical correspondence between support vector machine kernels and quantum propagators, demonstrating that kernel efficacy is determined by its spectral alignment with the system's Green's function. Based on this isomorphism, we propose a unified, physics-informed framework for kernel selection and design. For systems with known propagator forms, we derive analytical selection rules that map standard kernels to physical operators. For complex systems where the Green's function is analytically intractable, we introduce a constructive numerical method using the Kernel Polynomial Method with Jackson smoothing to generate custom, physics-aligned kernels. Numerical experiments spanning electrical conductivity, electronic band structure, anharmonic oscillators, and photonic crystals demonstrate that this framework consistently performs well as long as there is an alignment with a Green's function.

[ChemRxiv] Ternary Transition Metal Oxides for Electrochemical Energy Storage: Synthesis, Advantages, Design Strategies, and Future Prospects

ChemRxiv — Mon, 05 Jan 2026 00:00:00 GMT

Ternary transition metal oxides (TTMOs) have emerged as a new class of electrode materials for high-performance energy storage systems, particularly supercapacitors (SCs) and hybrid battery-capacitor devices. This comprehensive review aims to comprehensively survey recent advances in the design, synthesis, and analysis of TTMOs-based nanostructures for supercapacitor (SC) electrodes. It begins by outlining the key concepts related to charge storage mechanisms in SC electrodes, electric double-layer capacitance (EDL), pseudocapacitive (PC), and battery-type (BT) behavior, followed by a clarification of device configurations, including symmetric SC (SSC), asymmetric SC (ASC), and hybrid SC (HSC) devices. This review then examines the fabrication strategies for TTMOs, emphasizing the impact of synthetic approaches on material morphology, crystallinity, and electrochemical performance. Special attention is given to the structure-property relationships that govern ion transport and charge storage dynamics in these materials. The influence of morphological features, including dimensionality, porosity, and hierarchical architecture, on electrochemical behavior is critically analyzed. A comparative evaluation of electrochemical matrices across various TTMO electrodes is presented, highlighting key performance and challenges. Ultimately, the review highlights emerging trends, current limitations, and future research directions that are poised to accelerate the development of next-generation TTMO materials for advanced energy storage technologies.

[ChemRxiv] Tunable Gelation and Viscoelasticity of Lung-Tissue–Mimetic Sealant: Linking Shear History to In Situ Mechanical Performance through Physics-Informed Machine Learning

ChemRxiv — Mon, 05 Jan 2026 00:00:00 GMT

Hydrogel-based lung sealants are optimized largely based on formulation and post-cure mechanics, even though deformation history during delivery and placement plays a critical, yet under-characterized, role in determining in situ performance. This disconnect is particularly consequential on the application of lung sealants, where excessive stiffness promotes delamination while insufficient stiffness compromises airtight sealing. Here, we establish a quantitative process–structure–property framework for a gelatin–tannic acid–transglutaminase lung-mimetic sealant by integrating time-resolved rheology with physics-informed machine learning. + +Using small-amplitude oscillatory shear and steady torsional flow at 37 °C, we show that gelation follows a reproducible kinetic clock (storage modulus, G′– loss modulus, G″, crossover at ~100 s; modulus plateau by ~600 s), while the attainable viscoelastic state is strongly governed by deformation history. Increasing oscillatory strain amplitude from 0.1 % to 50 % suppresses network maturation, reducing the post-gelation storage modulus, G′, from ~10⁶ to ~10⁴ Pa, whereas sustained steady shear (0.01–1 s⁻¹) decreases magnitude of complex viscosity by 2–3 orders of magnitude and permanently downshifts elastic moduli, G′–, from ≈3.5×10³ to ≈4×10² Pa. These modulus ranges span the effective compliance of lung parenchyma under physiological tidal strain, delineating mechanical regimes associated with airtight sealing, strain accommodation, or premature interfacial failure. Controlled aeration during the processing of the sealant further decreases stiffness by ~30 % without altering gelation kinetics, providing an additional, physically interpretable compliance-tuning mechanism. + +To unify these effects, we introduce a dimensionless Degree of Gelation (DoG), serving as a rheological state variable that collapses oscillatory and steady-shear histories into a single, time-resolved descriptor of network evolution. Machine-learning models trained on experimentally accessible inputs (time, strain amplitude, shear rate, frequency, aeration) accurately predict DoG (R² ≈ 0.9) and, in inverse mode, identify handling conditions required to achieve targeted in situ mechanical states. + +This rheology–machine-learning framework reframes lung sealant development from a static materials optimization problem to a controllable, process-driven design strategy. By quantitatively linking applicator-level parameters to failure-relevant mechanical outcomes—airtightness, compliance, and resistance to delamination—it provides a mechanistic and generalizable foundation for the design of injectable hydrogels, bioadhesives, and tissue-interfacing

[ChemRxiv] Discovery of β-Sheet Peptide Assembly Codes via an Experimentally Validated Predictive Computational Platform

ChemRxiv — Mon, 05 Jan 2026 00:00:00 GMT

Deciphering the sequence codes governing ordered peptide assemblies remains challenging due to the need to explore vast sequence space with atomic resolution. Here, we present an experimentally validated computational framework combining hybrid-resolution molecular dynamics and machine learning for the discovery of β-sheet-rich amyloid-forming peptides. Through exhaustive simulations of all 8,000 tripeptides, we demonstrate that the widely used aggregation propensity (AP) is not effective in predicting β-sheet assembly. We introduce Amyloid-Like Tendency (ALT), a metric enabled by our hybrid-resolution simulations that effectively identifies cross-β architectures. Leveraging this physics-informed dataset, we further fine-tuned the Uni-Mol model to efficiently screen 160,000 tetrapeptides. Experimental validation of 46 candidates confirmed a predictive accuracy of ~85%, yielding 26 novel amyloid-forming peptides, including multiple hydrogelators. Mechanistic analysis reveals that specific sidechain stacking and central amino acid identity, beyond generic hydrophobicity, dictate ordered assembly. This establishes a scalable pipeline for the targeted design of functional peptide materials.

[ChemRxiv] Continued Challenges in High-Throughput Materials Predictions: MatterGen predicts compounds from the training dataset.

ChemRxiv — Mon, 05 Jan 2026 00:00:00 GMT

High-throughput computational tools and generative AI models aim to revolutionise materials discovery by enabling the rapid prediction of novel inorganic compounds. However, these tools face persistent challenges with modelling compounds where multiple elements occupy the same crystallographic site, often leading to misclassification of known disordered phases as new ordered compounds. Recently, Microsoft revealed MatterGen as a tool for predicting new materials. As a proof of concept, MatterGen was used to predict the novel compound TaCr2O6, which was subsequently synthesised in a disordered form as Ta1/3Cr2/3O2. However, detailed crystallographic analysis, presented in this paper, reveals that this is not a novel compound but is identical to the already known compound Ta1/2Cr1/2O2 reported in 1972 and actually included in MatterGen’s training dataset. These findings underscore the necessity of rigorous human verification in AI-assisted materials research, limiting their use for rapid and large-scale prediction of new materials. While generative models hold great promise, their effectiveness is currently limited by unresolved issues with disorder prediction and dataset validation. Improved integration with crystallographic expertise is essential to realise their full potential.

[ChemRxiv] Pressure- and Temperature-Dependent Ionic Transport in Ag₄Zr₃S₈ Nanocrystal Pellets

ChemRxiv — Mon, 05 Jan 2026 00:00:00 GMT

Nanocrystal (NC)–derived solid electrolytes provide access to compositionally complex and metastable ion conductors, yet their measured transport properties are often dominated by extrinsic contact effects. We probe the coupled roles of temperature, uniaxial pressure, pellet microstructure, and electrode material on the electrochemical impedance response of Ag₄Zr₃S₈ NC pellets. Ag₄Zr₃S₈ NCs were synthesized via colloidal routes using distinct sulfur sources and consolidated into pellets with controlled surface chemistry. EIS was performed over 298–393 K and 0.43–8.67 MPa using blocking and non-blocking electrodes. Pressure-dependent Nyquist analysis shows impedance is overwhelmingly dominated by interfacial and constriction resistances, with pressure primarily reducing contact limitations rather than altering intrinsic ion transport. Temperature–pressure heat maps of the high-frequency resistance reveal thermally activated transport strongly modulated by mechanical contact and electrode compatibility. These results establish pressure-resolved impedance spectroscopy as a diagnostic framework for separating intrinsic and extrinsic transport contributions in NC-based solid electrolytes.

[iScience] Mechanistic Evidence for Dibutyl Phthalate as an Environmental Trigger for Inflammatory Bowel Disease

iScience — Mon, 05 Jan 2026 00:00:00 GMT

Dibutyl phthalate (DBP) is a ubiquitous pollutant, but its molecular link to inflammatory bowel disease (IBD) is undefined. We employed an integrative network toxicology framework, combining DBP target databases with IBD patient transcriptomics to address this gap. A computational pipeline using machine learning and molecular docking predicted a core six-gene signature (KYNU, PCK1, LCN2, CDC25B, EPHB4, SORD). We validated these predictions in human colonic epithelial cells (NCM460). DBP exposure induced a pro-inflammatory state and upregulated the core genes, with LCN2 showing the strongest response.

[Applied Physics Letters Current Issue] Bidirectional optically modulated In 2 O 3 transistors with inorganic solid electrolyte gating for neuromorphic visual systems

Applied Physics Letters Current Issue — Mon, 05 Jan 2026 00:00:00 GMT

Inspired by retinal visual processing, we demonstrate a bidirectional optically controlled neuromorphic In₂O₃ transistor based on an inorganic solid electrolyte Li_1+xAl_xTi_2-x(PO₄)₃ (LATP) gate dielectric. The device exhibits light-controlled bidirectional visual bipolar cell behavior, exhibiting excitatory and inhibitory responses under ultraviolet (275 nm) and green light (520 nm) stimuli, respectively. X-ray photoelectron spectroscopy and capacitance–frequency measurements reveal that mobile Li⁺ ions in the LATP dielectric layer can adsorb electrons and form Coulombic binding states, thereby dynamically modulating photogenerated carrier transport. Optical pulse trains dynamically regulate the channel current, enabling bidirectional optical neural plasticity. Furthermore, a large-area device array was employed for image encoding and retinal damage simulation, highlighting its potential for artificial vision and neuromorphic computing. These findings establish an effective strategy for developing bidirectional optical, reconfigurable, and large-scale integrable neuromorphic devices, providing additional insights into the role of dielectric layer ion dynamics in neuromorphic optoelectronics.

[ScienceDirect Publication: Acta Materialia] Dual Engine-driven Strategy for Advanced Copper Alloy Design employing Large Language Models

ScienceDirect Publication: Acta Materialia — Sun, 04 Jan 2026 18:28:43 GMT

Publication date: Available online 3 January 2026

Source: Acta Materialia

Author(s): Fei Tan, Zixuan Zhao, Yanbin Jiang, Wenchao Zhang, Tong Xie, Wei Chen, Muzhi Ma, Yangfan Liu, Yanpeng Ye, Zhu Xiao, Qian Lei, Guofu Xu, Jie Ren, Yuyuan Zhao, Zhou Li

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Minimally Invasive, Label-Free, Point-of-Care Histopathological Diagnostic Platform of Malignant Tumors of the Female Reproductive System Based on Raman Spectroscopy and Machine Learning

The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications) — Sun, 04 Jan 2026 17:52:36 GMT

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03704

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

ChemRxiv — Sun, 04 Jan 2026 00:00:00 GMT

[ChemRxiv] Cellulose Coating Altered the Electro-Chemo-Mechanical Evolution of Sodium Thioantimonate Electrolyte in Solid-state Sodium Batteries: An Operando Raman Study

ChemRxiv — Sun, 04 Jan 2026 00:00:00 GMT

All-solid-state batteries (ASSBs) attracted increasing attention due to their improved safety and energy densities; yet electrolyte decomposition and subsequent contact loss limited the interfacial stability of ASSBs. Herein, we report an operando Raman characterization that provides high voltage, time, and spatial resolutions, which enables simultaneous analysis of interfacial decomposition mechanism and morphological evolution. Using Na3SbS4 electrolyte (NSS) and its carboxymethyl-cellulose-encapsulated analogue (NSS-CMC) as exemplars, we precisely contrasted the subtle differences in the two-step reduction mechanism of the two electrolytes. In both systems, Na3SbS3 formed as an intermediate, and Na3Sb binary as one major final product; while the CMC coating altered the kinetics of Na3SbS3 formation and consumption, and extended the formation potential of Na3Sb from 1.35 V (seen in NSS) to 0.50 V (vs. Na/Na+). Oxidation of NSS and NSS-CMC both occur near 2.20 V, although CMC coating altered the crystallinity of the oxidative products. Simultaneously, we captured phenomena that are unique to solid-state electrochemical systems such as particle relocation, morphological change, and reversed reactions. We inferred CMC’s dual role as a voltage barrier and a mechanical buffer in suppressing the electro-chemo-mechanical decomposition of NSS electrolyte. The deep mechanistic insights unravel the exact modification needed for improved interfacial stability.

[ScienceDirect Publication: Artificial Intelligence Chemistry] Accelerated green material and solvent discovery with chemistry- and physics-guided generative AI

ScienceDirect Publication: Artificial Intelligence Chemistry — Sat, 03 Jan 2026 12:38:39 GMT

Publication date: Available online 2 January 2026

Source: Artificial Intelligence Chemistry

Author(s): Eslam G. Al-Sakkari, Ahmed Ragab, Marzouk Benali, Olumoye Ajao, Daria C Boffito, Hanane Dagdougui

[Wiley: Angewandte Chemie International Edition: Table of Contents] Minutes‐Scale Ultrafast Synthesis of New Oxyhalides Solid Electrolytes with Interfacial Ionic Conduction for All‐Solid‐State Batteries

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 03 Jan 2026 06:30:47 GMT

Angewandte Chemie International Edition, Volume 65, Issue 1, 2 January 2026.

[Wiley: Advanced Materials: Table of Contents] Potential‐Gated Polymer Integrates Reversible Ion Transport and Storage for solid‐state Batteries

Wiley: Advanced Materials: Table of Contents — Sat, 03 Jan 2026 06:20:51 GMT

Advanced Materials, Volume 38, Issue 1, 2 January 2026.

[Wiley: Advanced Materials: Table of Contents] Generative Artificial Intelligence Navigated Development of Solvents for Next Generation High‐Performance Magnesium Batteries

Wiley: Advanced Materials: Table of Contents — Sat, 03 Jan 2026 06:20:51 GMT

Advanced Materials, Volume 38, Issue 1, 2 January 2026.

[Wiley: Angewandte Chemie International Edition: Table of Contents] Generality‐Driven Optimization of Enantio‐ and Regioselective Mono‐Reduction of 1,2‐Dicarbonyls by High‐Throughput Experimentation and Machine Learning

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 03 Jan 2026 06:15:46 GMT

Angewandte Chemie International Edition, Volume 65, Issue 1, 2 January 2026.

[Wiley: Angewandte Chemie International Edition: Table of Contents] An All‐Solid‐State Li–Cu Battery via Cuprous/Lithium‐Ion Halide Solid Electrolyte

Wiley: Angewandte Chemie International Edition: Table of Contents — Sat, 03 Jan 2026 06:15:46 GMT

Angewandte Chemie International Edition, Volume 65, Issue 1, 2 January 2026.

[iScience] AI-Driven Routing and Layered Architectures for Intelligent ICT in Nanosensor Networked Systems

iScience — Sat, 03 Jan 2026 00:00:00 GMT

This review examines the emerging integration of nanosensor networks with modern information and communication technologies to address critical needs in healthcare, environmental monitoring, and smart infrastructure. It evaluates how machine learning and artificial intelligence techniques improve data processing, energy management, real-time communication, and scalable system coordination within nanosensor environments. The analysis compares major learning approaches, including supervised, unsupervised, reinforcement, and deep learning methods, and highlights their effectiveness in data routing, anomaly detection, security, and predictive maintenance.

[ChemRxiv] The growing role of open source software in molecular modeling

ChemRxiv — Sat, 03 Jan 2026 00:00:00 GMT

The increasing importance and predictive power of modern molecular modeling, driven by physics- and machine learning-based methods, necessitates a new collaborative architecture to replace the isolated, traditional model of software development. The traditional approach often led to redundant engineering effort, high costs, and opaque systems that limit reproducibility, independent scrutiny, and scientific independence. + +This perspective advocates for permissively licensed open source software as a scientific and economic multiplier by reducing the duplication of effort, enabling scientific validation of modeling tools, and frictionless experimentation with new ideas. Coordinated, multi-project consortia, such as Open Force Field, Open Free Energy, OpenFold, and OpenADMET have formed to collaboratively build shared computational infrastructure and release all methods under permissive licenses. The success of these large-scale efforts requires organizational structures that extend beyond code. The Open Molecular Software Foundation (OMSF), a US nonprofit, serves as a domain-specific institutional home and fiscal sponsor. By providing governance, administrative infrastructure, and dedicated research software engineers, OMSF aligns incentives across academic and industrial stakeholders. This framework enables a synergistic ecosystem where projects interoperate to accelerate innovation, eliminate duplication, and ensure long-term software sustainability, thereby creating durable foundations that elevate the entire molecular modeling community.

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Tracing Lithophilic Sites: In Situ Nanovisualization of Their Migration and Degradation in All-Solid-State Lithium Batteries

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Fri, 02 Jan 2026 13:23:31 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c19144

[Wiley: Advanced Functional Materials: Table of Contents] Metal−Organic Framework Ion Conductor‐Based Polymer Solid Electrolytes for Long‐Cycle Lithium Batteries

Wiley: Advanced Functional Materials: Table of Contents — Fri, 02 Jan 2026 11:53:16 GMT

Advanced Functional Materials, Volume 36, Issue 1, 2 January 2026.

[Wiley: Small: Table of Contents] Regulating Interface Chemistry to Construct a Stable Solid Electrolyte Interphase for Long‐Life Zinc Metal Anodes

Wiley: Small: Table of Contents — Fri, 02 Jan 2026 11:26:58 GMT

Small, Volume 22, Issue 1, 2 January 2026.

[Recent Articles in Phys. Rev. Lett.] Common Sublattice-Pure Van Hove Singularities in the Kagome Superconductors $A{\mathrm{V}}_{3}{\mathrm{Sb}}_{5}$ ($A=\mathrm{K}$, Rb, Cs)

Recent Articles in Phys. Rev. Lett. — Fri, 02 Jan 2026 10:00:00 GMT

Author(s): Yujie Lan, Yuhao Lei, Congcong Le, Brenden R. Ortiz, Nicholas C. Plumb, Milan Radovic, Xianxin Wu, Ming Shi, Stephen D. Wilson, and Yong Hu

Kagome materials offer a versatile platform for exploring correlated and topological quantum states, where Van Hove singularities (VHSs) play a pivotal role in driving electronic instabilities, exhibiting distinct behaviors depending on electron filling and interaction settings. In the recently disc…

[Phys. Rev. Lett. 136, 016401] Published Fri Jan 02, 2026

[Recent Articles in Phys. Rev. Lett.] Half-Quantized Chiral Edge Current in a $C=1/2$ Parity Anomaly State

Recent Articles in Phys. Rev. Lett. — Fri, 02 Jan 2026 10:00:00 GMT

Author(s): Deyi Zhuo, Bomin Zhang, Humian Zhou, Han Tay, Xiaoda Liu, Zhiyuan Xi, Chui-Zhen Chen, and Cui-Zu Chang

A single massive Dirac surface band is predicted to exhibit a half-quantized Hall conductance, a hallmark of the $C=1/2$ parity anomaly state in quantum field theory. Experimental signatures of the $C=1/2$ parity anomaly state have been observed in semimagnetic topological insulator (TI) bilayers, y…

[Phys. Rev. Lett. 136, 016601] Published Fri Jan 02, 2026

[Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents] Synergistic Enhancement of Modified‐PVDF Humidity Sensitivity via Chemical Adsorption‐Ionic Conductivity and its Application in Intelligent Powered Air‐Purifying Respirator

Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents — Fri, 02 Jan 2026 09:41:25 GMT

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] In Situ Electric-Field Guided Assembly of Ordered Bilayer Solid Electrolyte Interphase (SEI) Enables High-Current Zinc Metal Anodes

The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications) — Fri, 02 Jan 2026 09:07:52 GMT

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03386

[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] A neutralizing APOA5 monoclonal antibody reduces amounts of lipoprotein lipase in capillaries and triggers hypertriglyceridemia

Fri, 02 Jan 2026 08:00:00 GMT

Proceedings of the National Academy of Sciences, Volume 123, Issue 1, January 2026.
SignificanceApolipoprotein AV (APOA5) reduces plasma triglyceride levels by binding to angiopoietin-like protein 3/8 complex (ANGPTL3/8) and suppressing its ability to block lipoprotein lipase, but our understanding of important APOA5 sequences and how ...

[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Antibody responses to a highly conserved peptide in HCV E2 protein correlate with chronicity or spontaneous clearance of HCV infection

Fri, 02 Jan 2026 08:00:00 GMT

Proceedings of the National Academy of Sciences, Volume 123, Issue 1, January 2026.
SignificanceChronicity is a hallmark of hepatitis C virus (HCV) infection, often leading to severe liver diseases such as cirrhosis and hepatocellular carcinoma. Although progression to chronicity or spontaneous clearance is believed to be immune mediated ...

[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Interpretable early warnings using machine learning in an online game-experiment

Fri, 02 Jan 2026 08:00:00 GMT

Proceedings of the National Academy of Sciences, Volume 123, Issue 1, January 2026.
SignificanceCritical transitions can model abrupt regime shifts in socio-ecological systems. While generic early warning signals that apply across systems have been investigated, no universal signal exists. We therefore propose a data-driven and system-...

[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Machine learning reveals hidden dimensions of functional similarity in proteins

Fri, 02 Jan 2026 08:00:00 GMT

Proceedings of the National Academy of Sciences, Volume 123, Issue 1, January 2026.

[Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents] Correlating the Interfacial Chemistries With Ion Conduction and Lithium Deactivation in Hybrid Solid Electrolytes

Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents — Fri, 02 Jan 2026 06:03:30 GMT

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.

[ChemRxiv] Complete Computational Exploration of Eight-Carbon Hydrocarbon Chemical Space

ChemRxiv — Fri, 02 Jan 2026 00:00:00 GMT

Hydrocarbons are the most fundamental class of chemical species, but even the chemical space of those with eight carbon atoms or less has not been explored exhaustively. Here we report a full enumeration and computational exploration of this space. Density functional theory-based geometry optimisation and energy calculations have identified all stable molecules within this space, forming a new database called CHX8. A universal strain value has been proposed and assigned to each of these molecules, acting as a proxy for synthesisability and providing a clear guideline of how synthetically plausible these molecules could be. This paper explores the limits of chemical space with CHX8, with a focus on trans-fused, unsaturated and anti-Bredt ring systems. We show that, contrary to prevailing wisdom, most of these unconventional structures should be synthetically accessible, with relative strain energies less than that of cubane. It is expected that this dataset will inspire the synthesis of many new molecules with applications in various areas of chemistry, biology and materials science. The resulting dataset also provides a valuable resource for the development of general and robust machine learning models.

[ChemRxiv] A round-robin exercise for the precise prediction of aqueous solubility of organic chemicals using chemometric, machine learning, and stacking ensemble of deep learning models

ChemRxiv — Fri, 02 Jan 2026 00:00:00 GMT

Aqueous solubility is an important property for assessing the druggability and ecotoxicological effects of molecules. Successful drug candidates should have optimal aqueous solubility to improve bioavailability to target tissues. To effectively screen molecules in a short period of time, reliable predictive models are highly useful. In the present study, we conducted a round-robin exercise using a large, curated dataset of over 6000 compounds to predict aqueous solubility quantitatively. The six participating groups used an array of Machine Learning and Deep Learning algorithms to develop models with strong robustness and external predictive performance. All the models underwent rigorous Leave-One-Out and 10-fold cross-validation. The diversity of training sets and descriptor types used by different groups paved the way for exploring the mechanistic basis for the efficient identification of contributing features. The best-performing model was selected using the statistical Sum of Ranking Differences (SRD) approach, considering the performances on training, cross-validation, and test, as well as the performance difference between the training and test sets. Additionally, a curated, true external set was screened by the six different models. Here, the best-performing model was selected using a consensus ranking strategy based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R_Ext^2. In both approaches, i.e., the inherent model performance in terms of training, test, and cross-validation statistics, and the ability of the model to efficiently predict true external data, the Stacking Ensemble of Deep q-RASPR model emerged as the winner. This model showed comparable predictive performance to the previously reported model, which apparently lacked a proper data curation workflow and contained a significant number of duplicates and mixtures in its dataset, which can inflate model statistics. The insights from the different feature contributions from the different groups identified the useful structural and physicochemical aspects, which can help synthetic chemists to optimize molecules.

[Joule] Seeing the unseen: Real-time tracking of battery cycling-to-failure via surface strain

Joule — Fri, 02 Jan 2026 00:00:00 GMT

This study proposes a strain-based approach to address passive failures in lithium-ion batteries, which present spontaneous safety risks often indistinguishable from routine degradation using conventional diagnostics. By establishing a strain-failure correlation, we introduce a slope-based threshold and a failure-proximity index to characterize degradation-to-failure transitions. Incorporating strain-informed machine learning, it effectively detects early failure onset and estimates proximity. This scalable approach is suitable for real-time, onboard monitoring, supporting safer and more reliable battery operation.

[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Understanding and Mitigating Lithium Metal Anode Failure in All-Solid-State Batteries with Inorganic Solid Electrolytes

ACS Energy Letters: Latest Articles (ACS Publications) — Thu, 01 Jan 2026 18:39:05 GMT

ACS Energy Letters

DOI: 10.1021/acsenergylett.5c03333

[ScienceDirect Publication: Computational Materials Science] Accelerating the search for superconductors using machine learning

ScienceDirect Publication: Computational Materials Science — Thu, 01 Jan 2026 18:29:38 GMT

Publication date: February 2026

Source: Computational Materials Science, Volume 263

Author(s): Suhas Adiga, Umesh V. Waghmare

[ScienceDirect Publication: Journal of Catalysis] Machine learning–assisted discovery of chromium bis(2-pyridyl)amine catalysts for ethylene tri-/tetramerization

ScienceDirect Publication: Journal of Catalysis — Thu, 01 Jan 2026 12:22:12 GMT

Publication date: February 2026

Source: Journal of Catalysis, Volume 454

Author(s): Youcai Zhu, Yue Mu, Xiaoke Shi, Shu Yang, Li Sun, Zhen Liu

[ScienceDirect Publication: Journal of Catalysis] The influence of the organic residue and the solvent in the Schlenk equilibrium for Grignard reagents in THF. A molecular dynamics study with machine learning potentials

ScienceDirect Publication: Journal of Catalysis — Thu, 01 Jan 2026 12:22:12 GMT

Publication date: February 2026

Source: Journal of Catalysis, Volume 454

Author(s): Marco Bortoli, Sigbjørn Løland Bore, Odile Eisenstein, Michele Cascella

[ScienceDirect Publication: Journal of Catalysis] Protonation dynamics of confined ethanol–water mixtures in H-ZSM-5 from machine learning-driven metadynamics

ScienceDirect Publication: Journal of Catalysis — Thu, 01 Jan 2026 12:22:12 GMT

Publication date: February 2026

Source: Journal of Catalysis, Volume 454

Author(s): Princy Jarngal, Benjamin A. Jackson, Simuck F. Yuk, Difan Zhang, Mal-Soon Lee, Maria Cristina Menziani, Vassiliki-Alexandra Glezakou, Roger Rousseau, GiovanniMaria Piccini

[ScienceDirect Publication: Acta Materialia] Inverse Design of High-Performance Glasses Through an Encoder-Decoder Machine Learning Approach Toward Materials Discovery: Application to Oxynitride Glasses

ScienceDirect Publication: Acta Materialia — Thu, 01 Jan 2026 12:22:12 GMT

Publication date: Available online 29 December 2025

Source: Acta Materialia

Author(s): Alexis Duval, Eric Robin, Patrick Houizot, Tanguy Rouxel

[ScienceDirect Publication: Acta Materialia] DuctGPT: A Generative Transformer for Forward Screening of Ductile Refractory Multi-Principal Element Alloys

ScienceDirect Publication: Acta Materialia — Thu, 01 Jan 2026 12:22:12 GMT

Publication date: 1 January 2026

Source: Acta Materialia, Volume 304

Author(s): Sai Pranav Reddy Guduru, Mkpe O. Kekung, Ryan T. Ott, Sougata Roy, Prashant Singh

[ScienceDirect Publication: Acta Materialia] On-the-fly machine learning of interatomic potentials for elastic property modeling in Al–Mg–Zr solid solutions

ScienceDirect Publication: Acta Materialia — Thu, 01 Jan 2026 12:22:12 GMT

Publication date: 15 February 2026

Source: Acta Materialia, Volume 305

Author(s): Lukas Volkmer, Leonardo Medrano Sandonas, Philip Grimm, Julia Kristin Hufenbach, Gianaurelio Cuniberti

[ScienceDirect Publication: Journal of Materiomics] Data-augmented machine learning models for oxynitride glasses via Wasserstein generative adversarial network with gradient penalty and content constraint

ScienceDirect Publication: Journal of Materiomics — Thu, 01 Jan 2026 12:21:56 GMT

Publication date: Available online 8 August 2025

Source: Journal of Materiomics

Author(s): Jing Tian, Yuan Li, Min Guan, Jijie Zheng, Jingyuan Chu, Yong Liu, Gaorong Han

[ScienceDirect Publication: Journal of Materiomics] Machine learning assisted τ_f value prediction of ABO₃-type microwave dielectric ceramics

ScienceDirect Publication: Journal of Materiomics — Thu, 01 Jan 2026 12:21:56 GMT

Publication date: Available online 8 August 2025

Source: Journal of Materiomics

Author(s): Mingyue Yang, Liangyu Mo, Jincheng Qin, Faqiang Zhang, Mingsheng Ma, Yongxiang Li, Zhifu Liu

[ScienceDirect Publication: Journal of Materiomics] Domain knowledge-assisted materials data anomaly detection towards constructing high-performance machine learning models

ScienceDirect Publication: Journal of Materiomics — Thu, 01 Jan 2026 12:21:56 GMT

Publication date: November 2025

Source: Journal of Materiomics, Volume 11, Issue 6

Author(s): Yue Liu, Shuchang Ma, Zhengwei Yang, Duo Wu, Yali Zhao, Maxim Avdeev, Siqi Shi

[ScienceDirect Publication: Journal of Materiomics] PTCDA/CuS cathode enabling stable sulfide-based all-solid-state batteries

ScienceDirect Publication: Journal of Materiomics — Thu, 01 Jan 2026 12:21:56 GMT

Publication date: January 2026

Source: Journal of Materiomics, Volume 12, Issue 1

Author(s): Zhixing Wan, Shuo Wang, Yahao Mu, Ruihua Zhou, Hang Liu, Tingwu Jin, Di Wu, Jianlong Xia, Ce-Wen Nan

[ScienceDirect Publication: Current Opinion in Solid State and Materials Science] Solid electrolyte-driven suppression of H2–H3 phase transition in Ni-rich cathodes for stable high-voltage cycling

ScienceDirect Publication: Current Opinion in Solid State and Materials Science — Thu, 01 Jan 2026 12:21:56 GMT

Publication date: December 2025

Source: Current Opinion in Solid State and Materials Science, Volume 39

Author(s): Hao Chen, Hsiao-Hsuan Wu, Chia-Chen Li

[ScienceDirect Publication: Current Opinion in Solid State and Materials Science] State-of-the-art review of additive friction stir deposition: microstructural evolution, machine learning applications, and future directions

ScienceDirect Publication: Current Opinion in Solid State and Materials Science — Thu, 01 Jan 2026 12:21:56 GMT

Publication date: January 2026

Source: Current Opinion in Solid State and Materials Science, Volume 40

Author(s): Ashish Kumar, Lei Shi, Virendra Pratap Singh, Sudipta Mohapatra, Long Li, Chuansong Wu, Sergey Mironov, Amitava De

[ScienceDirect Publication: Journal of Energy Storage] Machine learning-aided prediction and optimization of specific capacitance in functionalized GO-based Mn/Fe co-doped Bi₂O₃ nanocomposites

ScienceDirect Publication: Journal of Energy Storage — Thu, 01 Jan 2026 12:21:56 GMT

Publication date: 10 February 2026

Source: Journal of Energy Storage, Volume 146

Author(s): Vijay A. Mane, Kartik M. Chavan, Sushant S. Munde, Dnyaneshwar V. Dake, Nita D. Raskar, Ramprasad B. Sonpir, Pravin V. Dhole, Ketan P. Gattu, Sandeep B. Somvanshi, Pavan R. Kayande, Jagruti S. Pawar, Babasaheb N. Dole

[ScienceDirect Publication: Journal of Energy Storage] Time-resolved impedance spectroscopy analysis of stable lithium iron phosphate cathode with enhanced electronic/ionic conductivity and ion diffusion characteristics

ScienceDirect Publication: Journal of Energy Storage — Thu, 01 Jan 2026 12:21:56 GMT

Publication date: 10 February 2026

Source: Journal of Energy Storage, Volume 146

Author(s): Jiguo Tu, Yan Li, Libo Chen, Dongbai Sun

[ScienceDirect Publication: Journal of Energy Storage] Hollow nanofiber ion conductor protective layer on Zn metal anode for long-term stable zinc battery

ScienceDirect Publication: Journal of Energy Storage — Thu, 01 Jan 2026 12:21:56 GMT

Publication date: 10 February 2026

Source: Journal of Energy Storage, Volume 146

Author(s): Mengfei Sun, Zumin Zhang, Yang Su, Wensheng Yu, Xiangting Dong, Dongtao Liu, Xinlu Wang, Gaopeng Li, Jinxian Wang

[ScienceDirect Publication: Journal of Energy Storage] Alkaline-compatible polyaniline/graphene negative electrode for ultrahigh-energy all-solid-state asymmetric supercapacitors

ScienceDirect Publication: Journal of Energy Storage — Thu, 01 Jan 2026 12:21:56 GMT

Publication date: 10 February 2026

Source: Journal of Energy Storage, Volume 146

Author(s): Aizhen Xu, Li Yin, Shaoqing Zhang, Zhiyi Zhao, Wenna Lv, Yuanyu Zhu, Yujun Qin

[ScienceDirect Publication: Solid State Ionics] Crossover from insulating into solid electrolyte behavior in bulk CaSO₄⋅0.5H₂O material due to ion exchange processes induced by high-temperature treatment with orthophosphoric acid

ScienceDirect Publication: Solid State Ionics — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: 1 February 2026

Source: Solid State Ionics, Volume 434

Author(s): Ivan Nikulin, Tatiana Nikulicheva, Vitaly Vyazmin, Oleg Ivanov, Nikita Anosov, Olga Telpova

[ScienceDirect Publication: Solid State Ionics] First-principles elucidation of the effects of Al-doping on Li-ion diffusion in LiCoO₂

ScienceDirect Publication: Solid State Ionics — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: 1 February 2026

Source: Solid State Ionics, Volume 434

Author(s): Jordan A. Barr, Scott P. Beckman, Brandon C. Wood, Liwen F. Wan

[ScienceDirect Publication: Solid State Ionics] Anion engineering enhances the electrochemical performance of Li₇P₃S₁₁ solid electrolyte

ScienceDirect Publication: Solid State Ionics — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: 15 February 2026

Source: Solid State Ionics, Volume 435

Author(s): D. Narsimulu, Ramkumar Balasubramaniam, Kwang-Sun Ryu

[ScienceDirect Publication: Computational Materials Science] Machine learning assisted local descriptors predicate oxygen reduction activity of transition metal@Ti_1−xZn_x alloys

ScienceDirect Publication: Computational Materials Science — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: 30 January 2026

Source: Computational Materials Science, Volume 262

Author(s): Tian-Zhe Wan, Shou-Heng Guo, Guang-Qiang Yu, Jun-Zhe Li, Ya-Nan Zhu, Xi-Bo Li

[ScienceDirect Publication: Computational Materials Science] PyVUMAT: A package to develop and deploy machine learning material models in finite element analysis simulations

ScienceDirect Publication: Computational Materials Science — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: 30 January 2026

Source: Computational Materials Science, Volume 262

Author(s): Joshua C. Crone

[ScienceDirect Publication: Computational Materials Science] Predicting hydrogen storage capacity of metal hydrides using novel imputation techniques and tree-based machine learning models

ScienceDirect Publication: Computational Materials Science — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: 30 January 2026

Source: Computational Materials Science, Volume 262

Author(s): Zaid Allal, Hassan N. Noura, Flavien Vernier, Ola Salman, Khaled Chahine

[ScienceDirect Publication: Computational Materials Science] Accelerating magnetic materials discovery using interaction matrix-based machine learning descriptors

ScienceDirect Publication: Computational Materials Science — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: 30 January 2026

Source: Computational Materials Science, Volume 262

Author(s): Apoorv Verma, Junaid Jami, Amrita Bhattacharya

[ScienceDirect Publication: Computational Materials Science] Effects of training machine-learning potentials for radiation damage simulations using different pseudopotentials

ScienceDirect Publication: Computational Materials Science — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: 30 January 2026

Source: Computational Materials Science, Volume 262

Author(s): A. Fellman, J. Byggmästar, F. Granberg, F. Djurabekova, K. Nordlund

[ScienceDirect Publication: Computational Materials Science] Structure-driven prediction and mechanism insights into piezoelectric performance of potassium sodium niobate via interpretable machine learning

ScienceDirect Publication: Computational Materials Science — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: February 2026

Source: Computational Materials Science, Volume 263

Author(s): Hui Li, WenKe Lu, Chunlei Li, Jinyi Liu, Zihui Feng, Jiaming Liu, Lan Yang

[ScienceDirect Publication: Computational Materials Science] Band structure modulation of germanium under arbitrary strain directions: A combined approach of strain matrix theory, first-principles calculation and machine learning

ScienceDirect Publication: Computational Materials Science — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: February 2026

Source: Computational Materials Science, Volume 263

Author(s): Hai Wang, Wenqi Huang, Mengjiang Jia

[ScienceDirect Publication: Computational Materials Science] Machine learning-driven prognostication of multifunctional properties of (K,Na)NbO₃-based lead-free ceramics for optimized materials design

ScienceDirect Publication: Computational Materials Science — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: February 2026

Source: Computational Materials Science, Volume 263

Author(s): Manisha Kumari, Alok Shukla

[ScienceDirect Publication: Computational Materials Science] Machine learning for domain transfer between simulated and experimental 2D X-ray diffraction patterns using generative adversarial networks

ScienceDirect Publication: Computational Materials Science — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: February 2026

Source: Computational Materials Science, Volume 263

Author(s): Samantha J. Brozak, David Montes de Oca Zapiain, Brendan Donohoe, Tommy Ao, Nathan P. Brown, Marcus D. Knudson, Carianne Martinez, J. Matthew D. Lane

[ScienceDirect Publication: Computational Materials Science] Machine learning interatomic potentials for monolayer hexagonal boron nitride: Thermal transport and defect effects via deep potential molecular dynamics

ScienceDirect Publication: Computational Materials Science — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: February 2026

Source: Computational Materials Science, Volume 263

Author(s): Guanghao Zhang, Zicheng Wang, Caihua Shi, Ye Han

[ScienceDirect Publication: Computational Materials Science] An enhanced machine learning and computational screening framework for synthesizable single-phase high-entropy spinel oxides

ScienceDirect Publication: Computational Materials Science — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: February 2026

Source: Computational Materials Science, Volume 263

Author(s): Mahalaxmi Chandramohan, Hridhya Vinod, Meenal Deo

[ScienceDirect Publication: Artificial Intelligence Chemistry] Machine Learning (ML)-driven quantitative structure-pharmacokinetic relationship (QSPKR) modeling of the tissue-to-plasma partition coefficient (K_p) of drugs across different tissues

ScienceDirect Publication: Artificial Intelligence Chemistry — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: December 2025

Source: Artificial Intelligence Chemistry, Volume 3, Issue 2

Author(s): Souvik Pore, Kunal Roy

[ScienceDirect Publication: Artificial Intelligence Chemistry] Machine learning prediction of pKa of organic acids

ScienceDirect Publication: Artificial Intelligence Chemistry — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: December 2025

Source: Artificial Intelligence Chemistry, Volume 3, Issue 2

Author(s): Juda Baikété, Alhadji Malloum, Jeanet Conradie

[ScienceDirect Publication: Artificial Intelligence Chemistry] GraphSLA: Graph machine learning for predicting small molecule - lncRNA associations

ScienceDirect Publication: Artificial Intelligence Chemistry — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: December 2025

Source: Artificial Intelligence Chemistry, Volume 3, Issue 2

Author(s): Ashish Panghalia, Parth Kumar, Vikram Singh

[ScienceDirect Publication: Artificial Intelligence Chemistry] Machine learning-guided synthesis of prospective organic molecular materials: An algorithm with latent variables for understanding and predicting experimentally unobservable reactions

ScienceDirect Publication: Artificial Intelligence Chemistry — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: December 2025

Source: Artificial Intelligence Chemistry, Volume 3, Issue 2

Author(s): Kazuhiro Takeda, Naoya Ohtsuka, Toshiyasu Suzuki, Norie Momiyama

[ScienceDirect Publication: Artificial Intelligence Chemistry] Neural networks for neurocomputing circuits: A computational study of tolerance to noise and activation function non-uniformity when machine learning materials properties

ScienceDirect Publication: Artificial Intelligence Chemistry — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: December 2025

Source: Artificial Intelligence Chemistry, Volume 3, Issue 2

Author(s): Ye min Thant, Methawee Nukunudompanich, Chu-Chen Chueh, Manabu Ihara, Sergei Manzhos

[ScienceDirect Publication: Artificial Intelligence Chemistry] Electrostatic map for the hydration-induced frequency shifts of the NH stretching mode of peptide constructed using some machine learning models

ScienceDirect Publication: Artificial Intelligence Chemistry — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: December 2025

Source: Artificial Intelligence Chemistry, Volume 3, Issue 2

Author(s): Hajime Torii, Mikito Tsujimoto, Yukichi Kitamura

[ScienceDirect Publication: Artificial Intelligence Chemistry] Comparative study of machine learning methods for accurate prediction of logP and pKb

ScienceDirect Publication: Artificial Intelligence Chemistry — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: June 2026

Source: Artificial Intelligence Chemistry, Volume 4, Issue 1

Author(s): Juda Baikété, Alhadji Malloum, Jeanet Conradie

[ScienceDirect Publication: Artificial Intelligence Chemistry] Statistical comparison and uncertainty analysis of graph neural networks and machine learning models for molecular property prediction in drug discovery

ScienceDirect Publication: Artificial Intelligence Chemistry — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: June 2026

Source: Artificial Intelligence Chemistry, Volume 4, Issue 1

Author(s): Sarmad Waleed, Shams ul Islam, Muhammad Saleem, Ali Ahmed

[ScienceDirect Publication: Artificial Intelligence Chemistry] Integrating machine learning with electrochemical sensors for intelligent food safety monitoring

ScienceDirect Publication: Artificial Intelligence Chemistry — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: June 2026

Source: Artificial Intelligence Chemistry, Volume 4, Issue 1

Author(s): Aaryashree, Arti Devi

[ScienceDirect Publication: eScience] Machine learning-driven discovery of high-performance Co/Sr-free air electrodes for protonic ceramic electrolysis cells

ScienceDirect Publication: eScience — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: Available online 24 October 2025

Source: eScience

Author(s): Ning Wang, Qingwen Su, Huanxin Xiang, Biyao Zhou, Jiaxin Huang, Fangyuan Zheng, Ruijie Zhu, Yoshitaka Aoki, Ling Meng, Feng Jiao, Baoyin Yuan, Chunmei Tang, Siyu Ye

[ScienceDirect Publication: eScience] Oxygen-induced lithium inter-cage conduction for enhanced performance in all-solid-state batteries

ScienceDirect Publication: eScience — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: Available online 18 November 2025

Source: eScience

Author(s): Yongsun Park, Myeongcho Jang, Jiyu Kim, Joon Ha Chang, Hae-Yong Kim, Seung-Deok Seo, Gwangseok Oh, Min Jae You, Sunho Ko, Jung Ho Kim, Kyung-Wan Nam, Seungho Yu, Sang-Cheol Nam, Ohmin Kwon

[ScienceDirect Publication: eScience] Achieving high-voltage polymer-based all-solid-state batteries based on thermodynamic and kinetic degradation insights

ScienceDirect Publication: eScience — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: January 2026

Source: eScience, Volume 6, Issue 1

Author(s): Xiaoyan Yu, Yun Su, Hang Su, Ruizhi Liu, Jingyi Qiu, Xiayu Zhu, Rui Wen, Hao Zhang, Xiaohui Rong, Yong-Sheng Hu, Gaoping Cao

[ScienceDirect Publication: Science Bulletin] Welding surface epitaxial layer enables high-loading sulfide all-solid-state batteries

ScienceDirect Publication: Science Bulletin — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: 15 December 2025

Source: Science Bulletin, Volume 70, Issue 23

Author(s): Zhuomin Qiang, Yanbin Ning, Wei Zhao, Yajie Song, Chaoqun Zhang, Shengwei Dong, Yifei Zhao, Yan Zhang, Yinyong Sun, Biao Deng, Xudong Li, Geping Yin, Shuaifeng Lou

[ScienceDirect Publication: Science Bulletin] Machine learning for spectral precision: a new horizon in radiative cooling material design

ScienceDirect Publication: Science Bulletin — Thu, 01 Jan 2026 12:21:54 GMT

Publication date: 30 December 2025

Source: Science Bulletin, Volume 70, Issue 24

Author(s): Xinpeng Hu, Mingxiang Liu, Xuemei Fu, Guangming Tao, Xiang Lu, Jinping Qu

[ScienceDirect Publication: Progress in Materials Science] The role of protein content in body fluids in magnesium alloy bioimplant degradation: A machine learning approach

ScienceDirect Publication: Progress in Materials Science — Thu, 01 Jan 2026 12:21:51 GMT

Publication date: April 2026

Source: Progress in Materials Science, Volume 158

Author(s): M.N. Bharath, R.K. Singh Raman, Alankar Alankar

[ScienceDirect Publication: Materials Today Physics] Machine-learning potentials for quantum and anharmonic effects in superconducting $F m \overline{3} m$ LaBeH₈

ScienceDirect Publication: Materials Today Physics — Thu, 01 Jan 2026 12:21:49 GMT

Publication date: December 2025

Source: Materials Today Physics, Volume 59

Author(s): Guiyan Dong, Tian Cui, Zihao Huo, Zhengtao Liu, Wenxuan Chen, Pugeng Hou, Yue-Wen Fang, Defang Duan

[ScienceDirect Publication: Materials Today Physics] A computational framework for interface design using lattice matching, machine learning potentials, and active learning: A case study on LaCoO₃/La₂NiO₄

ScienceDirect Publication: Materials Today Physics — Thu, 01 Jan 2026 12:21:49 GMT

Publication date: December 2025

Source: Materials Today Physics, Volume 59

Author(s): Guangchen Liu, Songge Yang, Yu Zhong

[ScienceDirect Publication: Materials Today Physics] Tackling dataset curation challenges towards reliable machine learning: a case study on thermoelectric materials

ScienceDirect Publication: Materials Today Physics — Thu, 01 Jan 2026 12:21:49 GMT

Publication date: December 2025

Source: Materials Today Physics, Volume 59

Author(s): Shoeb Athar, Adrien Mecibah, Philippe Jund

[ScienceDirect Publication: Materials Today Physics] Research progress of machine learning in flexible strain sensors in the context of material intelligence

ScienceDirect Publication: Materials Today Physics — Thu, 01 Jan 2026 12:21:49 GMT

Publication date: December 2025

Source: Materials Today Physics, Volume 59

Author(s): Jie Li, Zhe Li, Yan Lu, Gang Ye, Yan Hong, Li Niu, Jian Fang

[ScienceDirect Publication: Materials Today Physics] A physics-informed machine learning framework for unified prediction of superconducting transition temperatures

ScienceDirect Publication: Materials Today Physics — Thu, 01 Jan 2026 12:21:49 GMT

Publication date: January 2026

Source: Materials Today Physics, Volume 60

Author(s): Ehsan Alibagheri, Mohammad Sandoghchi, Alireza Seyfi, Mohammad Khazaei, S. Mehdi Vaez Allaei

[ScienceDirect Publication: Materials Today Physics] Revisiting thermoelectric transport in 122 Zintl phases: Anharmonic phonon renormalization and phonon localization effects

ScienceDirect Publication: Materials Today Physics — Thu, 01 Jan 2026 12:21:49 GMT

Publication date: January 2026

Source: Materials Today Physics, Volume 60

Author(s): Zhenguo Wang, Yinchang Zhao, Jun Ni, Zhenhong Dai

[ScienceDirect Publication: Materials Today Physics] Anomalous temperature evolution of lattice anharmonicity and thermal transport in orthorhombic SnSe

ScienceDirect Publication: Materials Today Physics — Thu, 01 Jan 2026 12:21:49 GMT

Publication date: January 2026

Source: Materials Today Physics, Volume 60

Author(s): Tianxiang Jiang, Wujie Qiu, Haijuan Zhang, Jifen Wang, Kunpeng Zhao, Huaqing Xie

[ScienceDirect Publication: Materials Today Physics] Machine learning aided bandgap and defect engineering of mixed halide perovskites for photovoltaic applications

ScienceDirect Publication: Materials Today Physics — Thu, 01 Jan 2026 12:21:49 GMT

Publication date: January 2026

Source: Materials Today Physics, Volume 60

Author(s): Ayush Kumar Pandey, Vivek Pandey, Abhishek Tewari

[ScienceDirect Publication: Materials Today] A facile construction of LiF interlayer and F-doping via PECVD for LATP-based hybrid electrolytes: Enhanced Li-ion transport kinetics and superior lithium metal compatibility

ScienceDirect Publication: Materials Today — Thu, 01 Jan 2026 12:21:49 GMT

Publication date: December 2025

Source: Materials Today, Volume 91

Author(s): Xian-Ao Li, Yiwei Xu, Kepin Zhu, Yang Wang, Ziqi Zhao, Shengwei Dong, Bin Wu, Hua Huo, Shuaifeng Lou, Xinhui Xia, Xin Liu, Minghua Chen, Stefano Passerini, Zhen Chen

[ScienceDirect Publication: Materials Today] Revitalizing multifunctionality of Li-Al-O system enabling mother-powder-free sintering of garnet-type solid electrolytes

ScienceDirect Publication: Materials Today — Thu, 01 Jan 2026 12:21:49 GMT

Publication date: Available online 10 December 2025

Source: Materials Today

Author(s): Hwa-Jung Kim, Jong Hoon Kim, Minseo Choi, Jung Hyun Kim, Hosun Shin, Ki Chang Kwon, Sun Hwa Park, Hyun Min Park, Seokhee Lee, Young Heon Kim, Hyeokjun Park, Seung-Wook Baek

[ScienceDirect Publication: Nano Energy] Monoclinic Li₂ZrO₃ with cationic vacancy–based ion transport channels enhanced composite polymer electrolytes for high-rate solid-state lithium metal batteries

ScienceDirect Publication: Nano Energy — Thu, 01 Jan 2026 12:21:46 GMT

Publication date: January 2026

Source: Nano Energy, Volume 147

Author(s): Qianyi Xu, Yanru Wang, Xiang Feng, Timing Fang, Xueyan Li, Longzhou Zhang, Lijie Zhang, Daohao Li, Dongjiang Yang

[ScienceDirect Publication: Nano Energy] Sulfonated ether-free polybenzimidazole membrane with fast and selective ion transport enabling ultrahigh cycle stability in vanadium redox flow batteries

ScienceDirect Publication: Nano Energy — Thu, 01 Jan 2026 12:21:46 GMT

Publication date: January 2026

Source: Nano Energy, Volume 147

Author(s): Hui Yan, Wei Wei, Xin Li, Qi-an Zhang, Ying Li, Ao Tang

[ScienceDirect Publication: Nano Energy] Calendar aging of sulfide all-solid-state batteries

ScienceDirect Publication: Nano Energy — Thu, 01 Jan 2026 12:21:46 GMT

Publication date: January 2026

Source: Nano Energy, Volume 147

Author(s): Yujing Wu, Ziqi Zhang, Dengxu Wu, Fuqiang Xu, Mu Zhou, Hong Li, Liquan Chen, Fan Wu

[ScienceDirect Publication: Nano Energy] Energy-efficient, high-accuracy sensing in loose-fitting textile sensor matrix for LLM-enabled human-robot collaboration

ScienceDirect Publication: Nano Energy — Thu, 01 Jan 2026 12:21:46 GMT

Publication date: January 2026

Source: Nano Energy, Volume 147

Author(s): Pengfei Deng, Yang Meng, Qilong Cheng, Yuanqiu Tan, Zhihong Chen, Tian Li

[ScienceDirect Publication: Nano Energy] Lithium superionic solid electrolyte: Phosphorus-free sulfide glass of LiSbGe_(4-x)/4S_4-xCl_x

ScienceDirect Publication: Nano Energy — Thu, 01 Jan 2026 12:21:46 GMT

Publication date: January 2026

Source: Nano Energy, Volume 147

Author(s): Yuna Kim, Woojung Lee, Jiyun Han, Yeong Mu Seo, Dokyung Kim, Young Joo Lee, Byung Gon Kim, Munseok S. Chae, Sung Jin Kim, In Young Kim

[ScienceDirect Publication: Nano Energy] Advancing high-safety and low-cost all-solid-state batteries with polyanion cathodes: Challenges and recent progress

ScienceDirect Publication: Nano Energy — Thu, 01 Jan 2026 12:21:46 GMT

Publication date: February 2026

Source: Nano Energy, Volume 148

Author(s): Ali Yaghtin, Atiyeh Nekahi, Jeremy I.G. Dawkins, Xia Li, Karim Zaghib, Sixu Deng

[ScienceDirect Publication: Nano Energy] Enabling high-accuracy lithium-ion battery status prediction via machine learning-integrated perovskite sensors

ScienceDirect Publication: Nano Energy — Thu, 01 Jan 2026 12:21:46 GMT

Publication date: February 2026

Source: Nano Energy, Volume 148

Author(s): Jiaxin Sun, Xianjie Xu, Zhefu Mu, Zijun Huang, Guo Chen, Xinkai Qi, Hongwei Liu, Lei Zhu, Xiuquan Gu, Xinjian He, Sheng Huang

[ScienceDirect Publication: Nano Energy] Phase boundary engineering in micro-sized Sn/SnSb anode enabling superior sodium storage: Synergistic stress relief and fast ion transport

ScienceDirect Publication: Nano Energy — Thu, 01 Jan 2026 12:21:46 GMT

Publication date: February 2026

Source: Nano Energy, Volume 148

Author(s): Yuhong Liang, Chengcheng He, Zhengyang Zhao, Longqing Zhang, Rui Sun, Qian Ning, Huibing He, Yang Ren, Jing Xu, Qiang Zhang, Yajie Song, Xucai Yin

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

ScienceDirect Publication: Matter — Thu, 01 Jan 2026 12:21:46 GMT

Publication date: Available online 10 October 2025

Source: Matter

Author(s): Zhipeng Miao, Yahui Han, Qi Pan, Yipei Wang, Haibin Wang, Yunhang Xie, Jie Yu, Yapeng Shi, Rui Zhang, Yanlin Song, Pengwei Li

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

ScienceDirect Publication: Matter — Thu, 01 Jan 2026 12:21:46 GMT

Publication date: Available online 14 October 2025

Source: Matter

Author(s): Yanmin Zhu, Loza F. Tadesse

[ScienceDirect Publication: Matter] Precisely deciphering solid electrolyte interphase

ScienceDirect Publication: Matter — Thu, 01 Jan 2026 12:21:46 GMT

Publication date: 5 November 2025

Source: Matter, Volume 8, Issue 11

Author(s): Enhui Wang, Shaohua Ge, Wenbin Li, Beibei Fu, Fangyi Zhou, Weihua Chen

[ScienceDirect Publication: Matter] Rapid scalable plasma processing of thin-film Li–La–Zr–O solid-state electrolytes

ScienceDirect Publication: Matter — Thu, 01 Jan 2026 12:21:46 GMT

Publication date: 5 November 2025

Source: Matter, Volume 8, Issue 11

Author(s): Gabriel Badillo Crane, Thomas W. Colburn, Sarah E. Holmes, Justus Just, Yi Cui, Reinhold H. Dauskardt

[ScienceDirect Publication: Joule] Impact of metallic interlayers at the lithium-Li₆PS₅Cl solid electrolyte interface

ScienceDirect Publication: Joule — Thu, 01 Jan 2026 12:21:46 GMT

Publication date: 19 November 2025

Source: Joule, Volume 9, Issue 11

Author(s): Souhardh Kotakadi, Jack Aspinall, Matthew Burton, Yi Liang, Yuichi Aihara, Mauro Pasta

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

ScienceDirect Publication: Joule — Thu, 01 Jan 2026 12:21:46 GMT

Publication date: 17 December 2025

Source: Joule, Volume 9, Issue 12

Author(s): Do-Hyeon Kim, Young-Han Lee, Jeong-Myeong Yoon, Pugalenthiyar Thondaiman, Byung Chul Kim, In-Chul Choi, Jeong-Hee Choi, Ki-Joon Jeon, Cheol-Min Park

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

ScienceDirect Publication: Joule — Thu, 01 Jan 2026 12:21:46 GMT

Publication date: Available online 19 December 2025

Source: Joule

Author(s): Huaimin Jin, Xingyu Wang, Simeng Zhang, Xiangzhen Zhu, Chong Liu, Junyi Yue, Jie Qu, Bei Wu, Xu Han, Yueyue Wang, Yang Xu, Han Wu, Liyu Zhou, Mingying Zhang, Hao Lai, Shuo Wang, Jiangwen Liang, Xueliang Sun, Xiaona Li

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

ScienceDirect Publication: Joule — Thu, 01 Jan 2026 12:21:46 GMT

Publication date: Available online 23 December 2025

Source: Joule

Author(s): Chenyang Zhang, Yuteng Jia, Bingqian Zhang, Qiangqiang Zhao, Ruida Xu, Shuping Pang, Han Wang, Stefaan De Wolf, Kai Wang

[cond-mat updates on arXiv.org] Atomic-scale visualization of d-wave altermagnetism

cond-mat updates on arXiv.org — Thu, 01 Jan 2026 05:00:00 GMT

arXiv:2512.24114v1 Announce Type: new Abstract: Altermagnetism is a newly discovered fundamental form of magnetic order, distinct from conventional ferromagnetism and antiferromagnetism. It uniquely exhibits no net magnetization while simultaneously breaking time-reversal symmetry, a combination previously thought to be mutually exclusive. Although its existence and signatures in momentum space have been established, the direct real-space visualization of its defining rotational symmetry breaking has remained a missing cornerstone. Here, using scanning tunnelling microscopy, we present atomic-scale imaging of electronic states in the candidate material CsV2Se2O. We directly visualize the hallmark symmetry breaking in the form of unidirectional electronic patterns tied to magnetic domain walls and spin defects, as well as elliptical charging rings surrounding those defects. These observed electronic states are all linked to the underlying alternating spin texture. Our work provides the foundational real-space evidence for altermagnetism, moving the field from theoretical and momentum-space probes to direct visual confirmation; thereby opening a path to explore how this unconventional magnetic order couples to and controls other quantum electronic states.

[cond-mat updates on arXiv.org] Insights Into Radiation Damage in YBa$_2$Cu$_3$O$_{7-\delta}$ From Machine-Learned Interatomic Potentials

cond-mat updates on arXiv.org — Thu, 01 Jan 2026 05:00:00 GMT

arXiv:2512.24430v1 Announce Type: new Abstract: Accurate prediction of radiation damage in YBa$_2$Cu$3$O${7-\delta}$ (YBCO) is essential for assessing the performance of high-temperature superconducting (HTS) tapes in compact fusion reactors. Existing empirical interatomic potentials have been used to model radiation damage in stoichiometric YBCO, but fail to describe oxygen-deficient compositions, which are ubiquitous in industrial Rare-Earth Barium Copper Oxide conductors and strongly influence superconducting properties. In this work, we demonstrate that modern machine-learned interatomic potentials enable predictive modelling of radiation damage in YBCO across a wide range of oxygen stoichiometries, with higher fidelity than previous empirical models. We employ two recently developed approaches: an Atomic Cluster Expansion (ACE) potential and a tabulated Gaussian Approximation Potential (tabGAP). Both models accurately reproduce Density Functional Theory (DFT) energies, forces, and threshold displacement energy distributions, providing a reliable description of atomic-scale collision processes. Molecular dynamics simulations of 5 keV cascades predict enhanced peak defect production and recombination relative to a widely used empirical potential, indicating different cascade evolution. By explicitly varying oxygen deficiency, we show that total defect production depends only weakly on stoichiometry, offering insight into the robustness of radiation damage processes in oxygen-deficient YBCO. Finally, fusion-relevant 300 keV cascade simulations reveal amorphous regions with dimensions comparable to the superconducting coherence length, consistent with electron microscopy observations of neutron-irradiated HTS tapes. These results establish machine-learned interatomic potentials as efficient and predictive tools for investigating radiation damage in YBCO across relevant compositions and irradiation conditions.

[cond-mat updates on arXiv.org] Upscaling from ab initio atomistic simulations to electrode scale: The case of manganese hexacyanoferrate, a cathode material for Na-ion batteries

cond-mat updates on arXiv.org — Thu, 01 Jan 2026 05:00:00 GMT

arXiv:2512.24816v1 Announce Type: new Abstract: We present a generalizable scale-bridging computational framework that enables predictive modeling of insertion-type electrode materials from atomistic to device scales. Applied to sodium manganese hexacyanoferrate, a promising cathode material for grid-scale sodium-ion batteries, our methodology employs an active-learning strategy to train a Moment Tensor Potential through iterative hybrid grand-canonical Monte Carlo--molecular dynamics sampling, robustly capturing configuration spaces at all sodiation levels. The resulting machine learning interatomic potential accurately reproduces experimental properties including volume expansion, operating voltage, and sodium concentration-dependent structural transformations, while revealing a four-order-of-magnitude difference in sodium diffusivity between the rhombohedral (sodium-rich) and tetragonal (sodium-poor) phases at 300 K. We directly compute all critical parameters -- temperature- and concentration-dependent diffusivities, interfacial and strain energies, and complete free-energy landscapes -- to feed them into pseudo-2D phase-field simulations that predict phase-boundary propagation and rate-dependent performances across electrode length scales. This multiscale workflow establishes a blueprint for rational computational design of next-generation insertion-type materials, such as battery electrode materials, demonstrating how atomistic insights can be systematically translated into continuum-scale predictions.

[cond-mat updates on arXiv.org] SSCHA-based evolutionary crystal structure prediction at finite temperatures with account for quantum nuclear motion

cond-mat updates on arXiv.org — Thu, 01 Jan 2026 05:00:00 GMT

arXiv:2512.24849v1 Announce Type: new @@ -14,7 +73,22 @@ Abstract: Floquet engineering offers an unparalleled platform for realizing nove Abstract: A Chain of Springs and Masses (CSM) model is used in the interpretation of molecular dynamics (MD) simulations of movement of atoms in orientated FCC crystals. A force of dynamic origin is found that is perpendicular to the direction of the external shear pressure. It is proportional to the square of the applied pressure; It causes breaking of axial symmetry for propagation of transverse acoustic waves. It leads to a non-linear elastic response of crystals and to chaotic patterns in the motion of atoms. We provide an analytical derivation of an effective atomistic 3D potential for interaction between crystallographic layers. The potential is found to possess a component that has an anharmonic threefold axial symmetry around one direction. It reduces to the H{\'e}non-Heinen potential in a 2D cross-section, leading to mathematically rich, complex dynamic features. Results of simulation predict displacements of atoms that are inconsistent with the static theory of elasticity that may have been overlooked in experiments.

[cond-mat updates on arXiv.org] GoodRegressor: A General-Purpose Symbolic Regression Framework for Physically Interpretable Materials Modeling

cond-mat updates on arXiv.org — Thu, 01 Jan 2026 05:00:00 GMT

arXiv:2510.18325v4 Announce Type: replace Abstract: Symbolic regression offers a promising route toward interpretable machine learning, yet existing methods suffer from poor predictability and computational intractability when exploring large expression spaces. I introduce GoodRegressor, a general-purpose C++-based framework that resolves these limitations while preserving full physical interpretability. By combining hierarchical descriptor construction, interaction discovery, nonlinear transformations, statistically rigorous model selection, and stacking ensemble, GoodRegressor efficiently explores symbolic model spaces such as $1.44 \times 10^{457}$, $5.99 \times 10^{124}$, and $4.20 \times 10^{430}$ possible expressions for oxygen-ion conductors, NASICONs, and superconducting oxides, respectively. Across these systems, it produces compact equations that surpass state-of-the-art black-box models and symbolic regressors, improving $R^2$ by $4 \sim 40$ %. The resulting expressions reveal physical insights, for example, into oxygen-ion transport through coordination environment and lattice flexibility. Independent ensemble runs yield nearly identical regressed values and the identical top-ranked candidate, demonstrating high reproducibility. With scalability up to $10^{4392}$ choices without interaction terms, GoodRegressor provides a foundation for general-purpose interpretable machine intelligence.

[cond-mat updates on arXiv.org] Thermodynamic Phase Stability, Structural, Mechanical, Optoelectronic, and Thermoelectric Properties of the III-V Semiconductor AlSb for Energy Conversion Applications

cond-mat updates on arXiv.org — Thu, 01 Jan 2026 05:00:00 GMT

arXiv:2512.22277v2 Announce Type: replace Abstract: This study presents a first principles investigation of the structural, thermodynamic, electronic, optical and thermoelectric properties of aluminum antimonide (AlSb) in its cubic (F-43m) and hexagonal (P63mc) phases. Both structures are dynamically and mechanically stable, as confirmed by phonon calculations and the Born Huang criteria. The lattice constants obtained using the SCAN and PBEsol functionals show good agreement with experimental data. The cubic phase exhibits a direct band gap of 1.66 to 1.78 eV, while the hexagonal phase shows a band gap of 1.48 to 1.59 eV, as confirmed by mBJ and HSE06 calculations. Under external pressure, the band gap decreases in the cubic phase and increases in the hexagonal phase due to different s p orbital hybridization mechanisms. The optical absorption coefficient reaches 1e6 cm-1, which is comparable to or higher than values reported for other III V semiconductors. The Seebeck coefficient exceeds 1500 microV per K under intrinsic conditions, and the thermoelectric performance improves above 600 K due to enhanced phonon scattering and lattice anharmonicity. The calculated formation energies (-1.316 eV for F-43m and -1.258 eV for P63mc) confirm that the cubic phase is thermodynamically more stable. The hexagonal phase exhibits higher anisotropy and lower lattice stiffness, which is favorable for thermoelectric applications. These results demonstrate the strong interplay between crystal symmetry, phonon behavior and charge transport, and provide useful guidance for the design of AlSb based materials for optoelectronic and energy conversion technologies.

[cond-mat updates on arXiv.org] CrystalDiT: A Diffusion Transformer for Crystal Generation

cond-mat updates on arXiv.org — Thu, 01 Jan 2026 05:00:00 GMT

arXiv:2508.16614v3 Announce Type: replace-cross -Abstract: We present CrystalDiT, a diffusion transformer for crystal structure generation that achieves state-of-the-art performance by challenging the trend of architectural complexity. Instead of intricate, multi-stream designs, CrystalDiT employs a unified transformer that imposes a powerful inductive bias: treating lattice and atomic properties as a single, interdependent system. Combined with a periodic table-based atomic representation and a balanced training strategy, our approach achieves 8.78% SUN (Stable, Unique, Novel) rate on MP-20, substantially outperforming recent methods including FlowMM (4.21%) and MatterGen (3.66%). Notably, CrystalDiT generates 63.28% unique and novel structures while maintaining comparable stability rates, demonstrating that architectural simplicity can be more effective than complexity for materials discovery. Our results suggest that in data-limited scientific domains, carefully designed simple architectures outperform sophisticated alternatives that are prone to overfitting.

[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Challenges in Transitioning from Pellet to Practical Argyrodite-Based All-Solid-State Batteries

ACS Energy Letters: Latest Articles (ACS Publications) — Wed, 31 Dec 2025 12:54:38 GMT

ACS Energy Letters

DOI: 10.1021/acsenergylett.5c03368

[ScienceDirect Publication: Journal of Energy Storage] Hollow nanofiber ion conductor protective layer on Zn metal anode for long-term stable zinc battery

ScienceDirect Publication: Journal of Energy Storage — Wed, 31 Dec 2025 12:41:33 GMT

Publication date: 10 February 2026

Source: Journal of Energy Storage, Volume 146

Author(s): Mengfei Sun, Zumin Zhang, Yang Su, Wensheng Yu, Xiangting Dong, Dongtao Liu, Xinlu Wang, Gaopeng Li, Jinxian Wang

[ScienceDirect Publication: Journal of Energy Storage] Alkaline-compatible polyaniline/graphene negative electrode for ultrahigh-energy all-solid-state asymmetric supercapacitors

ScienceDirect Publication: Journal of Energy Storage — Wed, 31 Dec 2025 12:41:33 GMT

Publication date: 10 February 2026

Source: Journal of Energy Storage, Volume 146

Author(s): Aizhen Xu, Li Yin, Shaoqing Zhang, Zhiyi Zhao, Wenna Lv, Yuanyu Zhu, Yujun Qin

[Wiley: Angewandte Chemie International Edition: Table of Contents] Machine Learning–Guided Solvation Engineering of Chiral Viologens for Durable Neutral Aqueous Organic Flow Batteries

Wiley: Angewandte Chemie International Edition: Table of Contents — Wed, 31 Dec 2025 06:56:15 GMT

Angewandte Chemie International Edition, EarlyView.

[Nature Communications] Scalable photonic reservoir computing for parallel machine learning tasks

Nature Communications — Wed, 31 Dec 2025 00:00:00 GMT

Nature Communications, Published online: 31 December 2025; doi:10.1038/s41467-025-67983-z

Neuromorphic computing processes data faster and with less energy than electronics. Here, authors demonstrate a reconfigurable photonic reservoir computer that performs multiple machine learning tasks in parallel at ultrafast rates while using extremely low energy per operation.

[Nature Machine Intelligence] Harnessing the power of single-cell large language models with parameter-efficient fine-tuning using scPEFT

Nature Machine Intelligence — Wed, 31 Dec 2025 00:00:00 GMT

Nature Machine Intelligence, Published online: 31 December 2025; doi:10.1038/s42256-025-01170-z

He et al. present a parameter-efficient fine-tuning method for single-cell language models that improves performance on unseen diseases, treatments and cell types.

[Nature Machine Intelligence] Assessing the potential of deep learning for protein–ligand docking

Nature Machine Intelligence — Wed, 31 Dec 2025 00:00:00 GMT

Nature Machine Intelligence, Published online: 31 December 2025; doi:10.1038/s42256-025-01160-1

Morehead et al. introduce the benchmark PoseBench and evaluate the strengths and limitations of current AI-based protein–ligand docking and structure prediction methods.

[ChemRxiv] Sensing the Acidity of Hydrogen Bond Networks

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

The reactivity of hydrogen bond networks (HBNs) is critical to many chemical and biological scenarios. When the HBNs are under constraint, hydrogen bond strength and acidity are affected significantly. HBNs exhibit cooperativity, where connections formed in one part of the HBN influence its behavior elsewhere. We combined experimental and computational approaches to examine the growth of the HBNs of water and hexafluoroisopropanol (HFIP), constrained by an aprotic cosolvent. We independently employed vibrational frequency shift of an acetonitrile probe, 1H NMR chemical shift of an aniline probe, and molecular dynamics with machine learning interatomic potentials, to demonstrate the increase in the hydrogen bond strength with the growth of the HBNs. Finally, using vibrational spectroscopy of a titratable probe, we established that not only the hydrogen bond strength, but also the acidity of HFIP is affected by the changes in the network geometry. These results enable the engineering and measurement of HBNs in confined environments with tailored acidity.

[ChemRxiv] Thiol-bearing tertiary alkylammonium chloride for regulation of PbI2 excess in FAPbI3 perovskite solar cells

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

One of the key strategies for record photovoltaic efficiencies in metal halide perovskite solar cells is the addition of PbI2 excess in a stoichiometric perovskite solution which controls crystallization, passivates defects and induces a preferred orientation in the perovskite layer. However, residual PbI2, typically found in the perovskite layer after crystallization, generates non-radiative recombination centres and promotes ion migration under light and heating stress, thus accelerating performance loss. To mitigate the above issues, a common strategy is the post-deposition of organic ammonium salts which interact in situ with residual PbI2. Here, we adopt a multifunctional alkylammonium salt, 2-diethylaminoethanethiol hydrochloride (DEAET), in which both the thiol (–SH) and protonated tertiary amine groups can strongly bind to PbI₂. Upon deposition of DEAET on top of FAPbI3 film, we show that DEAT decreases the percentage of residual PbI2 by 40% and totally eliminates Pb0. These two effects lead to enhanced radiative recombination, proving a net passivation effect, while chemical analysis (FTIR and liquid-state NMR) explains that this is due to strong interactions between tertiary protonated ammonium (-NH+) and thiol (-SH) groups of DEAT with under-coordinated Pb2+. The stabilization of FAPbI3 black phase along with the establishment of a solid barrier to impede the infiltration of moisture into the perovskite layer over time lead to enhanced operational stability for the as-fabricated solar cells. The encouraging findings of this study lay the foundation for the utilization of tertiary ammonium thiol-based salts as efficient agents for interface engineering in perovskite solar cells.

cond-mat updates on arXiv.org — Thu, 01 Jan 2026 05:00:00 GMT

[Wiley: Advanced Functional Materials: Table of Contents] Machine Learning‐Augmented Piezoelectric‐Ferroelectret Nanogenerators for Highly Sensitive Respiration Monitoring in Wearable Healthcare

Wiley: Advanced Functional Materials: Table of Contents — Wed, 31 Dec 2025 14:25:09 GMT

Advanced Functional Materials, EarlyView.

[Wiley: Advanced Functional Materials: Table of Contents] Ultralong‐Cycling‐Life Sodium Metal Capacitors Enabled by Hetero‐Salt Additive Strategy with NaF/LiF Hybrid Solid Electrolyte Interphase

Wiley: Advanced Functional Materials: Table of Contents — Wed, 31 Dec 2025 13:54:32 GMT

Advanced Functional Materials, EarlyView.

[ACS Energy Letters: Latest Articles (ACS Publications)] [ASAP] Challenges in Transitioning from Pellet to Practical Argyrodite-Based All-Solid-State Batteries

ACS Energy Letters: Latest Articles (ACS Publications) — Wed, 31 Dec 2025 12:54:38 GMT

ACS Energy Letters

DOI: 10.1021/acsenergylett.5c03368

[Wiley: Angewandte Chemie International Edition: Table of Contents] Machine Learning–Guided Solvation Engineering of Chiral Viologens for Durable Neutral Aqueous Organic Flow Batteries

Wiley: Angewandte Chemie International Edition: Table of Contents — Wed, 31 Dec 2025 06:56:15 GMT

Angewandte Chemie International Edition, EarlyView.

[Nature Communications] Domain oriented universal machine learning potential enables fast exploration of chemical space of battery electrolytes

Nature Communications — Wed, 31 Dec 2025 00:00:00 GMT

Nature Communications, Published online: 31 December 2025; doi:10.1038/s41467-025-67982-0

Efficient modeling of battery electrolytes is limited by the accuracy-cost trade-off. Here, authors develop a universal machine learning potential to accurately calculate transport and solvation properties across a broad chemical space.

[Nature Machine Intelligence] Harnessing the power of single-cell large language models with parameter-efficient fine-tuning using scPEFT

Nature Machine Intelligence — Wed, 31 Dec 2025 00:00:00 GMT

Nature Machine Intelligence, Published online: 31 December 2025; doi:10.1038/s42256-025-01170-z

He et al. present a parameter-efficient fine-tuning method for single-cell language models that improves performance on unseen diseases, treatments and cell types.

[Nature Machine Intelligence] Assessing the potential of deep learning for protein–ligand docking

Nature Machine Intelligence — Wed, 31 Dec 2025 00:00:00 GMT

Nature Machine Intelligence, Published online: 31 December 2025; doi:10.1038/s42256-025-01160-1

Morehead et al. introduce the benchmark PoseBench and evaluate the strengths and limitations of current AI-based protein–ligand docking and structure prediction methods.

[ChemRxiv] A Review on Computational Insights into +Anion Exchange Membranes for Water +Electrolysis to Generate Green Hydrogen

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

Anion exchange membranes (AEMs) have received a lot of attention in electrochemical energy storage +and conversion systems and have become a better choice to generate green hydrogen than their proton +exchange membrane counterparts owing to the non-acidic working conditions as well as the use of nonprecious metal catalytic electrodes. Albeit the safe operating conditions as well as the use of non-precious +metals, the ion conductivity and technology readiness level of AEMs are significantly lower than their PEM +counterparts. It is well accepted that the key factors that drive their performance are anion conductivity, +water uptake and chemical stability. However, there exist several other parameters that influence not +only the KPI’s but also the overall electrochemical performance of AEMs. The objective of this study is to +compile the various physical processes in an anion exchange membrane water electrolyser and focus on +the dominant ones that define the performance of these membranes. We further propose appropriate +methods to predict the KPIs using multiscale approach. In this report, we elaborately discuss the abovementioned points with a note that, this area still requires substantial research and profound +understanding from both experimental and computational point of view. In this article, a comprehensive +review on molecular dynamics simulation methods for anion exchange membranes is extensively +discussed. We also briefly touch upon the data analytics-based approaches to predict ion conductivity in these +membranes.

[ChemRxiv] Revealing amyloid-β peptide isoforms, including post-translationally modified species, using electrochemical profiling with a dual-electrode set-up

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

The amyloid-β (Aβ) peptides are crucial biomarkers for the diagnosis of Alzheimer's disease (AD), the most common neurodegenerative disease. The high diversity of the Aβ family provides a significant challenge for recognizing various Aβ forms, which may differ by a single amino acid or a post-translational modification. Such variation at the N-terminus of Aβ peptides leads to changes in their properties associated with typical AD biomolecular mechanisms, such as aggregation or generation of reactive oxygen species (ROS). In this work, a novel method for discriminating Aβ peptides with physiologically occurring truncations and modifications at their N-termini, based on the electrochemical profiling of their Cu(II) complexes, is presented. A dual-electrode set-up incorporating both glassy carbon and gold electrodes, together with Differential Pulse Voltammetry (DPV), was employed to generate unique electrochemical profiles, which were subsequently analyzed using chemometric techniques, including Principal Component Analysis (PCA) for data exploration, and Partial Least Squares Discriminant Analysis (PLS-DA) for classification. By combining electrochemical measurements with machine learning algorithms for pattern recognition, we successfully differentiated the studied Aβ forms, Aβ1-16, Aβ3-16, Aβpyr3-16, Aβ4-16, Aβ5-16, Aβ11-16, and Aβpyr11-16. The integration of machine learning not only enhances detection accuracy but also identifies subtle patterns that could support early-stage diagnostics. These findings support the ongoing development of analytical strategies that seek to improve the detection range and accuracy of Aβ peptides identification in Alzheimer’s disease research.

[ChemRxiv] Sensing the Acidity of Hydrogen Bond Networks

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

[ChemRxiv] Thiol-bearing tertiary alkylammonium chloride for regulation of PbI2 excess in FAPbI3 perovskite solar cells

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

[ChemRxiv] LAMMPS-ANI: Large Scale Molecular Dynamics Simulations with ANI Neural Network Potential

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

Machine Learning Interatomic Potentials (MLIPs), trained with Quantum Mechanics data, can model potential energy surfaces for molecular systems with very high accuracy and extreme speedups compared to reference quantum calculations, offering a powerful tool for studying complex chemical and biological @@ -27,83 +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.

[Nature Communications] Domain oriented universal machine learning potential enables fast exploration of chemical space of battery electrolytes

Nature Communications — Wed, 31 Dec 2025 00:00:00 GMT

Nature Communications, Published online: 31 December 2025; doi:10.1038/s41467-025-67982-0

[ChemRxiv] Revealing amyloid-β peptide isoforms, including post-translationally modified species, using electrochemical profiling with a dual-electrode set-up

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

[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%.

[ChemRxiv] A Review on Computational Insights into -Anion Exchange Membranes for Water -Electrolysis to Generate Green Hydrogen

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

Anion exchange membranes (AEMs) have received a lot of attention in electrochemical energy storage -and conversion systems and have become a better choice to generate green hydrogen than their proton -exchange membrane counterparts owing to the non-acidic working conditions as well as the use of nonprecious metal catalytic electrodes. Albeit the safe operating conditions as well as the use of non-precious -metals, the ion conductivity and technology readiness level of AEMs are significantly lower than their PEM -counterparts. It is well accepted that the key factors that drive their performance are anion conductivity, -water uptake and chemical stability. However, there exist several other parameters that influence not -only the KPI’s but also the overall electrochemical performance of AEMs. The objective of this study is to -compile the various physical processes in an anion exchange membrane water electrolyser and focus on -the dominant ones that define the performance of these membranes. We further propose appropriate -methods to predict the KPIs using multiscale approach. In this report, we elaborately discuss the abovementioned points with a note that, this area still requires substantial research and profound -understanding from both experimental and computational point of view. In this article, a comprehensive -review on molecular dynamics simulation methods for anion exchange membranes is extensively -discussed. We also briefly touch upon the data analytics-based approaches to predict ion conductivity in these -membranes.

[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

[ScienceDirect Publication: Acta Materialia] Inverse Design of High-Performance Glasses Through an Encoder-Decoder Machine Learning Approach Toward Materials Discovery: Application to Oxynitride Glasses

ScienceDirect Publication: Acta Materialia — Tue, 30 Dec 2025 18:31:09 GMT

Publication date: Available online 29 December 2025

Source: Acta Materialia

Author(s): Alexis Duval, Eric Robin, Patrick Houizot, Tanguy Rouxel

[ScienceDirect Publication: Journal of Energy Storage] Machine learning-aided prediction and optimization of specific capacitance in functionalized GO-based Mn/Fe co-doped Bi₂O₃ nanocomposites

ScienceDirect Publication: Journal of Energy Storage — Tue, 30 Dec 2025 12:42:28 GMT

Publication date: 10 February 2026

Source: Journal of Energy Storage, Volume 146

[ScienceDirect Publication: Journal of Energy Storage] Time-resolved impedance spectroscopy analysis of stable lithium iron phosphate cathode with enhanced electronic/ionic conductivity and ion diffusion characteristics

ScienceDirect Publication: Journal of Energy Storage — Tue, 30 Dec 2025 12:42:28 GMT

Publication date: 10 February 2026

Source: Journal of Energy Storage, Volume 146

Author(s): Jiguo Tu, Yan Li, Libo Chen, Dongbai Sun

[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.

[cond-mat updates on arXiv.org] Thermodynamic Phase Stability, Structural, Mechanical, Optoelectronic, and Thermoelectric Properties of the III-V Semiconductor AlSb for Energy Conversion Applications

cond-mat updates on arXiv.org — Tue, 30 Dec 2025 05:00:00 GMT

arXiv:2512.22277v1 Announce Type: new -Abstract: This study presents a first principles investigation of the structural, thermodynamic, electronic, optical and thermoelectric properties of aluminum antimonide (AlSb) in its cubic (F-43m) and hexagonal (P63mc) phases. Both structures are dynamically and mechanically stable, as confirmed by phonon calculations and the Born Huang criteria. The lattice constants obtained using the SCAN and PBEsol functionals show good agreement with experimental data. The cubic phase exhibits a direct band gap of 1.66 to 1.78 eV, while the hexagonal phase shows a band gap of 1.48 to 1.59 eV, as confirmed by mBJ and HSE06 calculations. Under external pressure, the band gap decreases in the cubic phase and increases in the hexagonal phase due to different s p orbital hybridization mechanisms. The optical absorption coefficient reaches 1e6 cm-1, which is comparable to or higher than values reported for other III V semiconductors. The Seebeck coefficient exceeds 1500 microV per K under intrinsic conditions, and the thermoelectric performance improves above 600 K due to enhanced phonon scattering and lattice anharmonicity. The calculated formation energies (-1.316 eV for F-43m and -1.258 eV for P63mc) confirm that the cubic phase is thermodynamically more stable. The hexagonal phase exhibits higher anisotropy and lower lattice stiffness, which is favorable for thermoelectric applications. These results demonstrate the strong interplay between crystal symmetry, phonon behavior and charge transport, and provide useful guidance for the design of AlSb based materials for optoelectronic and energy conversion technologies.

[cond-mat updates on arXiv.org] The Role of THz Phonons in the Ionic Conduction Mechanism of $Li_7La_3Zr_2O_{12}$ Polymorphs

cond-mat updates on arXiv.org — Tue, 30 Dec 2025 05:00:00 GMT

arXiv:2512.22427v1 Announce Type: new -Abstract: Superionic conduction in solid-state materials is governed not only by static factors, such as structure and composition, but also by dynamic interactions between the mobile ion and the crystal lattice. Specifically, the dynamics of lattice vibrations, or phonons, have attracted interest because of their hypothesized ability to facilitate superionic conduction. However, direct experimental measurement of the role of phonons in ionic conduction is challenging due to the fast intrinsic timescales of ion hopping and the difficulty of driving relevant phonon modes, which often lie in the low-energy THz regime. To overcome these limitations, we use laser-driven ultrafast impedance spectroscopy (LUIS). LUIS resonantly excites phonons using a THz field and probes ion hopping with picosecond time resolution. We apply LUIS to understand the dynamical role of phonons in $Li_7La_3Zr_2O_{12}$ (LLZO). When in its cubic phase (c-LLZO), this garnet-type solid electrolyte has an ionic conductivity two orders of magnitude greater than its tetragonal phase (t-LLZO). T-LLZO is characterized by an ordered and filled $Li^+$ sublattice necessitating synchronous ion hopping. In contrast, c-LLZO is characterized by a disordered and vacancy-rich $Li^+$ sublattice, and has a conduction mechanism dominated by single hops. We find that, upon excitation of phonons in the 0.5-7.5 THz range, the impedance of t-LLZO experiences a longer ion hopping decay signal in comparison to c-LLZO. The results suggest that phonon-mediated ionic conduction by THz modes may lead to larger ion displacements in ordered and fully occupied mobile ion sublattices as opposed to those that are disordered and vacancy-rich. Overall, this work highlights the interplay between static and dynamic factors that enables improved ionic conductivity in otherwise poorly conducting inorganic solids.

[cond-mat updates on arXiv.org] Thermally Activated Non-Affine Rearrangements in Amorphous Glass: Emergence of Intrinsic Length Scales

cond-mat updates on arXiv.org — Tue, 30 Dec 2025 05:00:00 GMT

arXiv:2512.22530v1 Announce Type: new -Abstract: We present a systematic study of temperature-driven nonaffine rearrangements in a model amorphous solid across the full thermodynamic range, from a high-temperature liquid, through supercooled and sub-glass regimes, into deep glassy states. The central result is a quantitative characterisation of the componentwise nonaffine residual displacements, obtained by subtracting local affine maps from particle displacements. For each state point the tails of the probability distributions of these nonaffine components display clear exponential decay; linear fits to the logarithm of the tail region yield characteristic nonaffine length scales {\xi}NA,x and {\xi}NA,y , which quantify the spatial extent of purely nonaffine, local rearrangements. To compare with other length scales, we compute van Hove distributions Gx(ux), Gy (uy ) which capture the full particle displacement field (coherent affine-like motion plus residuals). A robust, key finding is that the van Hove length scale consistently exceeds the filtered nonaffine length scale, i.e. {\xi}VH > {\xi}NA, across all temperatures, state points, and densities we studied. The nonaffine length {\xi}NA quantifies the distance over which complex deformation occurs, specifically nonlinear and anharmonic responses, irreversible (plastic) rearrangements, topological non-recoverable particle rearrangements, and other residual motions that cannot be represented by a local affine map. Moreover, near equality of {\xi}NA,x and {\xi}NA,y in all conditions provides further evidence that nonaffine rearrangements propagate isotropically under thermally driven deformation in contrast to externally driven shear.

[cond-mat updates on arXiv.org] Fast and accurate Fe-H machine-learning interatomic potential for elucidating hydrogen embrittlement mechanisms

cond-mat updates on arXiv.org — Tue, 30 Dec 2025 05:00:00 GMT

arXiv:2512.22934v1 Announce Type: new -Abstract: Understanding the mechanisms of hydrogen embrittlement (HE) is essential for advancing next-generation high-strength steels, thereby motivating the development of highly accurate machine-learning interatomic potentials (MLIPs) for the Fe-H binary system. However, the substantial computational expense associated with existing MLIPs has limited their applicability in practical, large-scale simulations. In this study, we construct a new MLIP within the Performant Implementation of the Atomic Cluster Expansion (PACE) framework, trained on a comprehensive HE-related dataset generated through a concurrent-learning strategy. The resulting potential achieves density functional theory-level accuracy in reproducing a wide range of lattice defects in alpha-Fe and their interactions with hydrogen, including both screw and edge dislocations. More importantly, it accurately captures the deformation and fracture behavior of nanopolycrystals containing hydrogen-segregated general grain boundaries-phenomena not explicitly represented in the training data. Despite its high fidelity, the developed potential requires computational resources only several tens of times greater than empirical potentials and is more than an order of magnitude faster than previously reported MLIPs. By delivering both a high-precision and computationally efficient potential, as well as a generalizable methodology for constructing such models, this study significantly advances the atomic-scale understanding of HE across a broad range of metallic materials.

[cond-mat updates on arXiv.org] A Simple and Efficient Non-DFT-Based Machine Learning Interatomic Potential to Simulate Titanium MXenes

cond-mat updates on arXiv.org — Tue, 30 Dec 2025 05:00:00 GMT

arXiv:2512.23063v1 Announce Type: new -Abstract: Titanium MXenes are two-dimensional inorganic structures composed of titanium and carbon or nitrogen elements, with distinctive electronic, thermal and mechanical properties. Despite the extensive experimental investigation, there is a paucity of computational studies at the level of classical molecular dynamics (MD). As demonstrated in a preceding study, known MD potentials are not capable of fully reproducing the structure and elastic properties of every titanium MXene. In this study, we present a simply trained, but yet efficient, non-density functional theory-based machine learning interatomic potential (MLIP) capable of simulating the structure and elastic properties of titanium MXenes and bulk titanium carbide and nitride with precision comparable to DFT calculations. The training process for the MLIP is delineated herein, in conjunction with a series of dynamical tests. Limitations of the MLIP and steps towards improving its efficacy to simulate titanium MXenes are discussed.

[cond-mat updates on arXiv.org] Phosphorus-based lubricant additives on iron with Machine Learning Interatomic Potentials

cond-mat updates on arXiv.org — Tue, 30 Dec 2025 05:00:00 GMT

arXiv:2512.23583v1 Announce Type: new -Abstract: Phosphorus-based lubricant additives are used for protecting metallic contacts under boundary lubrication by forming surface films that reduce wear and friction. Despite their importance, the molecular mechanisms driving their friction-reducing effects remain unclear, especially for phosphate esters, whose molecular structure critically impact tribological behavior. In this study, we use machine learning-based molecular dynamics simulations to investigate the tribological performance of three representative phosphorus-based additives, Dibutyl Hydrogen Phosphite (DBHP), Octyl Acid Phosphate (OAP), and Methyl Polyethylene Glycol Phosphate (mPEG-P), on iron surfaces. The mPEG-P family is further analyzed by varying esterification degree and chain length. DBHP exhibits the lowest friction and largest interfacial separation, resulting from steric hindrance and tribochemical reactivity, as indicated by P-O bond cleavage and enhanced O-Fe interactions. In contrast, OAP and mPEG-P monoesters produce higher friction due to limited steric protection and reduced resistance to shear, leading to partial loss of surface coverage under extreme conditions. Within the mPEG-P family, multi-ester and longer-chain molecules significantly lower friction by maintaining larger separations, demonstrating that steric effects can outweigh surface reactivity under severe confinement. Overall, these results provide atomistic insights into how molecular architecture controls additive performance and support the design of phosphorus-based lubricants combining reactive anchoring with optimized steric structures for durable, low-friction interfaces.

[cond-mat updates on arXiv.org] Expert-Grounded Automatic Prompt Engineering for Extracting Lattice Constants of High-Entropy Alloys from Scientific Publications using Large Language Models

cond-mat updates on arXiv.org — Tue, 30 Dec 2025 05:00:00 GMT

arXiv:2512.22130v1 Announce Type: cross -Abstract: Large language models (LLMs) have shown promise for scientific data extraction from publications, but rely on manual prompt refinement. We present an expert-grounded automatic prompt optimization framework that enhances LLM entity extraction reliability. Using high-entropy alloy lattice constant extraction as a testbed, we optimized prompts for Claude 3.5 Sonnet through feedback cycles on seven expert-annotated publications. Despite a modest optimization budget, recall improved from 0.27 to > 0.9, demonstrating that a small, expert-curated dataset can yield significant improvements. The approach was applied to extract lattice constants from 2,267 publications, yielding data for 1,861 compositions. The optimized prompt transferred effectively to newer models: Claude 4.5 Sonnet, GPT-5, and Gemini 2.5 Flash. Analysis revealed three categories of LLM mistakes: contextual hallucination, semantic misinterpretation, and unit conversion errors, emphasizing the need for validation protocols. These results establish feedback-guided prompt optimization as a low-cost, transferable methodology for reliable scientific data extraction, providing a scalable pathway for complex LLM-assisted research tasks.

[cond-mat updates on arXiv.org] HalluMat: Detecting Hallucinations in LLM-Generated Materials Science Content Through Multi-Stage Verification

cond-mat updates on arXiv.org — Tue, 30 Dec 2025 05:00:00 GMT

arXiv:2512.22396v1 Announce Type: cross -Abstract: Artificial Intelligence (AI), particularly Large Language Models (LLMs), is transforming scientific discovery, enabling rapid knowledge generation and hypothesis formulation. However, a critical challenge is hallucination, where LLMs generate factually incorrect or misleading information, compromising research integrity. To address this, we introduce HalluMatData, a benchmark dataset for evaluating hallucination detection methods, factual consistency, and response robustness in AI-generated materials science content. Alongside this, we propose HalluMatDetector, a multi-stage hallucination detection framework that integrates intrinsic verification, multi-source retrieval, contradiction graph analysis, and metric-based assessment to detect and mitigate LLM hallucinations. Our findings reveal that hallucination levels vary significantly across materials science subdomains, with high-entropy queries exhibiting greater factual inconsistencies. By utilizing HalluMatDetector verification pipeline, we reduce hallucination rates by 30% compared to standard LLM outputs. Furthermore, we introduce the Paraphrased Hallucination Consistency Score (PHCS) to quantify inconsistencies in LLM responses across semantically equivalent queries, offering deeper insights into model reliability.

[cond-mat updates on arXiv.org] Multi-AI Agent Framework Reveals the "Oxide Gatekeeper" in Aluminum Nanoparticle Oxidation

cond-mat updates on arXiv.org — Tue, 30 Dec 2025 05:00:00 GMT

arXiv:2512.22529v1 Announce Type: cross -Abstract: Aluminum nanoparticles (ANPs) are among the most energy-dense solid fuels, yet the atomic mechanisms governing their transition from passivated particles to explosive reactants remain elusive. This stems from a fundamental computational bottleneck: ab initio methods offer quantum accuracy but are restricted to small spatiotemporal scales (< 500 atoms, picoseconds), while empirical force fields lack the reactive fidelity required for complex combustion environments. Herein, we bridge this gap by employing a "human-in-the-loop" closed-loop framework where self-auditing AI Agents validate the evolution of a machine learning potential (MLP). By acting as scientific sentinels that visualize hidden model artifacts for human decision-making, this collaborative cycle ensures quantum mechanical accuracy while exhibiting near-linear scalability to million-atom systems and accessing nanosecond timescales (energy RMSE: 1.2 meV/atom, force RMSE: 0.126 eV/Angstrom). Strikingly, our simulations reveal a temperature-regulated dual-mode oxidation mechanism: at moderate temperatures, the oxide shell acts as a dynamic "gatekeeper," regulating oxidation through a "breathing mode" of transient nanochannels; above a critical threshold, a "rupture mode" unleashes catastrophic shell failure and explosive combustion. Importantly, we resolve a decades-old controversy by demonstrating that aluminum cation outward diffusion, rather than oxygen transport, dominates mass transfer across all temperature regimes, with diffusion coefficients consistently exceeding those of oxygen by 2-3 orders of magnitude. These discoveries establish a unified atomic-scale framework for energetic nanomaterial design, enabling the precision engineering of ignition sensitivity and energy release rates through intelligent computational design.

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

cond-mat updates on arXiv.org — Tue, 30 Dec 2025 05:00:00 GMT

arXiv:2512.23010v1 Announce Type: 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 democratizes access to state-of-the-art computational methodologies, accelerating hypothesis testing, pre-screening, and exploratory research for both new and experienced practitioners.

[cond-mat updates on arXiv.org] Uniqueness of Replica-symmetric Saddle Point for Ising Perceptron

cond-mat updates on arXiv.org — Tue, 30 Dec 2025 05:00:00 GMT

arXiv:2512.23195v1 Announce Type: cross -Abstract: We study the replica-symmetric saddle point equations for the Ising perceptron with Gaussian disorder and margin $\kappa\ge 0$. We prove that for each $\kappa\ge 0$ there is a critical capacity $\alpha_c(\kappa)=\frac{2}{\pi\,\mathbb E[(\kappa-Z)_+^2]}$, where $Z$ is a standard normal and $(x)_+=\max\{x,0\}$, such that the saddle point equation has a unique solution for $\alpha\in(0,\alpha_c(\kappa))$ and has no solution when $\alpha\ge \alpha_c(\kappa)$. When $\alpha\uparrow \alpha_c(\kappa)$ and $\kappa>0$, the replica-symmetric free energy at this solution diverges to $-\infty$. In the zero-margin case $\kappa=0$, Ding and Sun obtained a conditional uniqueness result, with one step verified numerically. Our argument gives a fully analytic proof without computer assistance. We used GPT-5 to help develop intermediate proof steps and to perform sanity-check computations.

[cond-mat updates on arXiv.org] SimplySQS: An Automated and Reproducible Workflow for Special Quasirandom Structure Generation with ATAT

cond-mat updates on arXiv.org — Tue, 30 Dec 2025 05:00:00 GMT

arXiv:2510.18020v3 Announce Type: replace -Abstract: The special quasirandom structure (SQS) method is widely used for modeling disordered materials under periodic boundary conditions, with the ATAT mcsqs module being one of the most established implementations. However, SQS generation with mcsqs typically relies on manual preparation of input files, ad hoc execution scripts, and post-processing steps, which introduces user-dependent errors and limits reproducibility. Here, we present SimplySQS (https://simplysqs.com), an automated and reproducible workflow for SQS generation that is delivered through an online, interactive interface. SimplySQS guides users through structure import, compositional and supercell definition, and cluster parameter selection, while automatically generates all required ATAT input files and a single all-in-one execution script that encapsulates the complete search process. By standardizing input preparation, execution, and output analysis, the framework minimizes errors associated with manual file handling and enables consistent reproducibility of SQS searches. The workflow is demonstrated on the Pb1-xSrxTiO3 (PSTO, including PbTiO3 (PTO) and SrTiO3 (STO)) perovskite system. SQSs spanning the entire concentration range were generated using a single automated bash script produced by SimplySQS, after which all resulting structures were subjected to geometry optimization using a universal machine-learning interatomic potential (MACE MATPES-r2SCAN-0). This approach reliably reproduced the experimentally observed cubic-to-tetragonal transition near x = 0.5, with lattice parameters deviating by less than 1 % in the cubic region (x > 0.5) and less than 4 % in the tetragonal region (x < 0.5). Overall, SimplySQS transforms SQS generation with ATAT into intuitive, reproducible, and systematic framework for modeling disordered materials.

[cond-mat updates on arXiv.org] Benchmarking Universal Machine Learning Interatomic Potentials on Elemental Systems

cond-mat updates on arXiv.org — Tue, 30 Dec 2025 05:00:00 GMT

arXiv:2512.20230v2 Announce Type: replace -Abstract: The rapid emergence of universal Machine Learning Interatomic Potentials (uMLIPs) has transformed materials modeling. However, a comprehensive understanding of their generalization behavior across configurational space remains an open challenge. In this work, we introduce a benchmarking framework to evaluate both the equilibrium and far-from-equilibrium performance of state-of-the-art uMLIPs, including three MACE-based models, MatterSim, and PET-MAD. Our assessment utilizes Equation-of-State (EOS) tests to evaluate near-equilibrium properties, such as bulk moduli and equilibrium volumes, alongside extensive Minima Hopping (MH) structural searches to probe the global Potential Energy Surface (PES). Here, we assess universality within the fundamental limit of unary (elemental) systems, which serve as a necessary baseline for broader chemical generalization and provide a framework that can be systematically extended to multicomponent materials. We find that while most models exhibit high accuracy in reproducing equilibrium volumes for transition metals, significant performance gaps emerge in alkali and alkaline earth metal groups. Crucially, our MH results reveal a decoupling between search efficiency and structural fidelity, highlighting that smoother learned PESs do not necessarily yield more accurate energetic landscapes.

[cond-mat updates on arXiv.org] Emergence of Friedel-like oscillations from Lorenz dynamics in walking droplets

cond-mat updates on arXiv.org — Tue, 30 Dec 2025 05:00:00 GMT

arXiv:2512.21049v2 Announce Type: replace -Abstract: Friedel oscillations are spatially decaying density modulations near localized defects and are a hallmark of quantum systems. Walking droplets provide a macroscopic platform for hydrodynamic quantum analogs, and Friedel-like oscillations were recently observed in droplet-defect scattering through wave-mediated speed modulation [P.~J.~S\'aenz \textit{et al.}, \textit{Sci.\ Adv.} \textbf{6}, eay9234 (2020)]. Here we show that Friedel-like oscillatory statistics can also arise from a purely local dynamical mechanism, revealed using a minimal Lorenz model description of a walking droplet viewed as an active particle with internal degrees of freedom. A localized defect directly perturbs the particle's internal dynamical state, generating underdamped velocity oscillations that give rise to oscillatory ensemble position statistics. This work opens new avenues for hydrodynamic quantum analogs by revealing how quantum-like statistics can emerge from local internal-state dynamics of active particles.

[cond-mat updates on arXiv.org] Functional Renormalization Group flows as diffusive Hamilton-Jacobi-type equations

cond-mat updates on arXiv.org — Tue, 30 Dec 2025 05:00:00 GMT

arXiv:2512.05973v2 Announce Type: replace-cross -Abstract: In order to find reliable and efficient numerical approximation schemes, we suggest to identify the Functional Renormalization Group flow equations of one-particle irreducible two-point functions as Hamilton-Jacobi(-Bellman)-type partial differential equations. Based on this reformulation and reinterpretation we adopt a numerical scheme for the solution of field-dependent flow equations as nonlinear partial differential equations. We demonstrate this novel approach by first applying it to a simple fermion-boson system in zero spacetime dimensions - which itself presents as an interesting playground for method development. Afterwards, we show, how the gained insights can be transferred to more interesting problems: One is the bosonic $\mathbb{Z}_2$-symmetric model in three Euclidean dimensions within a truncation that involves the field-dependent effective potential and field-dependent wave-function renormalization. The other example is the $(1 + 1)$-dimensional Gross-Neveu model within a truncation that involves a field-dependent potential and a field-dependent fermion mass/Yukawa coupling at nonzero temperature, chemical potential, and finite fermion number.

[ChemRxiv] Probabilistic Forecasting for Coarse-Grained Molecular Dynamics

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

Coarse-grained molecular dynamics enables access to long length and time scales but often fails to reproduce atomistic kinetics when memory effects and slow collective motions are important. We introduce Probabilistic Forecasting for Coarse-Graining (PFCG), a machine learning framework that learns stochastic coarse-grained equations of motion directly from atomistic trajectories by formulating coarse-grained simulation as a probabilistic time-series forecasting problem with both Markovian and non-Markovian contributions. PFCG incorporates non-Markovian effects through finite trajectory history without requiring explicit memory kernels or learned effective potentials. We apply PFCG to miniproteins and polyalanine peptides and evaluate both configurational and dynamical fidelity using free energy surfaces, autocorrelation functions, and transition timescales from Markov state models. Across all systems, non-Markovian PFCG models significantly improve dynamical agreement with atomistic simulation relative to Markovian baselines while also maintaining excellent agreement with stationary distributions. These results highlight the importance of inductive biases at the level of equations of motion and establish PFCG as a complementary approach to existing machine learning-based coarse-graining methods for modeling biomolecular processes.

[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

[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.

[ChemRxiv] Hybrid ChemBERTa and DFT Machine Learning Framework for Predicting Enantioselectivity in Organosilanes Mediated Carbonyl Reduction Reactions

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

Predicting the small yet meaningful enantioselectivity differences in organosilanes mediated carbonyl reductions remains challenging because multiple steric, electronic and conformational factors interact in ways that traditional mechanistic rules struggle to describe. To address this challenge, this study integrates quantum chemical de- scriptors with ChemBERTa based molecular embeddings to construct a machine learn- ing framework capable of capturing these subtle structure selectivity relationships. A systematic model comparison was performed using a carefully curated dataset, where LightGBM demonstrated the highest predictive accuracy with RMSE value of 8.381 for %ee. SHAP based interpretability analysis clarified which steric, electronic and geometrical descriptors most strongly influence facial selectivity across these reduc- tions. Together, this hybrid computational approach provides both predictive power and mechanistic insight offering a practical tool for understanding selectivity trends.

[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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[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.

[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

[ScienceDirect Publication: Journal of Catalysis] Protonation dynamics of confined ethanol–water mixtures in H-ZSM-5 from machine learning-driven metadynamics

ScienceDirect Publication: Journal of Catalysis — Mon, 29 Dec 2025 18:30:33 GMT

Publication date: February 2026

Source: Journal of Catalysis, Volume 454

Author(s): Princy Jarngal, Benjamin A. Jackson, Simuck F. Yuk, Difan Zhang, Mal-Soon Lee, Maria Cristina Menziani, Vassiliki-Alexandra Glezakou, Roger Rousseau, GiovanniMaria Piccini

[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: Small: Table of Contents] Elucidating the Sigmoidal Adsorption Behavior of Xenon in Flexible Hofmann‐Type MOFs Through Experiments and Molecular Dynamics with Machine Learning Potentials

Wiley: Small: Table of Contents — Mon, 29 Dec 2025 08:31:34 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.

[cond-mat updates on arXiv.org] Upper bounds on the separation efficiency of diffusiophoresis

cond-mat updates on arXiv.org — Mon, 29 Dec 2025 05:00:00 GMT

arXiv:2512.21758v1 Announce Type: new -Abstract: The separation of colloidal particles from fluids is essential to ensure a safe global supply of drinking water yet, in the case of microscopic particles, it remains a highly energy-intensive process when using traditional filtration methods. Water cleaning through diffusiophoresis $\unicode{x2014}$spontaneous colloid migration in chemical gradients$\unicode{x2014}$ effectively circumvents the need for physical filters, representing a promising alternative. This separation process is typically realized in internal flows where a cross-channel electrolyte gradient drives particle accumulation at walls, with colloid separation slowly increasing in the streamwise direction. However, the maximum separation efficiency, achieved sufficiently downstream as diffusiophoretic migration (driving particle accumulation) is balanced by Brownian motion (inducing diffusive spreading), has not yet been characterized. In this work, we develop a theory to predict this upper bound, and derive the colloid separation efficiency by analyzing the asymptotic structure of the governing equations. We find that the mechanism by which the chemical permeates in the channel, as well as the reaction kinetics governing its dissociation into ions, play key roles in the process. Moreover, we identify four distinct regimes in which separation is controlled by different scaling laws involving a Damk\"ohler and a P\'eclet number, which measure the ratio of reaction kinetics to ion diffusion and of diffusiophoresis to Brownian motion, respectively. We also confirm the scaling of one of these regimes using microfluidic experiments where separation is driven by CO$_\text{2}$ gradients. Our results shed light on pathways towards new, more efficient separations, and are also applicable to quantify colloidal accumulation in the presence of chemical gradients in more general situations.

[cond-mat updates on arXiv.org] Temperature- and Pressure-Dependent Vibrational Properties and Phase Stability of Pristine and Sb-Doped Vacancy-Ordered Double Perovskite

cond-mat updates on arXiv.org — Mon, 29 Dec 2025 05:00:00 GMT

arXiv:2512.21810v1 Announce Type: new -Abstract: Understanding lattice dynamics and structural transitions in vacancy-ordered double perovskites is crucial for developing lead-free optoelectronic materials, yet the role of dopants in modulating these properties remains poorly understood. We investigate Sb-doped Cs$_2$TiCl$_6$ through temperature-dependent Raman spectroscopy (4 to 273 K), high-pressure studies (0 to 30 GPa), powder XRD, and photoluminescence measurements. Sb doping dramatically improves phase purity, eliminating all impurity-related Raman modes present in pristine and Bi-doped samples while retaining only the three fundamental [TiCl$_6$]$^{2-}$ octahedral vibrations. This enhanced purity reveals a previously unobserved structural phenomenon: Sb-doped samples (2\% doped and 3\%) incorporated) exhibit a sharp anomaly at 100 K marked by the emergence of a new Raman mode M$_1$ at 314--319 cm$^{-1}$ and abrupt changes in the temperature coefficient $\chi$ (factor of 2--8$\times$ change) and anharmonic constant $A$ across this threshold. No such transition occurs in pristine Cs$_2$TiCl$_6$, indicating Sb-dopant-induced order-disorder transformation. The enhanced phonon anharmonicity in Sb-doped samples directly manifests in photoluminescence: self-trapped exciton emission at 448 nm shows 19\% broader FWHM (164.73 nm) compared to Bi-doped samples (138.2 nm), confirming stronger electron-phonon coupling. High-pressure measurements reveal structural robustness to 30 GPa with no phase transitions. These findings establish that strategic Sb doping not only improves material quality but also enables a novel low-temperature structural transition, providing fundamental insights into dopant-mediated phase control in vacancy-ordered perovskites for next-generation optoelectronic devices.

[cond-mat updates on arXiv.org] Incorporating rank-free coupling and external field via an amplitude-only modulated spatial photonic Ising machine

cond-mat updates on arXiv.org — Mon, 29 Dec 2025 05:00:00 GMT

arXiv:2512.21587v1 Announce Type: cross -Abstract: Ising machines have emerged as effective solvers for combinatorial optimization problems, such as NP-hard problems, machine learning, and financial modeling. Recent spatial photonic Ising machines (SPIMs) excel in multi-node optimization and spin glass simulations, leveraging their large-scale and fully connected characteristics. However, existing laser diffraction-based SPIMs usually sacrifice time efficiency or spin count to encode high-rank spin-spin coupling and external fields, limiting their scalability for real-world applications. Here, we demonstrate an amplitude-only modulated rank-free spatial photonic Ising machine (AR-SPIM) with 200 iterations per second. By re-formulating an arbitrary Ising Hamiltonian as the sum of Hadamard products, followed by loading the corresponding matrices/vectors onto an aligned amplitude spatial light modulator and digital micro-mirrors device, we directly map a 797-spin Ising model with external fields (nearly 9-bit precision, -255 to 255) into an incoherent light field, eliminating the need for repeated and auxiliary operations. Serving as encoding accuracy metrics, the linear coefficient of determination and Pearson correlation coefficient between measured light intensities and Ising Hamiltonians exceed 0.9800, with values exceed 0.9997 globally. The AR-SPIM achieves less than 0.3% error rate for ground-state search of biased Max-cut problems with arbitrary ranks and weights, enables complex phase transition observations, and facilitates scalable spin counts for sparse Ising problems via removing zero-valued Hadamard product terms. This reconfigurable AR-SPIM can be further developed to support large-scale machine-learning training and deployed for practical applications in discrete optimization and quantum many-body simulations.

[cond-mat updates on arXiv.org] Accelerating Scientific Discovery with Autonomous Goal-evolving Agents

cond-mat updates on arXiv.org — Mon, 29 Dec 2025 05:00:00 GMT

arXiv:2512.21782v1 Announce Type: cross -Abstract: There has been unprecedented interest in developing agents that expand the boundary of scientific discovery, primarily by optimizing quantitative objective functions specified by scientists. However, for grand challenges in science , these objectives are only imperfect proxies. We argue that automating objective function design is a central, yet unmet requirement for scientific discovery agents. In this work, we introduce the Scientific Autonomous Goal-evolving Agent (SAGA) to amend this challenge. SAGA employs a bi-level architecture in which an outer loop of LLM agents analyzes optimization outcomes, proposes new objectives, and converts them into computable scoring functions, while an inner loop performs solution optimization under the current objectives. This bi-level design enables systematic exploration of the space of objectives and their trade-offs, rather than treating them as fixed inputs. We demonstrate the framework through a broad spectrum of applications, including antibiotic design, inorganic materials design, functional DNA sequence design, and chemical process design, showing that automating objective formulation can substantially improve the effectiveness of scientific discovery agents.

[cond-mat updates on arXiv.org] Correlated Terahertz phonon-ion interactions control ion conduction in a solid electrolyte

cond-mat updates on arXiv.org — Mon, 29 Dec 2025 05:00:00 GMT

arXiv:2305.01632v4 Announce Type: replace -Abstract: Ionic conduction in solids that exceeds 1 mS/cm is predicted to involve coupled phonon-ion interactions in the crystal lattice. Here, we use theory and experiment to measure the possible contribution of coupled phonon-ion hopping modes which enhance Li+ migration in Li0.5La0.5TiO3 (LLTO). The ab initio calculations predict that the targeted excitation of individual TiO6 rocking modes greatly increases the Li+ jump rate as compared to the excitation of vibrational modes associated with heating. Experimentally, coherently driving TiO6 rocking modes via terahertz (THz) illumination leads to a ten-fold decrease in the differential impedance compared to the excitation of acoustic and optical phonons. Additionally, we differentiate the ultrafast responses of LLTO due to ultrafast heating and THz-range vibrations using laser-driven spectroscopy (LUIS), finding a unique long-lived response for the THz-range excitation. These findings provide new insights into coupled ion migration mechanisms, indicating the important role of THz-range coupled phonon-ion hopping modes in enabling fast ion conduction at room temperature.

[cond-mat updates on arXiv.org] Fluctuation theorems with optical tweezers: theory and practice

cond-mat updates on arXiv.org — Mon, 29 Dec 2025 05:00:00 GMT

arXiv:2503.20894v2 Announce Type: replace -Abstract: Fluctuation theorems, such as the Jarzynski equality and the Crooks relation, are effective tools connecting non-equilibrium work statistics and equilibrium free energy differences. However, detailed hands-on, reproducible protocols for implementing and analyzing these relations in real experiments remain scarce. This tutorial provides an end-to-end workflow for measuring, validating, and applying fluctuation theorems using a single-beam optical tweezers setup. It introduces the foundational ideas and consolidates practical calibration (PSD-based trap stiffness and position sensitivity), protocol design (forward/reverse finite-time drives over multiple amplitudes and durations), and robust estimators for free-energy difference and dissipated work, highlighting finite-sampling and rare-event effects. We demonstrate the procedures using an extensive set of measured trajectories under different conditions and provide openly accessible datasets and Python code, enabling new researchers or educators to reproduce the results with minimal effort. Beyond pedagogical validation, we discuss how these recipes translate to broader soft-matter and mesoscopic contexts. By combining user-friendly instruments with clear and transparent analysis, this work promotes the education and reliable adoption of stochastic thermodynamic methods in the curricula of physics and chemistry, as well as among emerging research teams.

[cond-mat updates on arXiv.org] Rewards-based image analysis in microscopy

cond-mat updates on arXiv.org — Mon, 29 Dec 2025 05:00:00 GMT

arXiv:2502.18522v2 Announce Type: replace-cross -Abstract: Imaging and hyperspectral data analysis is central to progress across biology, medicine, chemistry, and physics. The core challenge lies in converting high-resolution or high-dimensional datasets into interpretable representations that enable insight into the underlying physical or chemical properties of a system. Traditional analysis relies on expert-designed, multistep workflows, such as denoising, feature extraction, clustering, dimensionality reduction, and physics-based deconvolution, or on machine learning (ML) methods that accelerate individual steps. Both approaches, however, typically demand significant human intervention, including hyperparameter tuning and data labeling. Achieving the next level of autonomy in scientific imaging requires designing effective reward-based workflows that guide algorithms toward best data representation for human or automated decision-making. Here, we discuss recent advances in reward-based workflows for image analysis, which capture key elements of human reasoning and exhibit strong transferability across various tasks. We highlight how reward-driven approaches enable a shift from supervised black-box models toward explainable, unsupervised optimization on the examples of Scanning Probe and Electron Microscopies. Such reward-based frameworks are promising for a broad range of applications, including classification, regression, structure-property mapping, and general hyperspectral data processing.

[ChemRxiv] A Protocol to Identify Large Language Model Use in Undergraduate Chemistry Essays

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

Large language modules (LLMs) such as Chat-GPT have been widely adopted by chemistry undergraduate university students as a learning tool, but few methods exist to measure the scope of their influence on essay writing. This report introduces a protocol based on monitoring the frequency of specific words used excessively by LLMs amongst a sample of around 1000 chemistry student essays between 2018 and 2025. More than 50 key words known to be favored by LLMs were found to have simultaneously increased in frequency amongst essays submitted during the last two years. When the lists of key words were applied to specific essays, the findings indicated 13-29% of essays submitted during 2025 relied heavily on text generated by LLMs. This protocol offers a simple method to generate an approximate scope of LLM uptake amongst a cohort, with potential applications in defining higher education AI policy and assessment strategies.

[ChemRxiv] Graph machine learning can estimate drug concentrations in whole blood from forensic screening results

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

LC-HRMS is widely used in forensic toxicology for broad-scope screening. When a newly emerging or rarely encountered compound is tentatively identified, toxicologists must decide whether it may be relevant to the case and, if so, quantify it. Acquiring reference material for quantification is costly and time-consuming. A fast semi-quantitative estimate would help prioritize only compounds above the toxic threshold. This study presents a machine-learning framework that estimates drug concentrations in whole blood using molecular structure information and LC-HRMS signal intensities. Using a dataset of 191 drugs spiked into whole blood at multiple concentration levels, we trained and evaluated several machine-learning models. Standard models, including random forests, achieved moderate performance. In contrast, a recently published graph neural network (GNN) leveraging atomic features and global molecular properties consistently produced the highest accuracy. Under cross-validation, the GNN predicted signal-to-concentration ratio for 79\% of all molecules, corresponding to concentration estimates between 50-200\% of true value. Toxicological thresholds often span multiple orders of magnitude, making this precision acceptable for application. The GNN model was additionally evaluated on an external benchmark dataset of ionization efficiencies (logIE), where it outperformed the current state of the art. Overall, the results demonstrate the feasibility of using graph-based machine learning to estimate drug concentrations in whole blood without reference material for prioritization. This is a practical and implementation-ready machine learning tool that can support decision-making in toxicological evaluation, particularly for newly emerging or rarely encountered drugs. The GNN model is open source and the dataset used for training and testing the models is publicly available.

[ChemRxiv] Confidently uncertain: Probabilistic machine learning to predict soil biotransformation half-lives

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

Predicting environmental persistence of chemicals from molecular structure is an open challenge, yet indispensable in regulatory screenings for potentially harmful substances and to advance the development of safe-and-sustainable-by-design chemicals. Limited availability of biotransformation half-life data makes persistence prediction difficult, and models typically struggle to generalize beyond their training data. Therefore, reliable estimates of prediction confidence are key. Here, we propose a probabilistic model for the prediction of soil biotransformation half-lives. A Gaussian Process Regressor was trained on 867 mean pesticide half-lives with data uncertainty estimates. Instead of single half-life values, our model predicts well-calibrated probability distributions that can be used to calculate a compound's probability of being persistent. Although the overall model performance remains moderate, the predictions are reliable when the confidence in the prediction is high. We applied our model to pesticide transformation products with unknown half-lives, and to a database of globally marketed chemicals. We show that our model is able to identify chemicals that are known, or suspected to be, persistent in the environment. The model is available as an online app (https://pepper-app.streamlit.app/) and as a Python library (pepper-lab) to meet diverse user needs.

[ChemRxiv] Characterizing PEDOT:PSS for Electronic Control of Stiffness

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

Active stiffness, the changing of material stiffness in response to an external stimulus, can be harnessed for mechanically adaptive implantable devices and dynamic cell culture substrates for mechanobiology investigations. Conducting polymer (CP)-based materials are capable of changing stiffness in response to an applied electrical potential: redox-driven changes in charge state lead to ion transport and subsequent swelling. However, conducting polymers have seldom been investigated for this purpose. In this study, the stiffness of poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) films as a function of applied potential is characterized. Electrochemical preconditioning is first defined and the proportionality of ion transport to voltage is identified. The maximum stiffness change observed over the potential range was found to be ~32.5% and changes of ~6.7-10.4% were found with 0.2 V increments. PEDOT:PSS films deviate in both their charge state and stiffness over a period of many hours after unbiasing. After unbiasing, PEDOT:PSS loses the transported charge over time and the stiffness changes by ~2.6-15.2% over 24 hrs. Finally, to evaluate feasibility for biomedical applications, assays involving active stiffness modulation determine that the process is cytocompatible. These characterizations highlight both the potential of CPs for active stiffness and identify areas for future optimization.

[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.

[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.

[ChemRxiv] ChemTSv3: Generalizing Molecular Design via Flexible Search Space Control

ChemRxiv — Sun, 28 Dec 2025 00:00:00 GMT

Recent advances in generative artificial intelligence have enabled in silico molecular design to become a powerful approach for exploring chemical space toward specific design goals across various domains. However, in actual design workflows, determining the appropriate generation conditions, including generative strategies and reward formulations, remains difficult; thus, trial-and-error adjustments are unavoidable. Yet, most existing generation methods implicitly fix the searchable chemical space defined by the molecular representation and generation method, which significantly limits the flexibility of practical design. This paper introduces ChemTSv3, an exploration framework based on reinforcement learning with a flexible architecture that accommodates diverse design scenarios for adaptive molecular design. Specifically, molecular representations are unified as nodes, enabling, for example, string-based encodings, molecular graphs, and protein sequences to be handled within the same logic. Molecular generations and editing operations are abstracted as transitions between nodes, allowing classical graph-based modifications, sequential mutations, and even large-language-model-driven transformations to be handled within the same formulation. ChemTSv3 supports dynamic switching among molecular representations and transition types, which enables the search strategy itself to adapt to the stage and nature of the design task. ChemTSv3 enables scalable molecular generation, from drug-like small molecules to proteins, and its switching capability supports realistic change in design scenarios while allowing efficient exploration.

[ChemRxiv] Machine learning the quantum topology of chemical bonds

ChemRxiv — Sun, 28 Dec 2025 00:00:00 GMT

The chemical bonding can be characterized within quantum chemical topology (QCT), which provides a real-space description via the topological analysis of the electron density and the electron localization function (ELF). While QCT has traditionally been applied on a molecule-by-molecule basis, recent advances in machine learning (ML) and the availability of large quantum chemical datasets now enable bonding analysis at scale. Here, we integrate ELF-based topological descriptors with ML to establish a data-driven framework for mapping chemical bonding across the QM9 dataset. Wavefunctions computed at the B3LYP/6-31G(2df,p) level were used to extract ELF basin populations, which were paired with geometric and bonding descriptors to construct a bond-level dataset. Statistical analysis revealed relationships between ELF populations, bond lengths, and local chemical environments. Regression models were trained to predict ELF electron populations directly from molecular geometry. The best performance was obtained when local environmental descriptors were included, reducing the prediction error by a factor of two relative to models using only bond type and bond length. These results demonstrate real-space bonding parameters, such as bond electron populations, can be predicted from simple structural features, enabling scalable and interpretable exploration of chemical bonding across large chemical spaces.

[ChemRxiv] StereoMolGraph: Stereochemistry-Aware Molecular and Reaction Graphs

ChemRxiv — Sun, 28 Dec 2025 00:00:00 GMT

Conventional molecular graphs often are unable to reliably encode stereochemistry, especially for symmetric molecules, non-tetrahedral centers, and transition states. To overcome this, we present StereoMolGraph, an open source Python library implementing a stereochemistry-aware graph representation for molecules and condensed graphs of reactions. Our method uses permutation invariant local stereodescriptors, grounded in group theory, to provide an extensible representation of chirality. Based on this we introduce methods allowingfor robust comparison of stereoisomers, including the identification of enantiomerism and diastereomerism, and supports the of fleeting stereochemistry in transition states. We demonstrate the library’s utility for complex organic molecules and metal complexes and analysis of distinct chiral reaction pathways. With RDKit interoperability and visualization features, StereoMolGraph offers a practical and transparent tool for advanced stereochemically aware chemoinformatics workflows.

[ChemRxiv] GPU-Accelerated Analytic Coulomb- and Exchange Gradients for Hartree Fock and Density Functional Theory

ChemRxiv — Sun, 28 Dec 2025 00:00:00 GMT

We present a GPU-accelerated software package for the evaluation of analytic two-electron energy and gradient contributions in Hartree-Fock (HF) and Density Functional theory (DFT) calculations. The implementation is provided as a Python library with a C++ backend, enabling straightforward integration into modern computational chemistry and drug-discovery workflows. The code supports single-point energy and nuclear gradient evaluations on both single- and multi-GPU systems, and employs MPI-based parallelization with dynamic load balancing in multi-node environments. -We report comprehensive benchmarks demonstrating favorable scaling with respect to system size, as well as high throughput for batched evaluations relevant to molecular dynamics, geometry optimization, and large-scale virtual screening. Parallel execution of a single system was carried out on up to 24 A100 GPUs. The implementation builds on optimized GPU-enabled variants of the LibintX and GauXC libraries to efficiently compute density-fitted Coulomb, semi-numerical exchange, and exchange--correlation contributions.

[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

[ScienceDirect Publication: Computational Materials Science] An enhanced machine learning and computational screening framework for synthesizable single-phase high-entropy spinel oxides

ScienceDirect Publication: Computational Materials Science — Fri, 26 Dec 2025 18:29:22 GMT

Publication date: February 2026

Source: Computational Materials Science, Volume 263

Author(s): Mahalaxmi Chandramohan, Hridhya Vinod, Meenal Deo

[ScienceDirect Publication: Materials Today Physics] Anomalous temperature evolution of lattice anharmonicity and thermal transport in orthorhombic SnSe

ScienceDirect Publication: Materials Today Physics — Fri, 26 Dec 2025 18:29:17 GMT

Publication date: Available online 26 December 2025

Source: Materials Today Physics

Author(s): Tianxiang Jiang, Wujie Qiu, Haijuan Zhang, Jifen Wang, Kunpeng Zhao, Huaqing Xie

[ScienceDirect Publication: Materials Today Physics] Machine learning aided bandgap and defect engineering of mixed halide perovskites for photovoltaic applications

ScienceDirect Publication: Materials Today Physics — Fri, 26 Dec 2025 18:29:17 GMT

Publication date: Available online 26 December 2025

Source: Materials Today Physics

Author(s): Ayush Kumar Pandey, Vivek Pandey, Abhishek Tewari

[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

[Wiley: Advanced Materials: Table of Contents] Plasma Design of Alloy‐Based Gradient Solid Electrolyte Interphase on Lithium Metal Anodes for Energy Storage

Wiley: Advanced Materials: Table of Contents — Fri, 26 Dec 2025 14:02:31 GMT

Advanced Materials, EarlyView.

[Wiley: Advanced Functional Materials: Table of Contents] Pixelation‐Free, Monolithic Iontronic Pressure Sensors Enabling Large‐Area Simultaneous Pressure and Position Recognition via Machine Learning

Wiley: Advanced Functional Materials: Table of Contents — Fri, 26 Dec 2025 14:01:16 GMT

Advanced Functional Materials, EarlyView.

[Wiley: Advanced Functional Materials: Table of Contents] Machine Learning‐Enhanced Smart Interactive Glove Utilizing Flexible Gradient Ridge Architecture Iontronic Capacitive Sensor

Wiley: Advanced Functional Materials: Table of Contents — Fri, 26 Dec 2025 09:52:42 GMT

Advanced Functional Materials, EarlyView.

[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.

[Nature Communications] Inferring fine-grained migration patterns across the United States

Nature Communications — Fri, 26 Dec 2025 00:00:00 GMT

Nature Communications, Published online: 26 December 2025; doi:10.1038/s41467-025-68019-2

This study releases a very high-resolution migration dataset that reveals trends that shape daily life: rising moves into high-income neighborhoods, racial gaps in upward mobility, and wildfire-driven moves.

[Communications Materials] Machine learning interatomic potential reveals hydrogen embrittlement origins at general grain boundaries in α-iron

Communications Materials — Fri, 26 Dec 2025 00:00:00 GMT

Communications Materials, Published online: 26 December 2025; doi:10.1038/s43246-025-01042-4

Hydrogen embrittlement is an issue that alloys used in the energy sector must overcome. Here, a machine learning interatomic potential for iron-hydrogen is reported, with large-scale molecular dynamics simulations revealing that hydrogen can suppress >111 < /2 dislocation emission at grain boundaries.

[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.

[ScienceDirect Publication: Solid State Ionics] Anion engineering enhances the electrochemical performance of Li₇P₃S₁₁ solid electrolyte

ScienceDirect Publication: Solid State Ionics — Thu, 25 Dec 2025 18:28:52 GMT

Publication date: 15 February 2026

Source: Solid State Ionics, Volume 435

Author(s): D. Narsimulu, Ramkumar Balasubramaniam, Kwang-Sun Ryu

[ScienceDirect Publication: Science Bulletin] Machine learning for spectral precision: a new horizon in radiative cooling material design

ScienceDirect Publication: Science Bulletin — Thu, 25 Dec 2025 18:28:50 GMT

Publication date: 30 December 2025

Source: Science Bulletin, Volume 70, Issue 24

Author(s): Xinpeng Hu, Mingxiang Liu, Xuemei Fu, Guangming Tao, Xiang Lu, Jinping Qu

[cond-mat updates on arXiv.org] Towards predictive atomistic simulations of SiC crystal growth

cond-mat updates on arXiv.org — Thu, 25 Dec 2025 05:00:00 GMT

arXiv:2512.20804v1 Announce Type: new -Abstract: Simulations of SiC crystal growth using molecular dynamics (MD) have become popular in recent years. They, however, simulate very fast deposition rates, to reduce computational costs. Therefore, they are more akin to surface sputtering, leading to abnormal growth effects, including thick amorphous layers and large defect densities. A recently developed method, called the minimum energy atomic deposition (MEAD), tries to overcome this problem by depositing the atoms directly at the minimum energy positions, increasing the time scale. - We apply the MEAD method to simulate SiC crystal growth on stepped C-terminated 4H substrates with 4{\deg} and 8{\deg} off-cut angle. We explore relevant calculations settings, such as amount of equilibration steps between depositions and influence of simulation cell sizes and bench mark different interatomic potentials. The carefully calibrated methodology is able to replicate the stable step-flow growth, which was so far not possible using conventional MD simulations. Furthermore, the simulated crystals are evaluated in terms of their dislocations, surface roughness and atom mobility. Our methodology paves the way for future high fidelity investigations of surface phenomena in crystal growth.

[cond-mat updates on arXiv.org] Emergence of Friedel-like oscillations from Lorenz dynamics in walking droplets

cond-mat updates on arXiv.org — Thu, 25 Dec 2025 05:00:00 GMT

arXiv:2512.21049v1 Announce Type: new -Abstract: Friedel oscillations are spatially decaying density modulations near localized defects and are a hallmark of quantum systems. Walking droplets provide a macroscopic platform for hydrodynamic quantum analogs, and Friedel-like oscillations were recently observed in droplet-defect scattering experiments through wave-mediated speed modulation [P.~J.~S\'aenz \textit{et al.}, \textit{Sci.\ Adv.} \textbf{6}, eay9234 (2020)]. Here we show that Friedel-like statistics can also arise from a purely local, dynamical mechanism, which we elucidate using a minimal Lorenz-like model of a walking droplet. In this model, a localized defect perturbs the particle's internal dynamical state, generating underdamped velocity oscillations that give rise to oscillatory ensemble position statistics. This attractor-driven, local mechanism opens new avenues for hydrodynamic quantum analogs based on active particles with internal degrees of freedom.

[cond-mat updates on arXiv.org] From cluster to nanocrystal: the continuous evolution and critical size of copper clusters revealed by machine learning

cond-mat updates on arXiv.org — Thu, 25 Dec 2025 05:00:00 GMT

arXiv:2512.21067v1 Announce Type: new -Abstract: The evolution of cluster structure with size and the critical size for the transition from cluster to nanocrystal have long been fundamental problems in nanoscience. Due to limitations of experimental technology and computational methods, the exploration of the continuous evolution of clusters towards nanocrystal is still a big challenge. Here, we proposed a machine learning force field (MLFF) that can generalize well to various copper systems ranging from small clusters to large clusters and bulk. The continuous evolution of copper clusters CuN towards nanocrystal was revealed by investigating clusters in a wide size range (7 <= N <= 17885) based on MLFF simulated annealing. For small CuN (N < 40), electron counting rule plays a major role in stability. For large CuN (N > 80), geometric magic number rule plays a dominant role and the evolution of clusters is based on the formation of more and more icosahedral shells. For medium size CuN (40 <= N <= 80), both rules contribute. The critical size from cluster to nanocrystal was calculated to be around 8000 atoms (about 6 nm in diameter). Our work terminates the long-term challenge in nanoscience, and lay the methodological foundation for subsequent research on other cluster systems.

[cond-mat updates on arXiv.org] Active Learning Guided Computational Discovery of 2D Materials with Large Spin Hall Conductivity

cond-mat updates on arXiv.org — Thu, 25 Dec 2025 05:00:00 GMT

arXiv:2512.21077v1 Announce Type: new -Abstract: Two-dimensional (2D) materials are promising candidates for next-generation spintronic devices due to their tunable properties and potential for efficient spin-charge interconversion. However, discovering materials with intrinsically high spin Hall conductivity (SHC) is hindered by the vast chemical space and expensive nature of conventional experimental and first-principles methods. In this work, we employ an active learning framework to accelerate the discovery of high-SHC 2D materials. Machine learning (ML) models were trained on SHC values computed from density functional theory calculations, incorporating the Kubo formalism via tight-binding Hamiltonians constructed from maximally localized Wannier functions, with explicit treatment of spin-orbit coupling. Starting from random but chemically diverse 24 2D systems, the dataset was expanded to 41 cases (from an overall pool of around 2000 materials) over three active learning loops using an expected improvement acquisition strategy. The ML technique successfully identified several high SHC candidates with the best candidate exhibiting a SHC of 271.52 (hbar/e) Ohm^-1, nearly 23 times higher than the top performer in the initial round. Beyond candidate discovery, several features such as orbital symmetry near the Fermi energy, types of atomic species, material composition, covalent radii, and electronegativity of constituent atoms were found to play critical role in shaping the spin Hall response in 2D systems. The data generated is made publicly available to facilitate further advances in 2D spintronics.

[cond-mat updates on arXiv.org] Symbolic regression for defect interactions in 2D materials

cond-mat updates on arXiv.org — Thu, 25 Dec 2025 05:00:00 GMT

arXiv:2512.20785v1 Announce Type: cross -Abstract: Machine learning models have become firmly established across all scientific fields. Extracting features from data and making inferences based on them with neural network models often yields high accuracy; however, this approach has several drawbacks. Symbolic regression is a powerful technique for discovering analytical equations that describe data, providing interpretable and generalizable models capable of predicting unseen data. Symbolic regression methods have gained new momentum with the advancement of neural network technologies and offer several advantages, the main one being the interpretability of results. In this work, we examined the application of the deep symbolic regression algorithm SEGVAE to determine the properties of two-dimensional materials with defects. Comparing the results with state-of-the-art graph neural network-based methods shows comparable or, in some cases, even identical outcomes. We also discuss the applicability of this class of methods in natural sciences.

[cond-mat updates on arXiv.org] MiST: Understanding the Role of Mid-Stage Scientific Training in Developing Chemical Reasoning Models

cond-mat updates on arXiv.org — Thu, 25 Dec 2025 05:00:00 GMT

arXiv:2512.21231v1 Announce Type: cross -Abstract: Large Language Models can develop reasoning capabilities through online fine-tuning with rule-based rewards. However, recent studies reveal a critical constraint: reinforcement learning succeeds only when the base model already assigns non-negligible probability to correct answers -- a property we term 'latent solvability'. This work investigates the emergence of chemical reasoning capabilities and what these prerequisites mean for chemistry. We identify two necessary conditions for RL-based chemical reasoning: 1) Symbolic competence, and 2) Latent chemical knowledge. We propose mid-stage scientific training (MiST): a set of mid-stage training techniques to satisfy these, including data-mixing with SMILES/CIF-aware pre-processing, continued pre-training on 2.9B tokens, and supervised fine-tuning on 1B tokens. These steps raise the latent-solvability score on 3B and 7B models by up to 1.8x, and enable RL to lift top-1 accuracy from 10.9 to 63.9% on organic reaction naming, and from 40.6 to 67.4% on inorganic material generation. Similar results are observed for other challenging chemical tasks, while producing interpretable reasoning traces. Our results define clear prerequisites for chemical reasoning training and highlight the broader role of mid-stage training in unlocking reasoning capabilities.

[cond-mat updates on arXiv.org] Dislocation-mediated short-range order evolution during thermomechanical processing

cond-mat updates on arXiv.org — Thu, 25 Dec 2025 05:00:00 GMT

arXiv:2508.13484v2 Announce Type: replace -Abstract: Thermomechanical processing alters the microstructure of metallic alloys through coupled plastic deformation and thermal exposure, with dislocation motion driving plasticity and microstructural evolution. Our previous work (Islam et al., 2025) showed that the same dislocation motion both creates and destroys chemical short-range order (SRO), driving alloys into far-from-equilibrium SRO states. However, the connection between this dislocation-mediated SRO evolution and processing parameters remains largely unexplored. Here, we perform large-scale atomistic simulations of thermomechanical processing of equiatomic TiTaVW to determine how temperature and strain rate control SRO via competing creation ($\Gamma$) and annihilation ($\lambda$) rates. The simulations employ systems containing 2.4 million atoms and utilize a machine learning interatomic potential optimized to capture chemical complexity through the motif-based sampling technique. Using information-theoretic metrics, we quantify that the magnitude and chemical character of SRO vary systematically with processing parameters. We identify two regimes: a low-temperature regime with weak strain-rate sensitivity, and a high-temperature regime in which reduced dislocation density and increased screw character amplify chemical bias and accelerate SRO formation. The resulting steady-state SRO is far-from-equilibrium and cannot be produced by equilibrium thermal annealing. Together, these results provide a mechanistic and predictive link between processing parameters, dislocation physics, and SRO evolution in chemically complex alloys.

[cond-mat updates on arXiv.org] Moir\'e spintronics: Emergent phenomena, material realization and machine learning accelerating discovery

cond-mat updates on arXiv.org — Thu, 25 Dec 2025 05:00:00 GMT

arXiv:2509.04045v2 Announce Type: replace -Abstract: Twisted van der Waals (vdW) materials have emerged as a promising platform for exploring exotic quantum phenomena and engineering novel material properties in two dimensions, potentially revolutionizing developments in spintronics. This Review provides an overview of recent progress on emerging moir\'e spintronics in twisted vdW materials, with a particular focus on two-dimensional magnetic materials. Following a brief introduction to the general features of twisted vdW materials, we discuss recent theoretical and experimental studies on stacking-dependent interlayer magnetism, non-collinear spin textures, moir\'e magnetic exchange interactions, moir\'e skyrmions and moir\'e magnons. We further highlight the potential of machine learning to accelerate the discovery and design of multifunctional materials for moir\'e spintronics. Finally, we conclude by addressing the most pressing challenges and potential opportunities in this rapidly expanding field.

[Nature Communications] Machine learning-assisted kinetic matching model for rational electrode design in aqueous zinc-ion batteries

Nature Communications — Thu, 25 Dec 2025 00:00:00 GMT

Nature Communications, Published online: 25 December 2025; doi:10.1038/s41467-025-67996-8

Aqueous zinc-ion batteries are safe and affordable but limited by incompatible electrode kinetics. Here, authors present a machine learning framework that resolves this mismatch, enabling the rational design of durable and stretchable zinc-ion batteries.

[ScienceDirect Publication: Artificial Intelligence Chemistry] Integrating Machine Learning with Electrochemical Sensors for Intelligent Food Safety Monitoring

ScienceDirect Publication: Artificial Intelligence Chemistry — Wed, 24 Dec 2025 18:29:56 GMT

Publication date: Available online 23 December 2025

Source: Artificial Intelligence Chemistry

Author(s): Aaryashree, Arti Devi

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

ScienceDirect Publication: Joule — Wed, 24 Dec 2025 18:29:19 GMT

Publication date: Available online 23 December 2025

Source: Joule

Author(s): Chenyang Zhang, Yuteng Jia, Bingqian Zhang, Qiangqiang Zhao, Ruida Xu, Shuping Pang, Han Wang, Stefaan De Wolf, Kai Wang

[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.

[cond-mat updates on arXiv.org] Turing Pattern Engineering Enables Kinetically Ultrastable yet Ductile Metallic Glasses

cond-mat updates on arXiv.org — Wed, 24 Dec 2025 05:00:00 GMT

arXiv:2512.20196v1 Announce Type: new -Abstract: Enhancing the kinetic stability of glasses often necessitates deepening thermodynamic stability, which typically compromises ductility due to increased structural rigidity. Decoupling these properties remains a critical challenge for functional applications. Here, we demonstrate that pattern engineering in metallic glasses (MGs) enables unprecedented kinetic ultrastability while retaining thermodynamic metastability and intrinsic plasticity. Through atomistic simulations guided by machine-learning interatomic potentials and replica-exchange molecular dynamics, we reveal that clustering oxygen contents, driven by reaction-diffusion-coupled pattern dynamics, act as localized pinning sites. These motifs drastically slow structural relaxation, yielding kinetic stability comparable to crystal-like ultrastable glasses while retaining an energetic as-cast state. Remarkably, the thermodynamically metastable state preserves heterogeneous atomic mobility, allowing strain delocalization under mechanical stress. By tailoring oxygen modulation via geometric patterning, we achieve an approximately 200 K increase in the onset temperature of the glass transition (Tonset) while maintaining fracture toughness akin to conventional MGs. This work establishes a paradigm of kinetic stabilization without thermodynamic compromise, offering a roadmap to additively manufacture bulk amorphous materials with combined hyperstability and plasticity.

[cond-mat updates on arXiv.org] Composition-Based Machine Learning for Screening Superconducting Ternary Hydrides from a Curated Dataset

cond-mat updates on arXiv.org — Wed, 24 Dec 2025 05:00:00 GMT

arXiv:2512.20228v1 Announce Type: new -Abstract: We present an ensemble machine-learning approach for composition-based, structure-agnostic screening of candidate superconductors among ternary hydrides under high pressure. Hydrogen-rich hydrides are known to exhibit high superconducting transition temperatures, and ternary or multinary hydrides can stabilize superconducting phases at reduced pressures through chemical compression. To systematically explore this vast compositional space, we construct an ensemble of 30 XGBoost regression models trained on a curated dataset of approximately 2000 binary and ternary hydride entries. The model ensemble is used to screen a broad set of A-B-H compositions at pressures of 100, 200, and 300 GPa, with screening outcomes evaluated statistically based on prediction consistency across ensemble members. This analysis highlights several high-scoring compositional systems, including Ca-Ti-H, Li-K-H, and Na-Mg-H, which were not explicitly included in the training dataset. In addition, feature-importance analysis indicates that elemental properties such as ionization energy and atomic radius contribute significantly to the learned composition-level trends in superconducting transition temperature. Overall, these results demonstrate the utility of ensemble-based machine learning as a primary screening tool for identifying promising regions of chemical space in superconducting hydrides.

[cond-mat updates on arXiv.org] Benchmarking Universal Interatomic Potentials on Elemental Systems

cond-mat updates on arXiv.org — Wed, 24 Dec 2025 05:00:00 GMT

arXiv:2512.20230v1 Announce Type: new -Abstract: The rapid emergence of universal Machine Learning Interatomic Potentials (uMLIPs) has transformed materials modeling. However, a comprehensive understanding of their generalization behavior across configurational space remains an open challenge. In this work, we introduce a benchmarking framework to evaluate both the equilibrium and far-from-equilibrium performance of state-of-the-art uMLIPs, including three MACE-based models, MatterSim, and PET-MAD. Our assessment utilizes Equation-of-State (EOS) tests to evaluate near-equilibrium properties, such as bulk moduli and equilibrium volumes, alongside extensive Minima Hopping (MH) structural searches to probe the global Potential Energy Surface (PES). Here, we assess universality within the fundamental limit of unary (elemental) systems, which serve as a necessary baseline for broader chemical generalization and provide a framework that can be systematically extended to multicomponent materials. We find that while most models exhibit high accuracy in reproducing equilibrium volumes for transition metals, significant performance gaps emerge in alkali and alkaline earth metal groups. Crucially, our MH results reveal a decoupling between search efficiency and structural fidelity, highlighting that smoother learned PESs do not necessarily yield more accurate energetic landscapes.

[cond-mat updates on arXiv.org] Iterative learning scheme for crystal structure prediction with anharmonic lattice dynamics

cond-mat updates on arXiv.org — Wed, 24 Dec 2025 05:00:00 GMT

arXiv:2512.20424v1 Announce Type: new -Abstract: First-principles based crystal structure prediction (CSP) methods have revealed an essential tool for the discovery of new materials. However, in solids close to displacive phase transitions, which are common in ferroelectrics, thermoelectrics, charge-density wave systems, or superconducting hydrides, the ionic contribution to the free energy and lattice anharmonicity become essential, limiting the capacity of CSP techniques to determine the thermodynamical stability of competing phases. While variational methods like the stochastic self-consistent harmonic approximation (SSCHA) accurately account for anharmonic lattice dynamics \emph{ab initio}, their high computational cost makes them impractical for CSP. Machine-learning interatomic potentials offer accelerated sampling of the energy landscape compared to purely first-principles approaches, but their reliance on extensive training data and limited generalization restricts practical applications. Here, we propose an iterative learning framework combining evolutionary algorithms, atomic foundation models, and SSCHA to enable CSP with anharmonic lattice dynamics. Foundation models enable robust relaxations of random structures, drastically reducing required training data. Applied to the highly anharmonic H$_3$S system, our framework achieves good agreement with the benchmarks based on density functional theory, accurately predicting phase stability and vibrational properties from 50 to 200 GPa. Importantly, we find that the statistical averaging in the SSCHA reduces the error in the free energy evaluation, avoiding the need for extremely high accuracy of machine-learning potentials. This approach bridges the gap between data efficiency and predictive power, establishing a practical pathway for CSP with anharmonic lattice dynamics.

[cond-mat updates on arXiv.org] Fundamentals of quantum Boltzmann machine learning with visible and hidden units

cond-mat updates on arXiv.org — Wed, 24 Dec 2025 05:00:00 GMT

arXiv:2512.19819v1 Announce Type: cross -Abstract: One of the primary applications of classical Boltzmann machines is generative modeling, wherein the goal is to tune the parameters of a model distribution so that it closely approximates a target distribution. Training relies on estimating the gradient of the relative entropy between the target and model distributions, a task that is well understood when the classical Boltzmann machine has both visible and hidden units. For some years now, it has been an obstacle to generalize this finding to quantum state learning with quantum Boltzmann machines that have both visible and hidden units. In this paper, I derive an analytical expression for the gradient of the quantum relative entropy between a target quantum state and the reduced state of the visible units of a quantum Boltzmann machine. Crucially, this expression is amenable to estimation on a quantum computer, as it involves modular-flow-generated unitary rotations reminiscent of those appearing in my prior work on rotated Petz recovery maps. This leads to a quantum algorithm for gradient estimation in this setting. I then specialize the setting to quantum visible units and classical hidden units, and vice versa, and provide analytical expressions for the gradients, along with quantum algorithms for estimating them. Finally, I replace the quantum relative entropy objective function with the Petz-Tsallis relative entropy; here I develop an analytical expression for the gradient and sketch a quantum algorithm for estimating it, as an application of a novel formula for the derivative of the matrix power function, which also involves modular-flow-generated unitary rotations. Ultimately, this paper demarcates progress in training quantum Boltzmann machines with visible and hidden units for generative modeling and quantum state learning.

[cond-mat updates on arXiv.org] Apparent inconsistency between Streda formula and Hall conductivity in reentrant integer quantum anomalous Hall effect in twisted MoTe$_2$

cond-mat updates on arXiv.org — Wed, 24 Dec 2025 05:00:00 GMT

arXiv:2506.10965v4 Announce Type: replace -Abstract: Recent experiments in twisted bilayer MoTe$_2$ (tMoTe$_2$) have uncovered a rich landscape of correlated phases. In this work, we investigate the reentrant integer quantum anomalous Hall (RIQAH) states reported by F. Xu, arXiv.2504.06972 which display a notable mismatch between the Hall conductivity measured via transport and that inferred from the Streda formula. We argue that this discrepancy can be explained if the RIQAH state is a quantum Hall bubble or Wigner crystal phase, analogous to similar well-established phenomena in two-dimensional (2D) GaAs quantum wells. While this explains the RIQAH state at filling $\nu = -0.63$, F. Xu et al. report that the other RIQAH state at $\nu = -0.7$ has a smaller slope, necessitating a different interpretation. We propose and substantiate with analysis of the experimental data that this discrepancy arises due to a nearby resistive peak masking the true slope. Furthermore, we identify this resistive peak as a signature of a phase transition near $\nu = -0.75$, possibly driven by a van Hove singularity. The anomalous Hall response and Landau fan evolution across this transition suggest a change in Fermi-surface topology and a metallic phase with a non quantized Hall response. These observations offer new insights into the nature of the RIQAH states and raise the possibility that the nearby superconducting phase may have a valley-imbalanced metal parent state.

[cond-mat updates on arXiv.org] Surprisingly High Redundancy in Electronic Structure Data

cond-mat updates on arXiv.org — Wed, 24 Dec 2025 05:00:00 GMT

arXiv:2507.09001v2 Announce Type: replace -Abstract: Accurate prediction of electronic structure underpins advances in chemistry, materials science, and condensed matter physics. In recent years, Machine Learning (ML) has enabled the development of powerful surrogate models that can enable the prediction of the ground state electron density and related properties at a fraction of the computational cost of conventional first principles simulations. Such ML models typically rely on massive datasets generated through expensive Kohn-Sham Density Functional Theory calculations. A key reason for relying on such large datasets is the lack of prior knowledge about which portions of the data are essential, and which are redundant. This study reveals significant redundancies in electronic structure datasets across various material systems, including molecules, simple metals, and chemically complex alloys -- challenging the notion that extensive datasets are essential for accurate ML-based electronic structure predictions. We demonstrate that even random pruning can substantially reduce dataset size with minimal loss in predictive accuracy. Furthermore, a state-of-the-art coverage-based pruning strategy that selects data across all learning difficulties, retains chemical accuracy and model generalizability using up to 100-fold less data, while reducing training time by threefold or greater. By contrast, widely used importance-based pruning methods, which eliminate easy-to-learn data, can catastrophically fail at higher pruning factors due to significant reduction in data coverage. This heretofore unexplored high redundancy in electronic structure data holds the potential to identify a minimal, essential dataset representative of each material class.

[cond-mat updates on arXiv.org] Domain Wall-mediated Interfacial Ferroelectric Switching

cond-mat updates on arXiv.org — Wed, 24 Dec 2025 05:00:00 GMT

arXiv:2508.11997v2 Announce Type: replace -Abstract: Interfacial ferroelectricity offers a promising platform for ultrafast, low-power memory devices. While previous studies have demonstrated the importance of domain wall in polarization switching, the coexistence of various domain wall types and their impact on polarization stability lacks fundamental understanding. By integrating first-principles calculations, machine learning methods, and experimental validations, we show that domain walls connect opposite polarization states and respond to out-of-plane electric field through polarization vector deviation, leading to inhomogeneous interlayer sliding and domain-wall migration. This mechanism bears clear resemblance to that in traditional ferroelectrics. Notably, different domain wall types result in distinct switching behaviors, which play a crucial role in determining the reversibility of polarization switching. We then propose strategies beyond ideal conditions to achieve non-volatile ferroelectric switching, which are supported by our experimental observations. These insights shed light on the microscopic switching mechanism in hexagonal interfacial ferroelectrics, offering guidance for future nanoelectronics applications.

[cond-mat updates on arXiv.org] Local Order Average-Atom Interatomic Potentials

cond-mat updates on arXiv.org — Wed, 24 Dec 2025 05:00:00 GMT

arXiv:2510.06459v4 Announce Type: replace -Abstract: This article describes an extension to the effective Average Atom (AA) method for random alloys to account for local ordering (short-range order) effects by utilizing information from partial radial distribution functions. The new Local-Order Average Atom (LOAA) method is rigorously derived based on statistical mechanics arguments and validated for non-stoichiometric binary 2D hexagonal crystals and 3D FeNiCr and NiAl alloys whose ground state is obtained through Monte Carlo sampling. Material properties for these alloys, and phase transformations for the NiAl system, computed from static and dynamic atomistic simulations using standard interatomic potentials (IPs) exhibit a strong dependence on local ordering that is captured by simulations with effective LOAA IPs, but not the original AA method. The advantage of LOAA is that it requires smaller system sizes to achieve statistically converged results and therefore enables the simulation of complex materials, such as high-entropy alloys, at a fraction of the computational cost of standard IPs.

[ScienceDirect Publication: eScience] Achieving high-voltage polymer-based all-solid-state batteries based on thermodynamic and kinetic degradation insights

ScienceDirect Publication: eScience — Wed, 24 Dec 2025 01:38:36 GMT

Publication date: January 2026

Source: eScience, Volume 6, Issue 1

Author(s): Xiaoyan Yu, Yun Su, Hang Su, Ruizhi Liu, Jingyi Qiu, Xiayu Zhu, Rui Wen, Hao Zhang, Xiaohui Rong, Yong-Sheng Hu, Gaoping Cao

[ChemRxiv] Chemical Reactivity Mapping: Exploring Chemical Reaction based on Digitalization of Small Molecules through Mayr’s Parameter

ChemRxiv — Wed, 24 Dec 2025 00:00:00 GMT

A new strategy for exploring novel reactions, utilizing Mayr’s reactivity parameters, is developed. We create a highly accurate prediction model of reactivity parameters using machine learning. Validating the reactivity scales in the reactions of isoquinoline and predicting the reactivity in commercially available chemicals lead to a new approach for exploring chemical reactions. Incorporating Mayr's reactivity parameters in both chemical reactivity mapping and chemical reaction space contributes to expedite the exploitation of reactions in small molecule syntheses.

[ChemRxiv] Guiding Ligand Selection in Copper-Catalyzed Cross Couplings via Principal Component Analysis

ChemRxiv — Wed, 24 Dec 2025 00:00:00 GMT

Copper-catalyzed Ullmann-type couplings are a promising alternative to Pd-catalyzed C-O and C-N bond formations. Recent discoveries of numerous second-generation ligands for Cu-mediated Ullmann-type cross-coupling reactions have broadened the scope to include less reactive aryl halides and nucleophiles and have also enabled lower copper and ligand loadings in these processes. However, in contrast to Pd chemistry, in-silico workflows for guiding systematic ligand selection from early-stage screening through optimization remain significantly less developed for Cu-catalyzed C-O and C-N bond coupling reactions. This report describes the application of Principal Component Analysis (PCA) for streamlining ligand selection in Cu-catalyzed Ullmann-type coupling reactions. It provides a “map” of more than 80 ligands used in Ullmann couplings and is designed to reveal reactivity trends and highlight areas of high and low yields, thereby facilitating prioritization of high-value experiments in both medicinal and process chemistry settings. Validation for C-N and C-O couplings is provided using literature datasets, along with the development and application of a PCA-guided screening set for Ullmann couplings.

[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: Advanced Materials: Table of Contents] Defect‐Driven Ionic Trap Construction and Interface Modulation for Rapid Li+ Kinetics in Composite Solid Electrolytes

Wiley: Advanced Materials: Table of Contents — Tue, 23 Dec 2025 08:43:35 GMT

Advanced Materials, EarlyView.

[Wiley: Angewandte Chemie International Edition: Table of Contents] A Smart Self‐Immobilization Magnetic Resonance Contrast Agent for Delayed Tumor Imaging In Vivo

Wiley: Angewandte Chemie International Edition: Table of Contents — Tue, 23 Dec 2025 08:21:09 GMT

Angewandte Chemie International Edition, Volume 64, Issue 52, December 22, 2025.

[Wiley: Angewandte Chemie International Edition: Table of Contents] Iron/NHC‐Catalyzed Regio‐ and Stereoselective 1,6‐Additions of Aliphatic Grignard Reagents to α,β,γ,δ‐Unsaturated Carbonyl Compounds: Asymmetric Variants with Chiral NHCs

Wiley: Angewandte Chemie International Edition: Table of Contents — Tue, 23 Dec 2025 08:21:09 GMT

Angewandte Chemie International Edition, Volume 64, Issue 52, December 22, 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.

[ScienceDirect Publication: Journal of Catalysis] Defect-reconstructed carbon nitride nanosheets for sacrificial agent-free H₂O₂ photosynthesis coupled with biomass-derived polyols valorization

ScienceDirect Publication: Journal of Catalysis — Tue, 23 Dec 2025 06:33:51 GMT

Publication date: February 2026

Source: Journal of Catalysis, Volume 454

Author(s): Yanmei Zheng, Qiang Xu, Jianghong Ouyang, Ziwei Hang, Jingde Li, Xinli Guo, Zupeng Chen

[ScienceDirect Publication: Journal of Energy Storage] A novel kind of activating agents for preparation of high specific surface area porous carbon towards zinc‑iodine batteries

ScienceDirect Publication: Journal of Energy Storage — Tue, 23 Dec 2025 06:33:34 GMT

Publication date: 1 February 2026

Source: Journal of Energy Storage, Volume 145

Author(s): Yun Zhou, Boyang Liu, Xuejin Chen, Junchen Chen, Chenglong Li, Xulai Xiao

[ScienceDirect Publication: Computational Materials Science] A molecular simulation study to the effect of T313 bonding agent and crystal defects on the performance of ammonium perchlorate oxidizer

ScienceDirect Publication: Computational Materials Science — Tue, 23 Dec 2025 06:33:32 GMT

Publication date: 30 January 2026

Source: Computational Materials Science, Volume 262

Author(s): Bin Yuan, Rui Zhu, Jianfa Chen, Kuai He

[ScienceDirect Publication: Journal of Catalysis] Mobility and sintering of silica-supported platinum clusters via reactive neural network potentials

ScienceDirect Publication: Journal of Catalysis — Tue, 23 Dec 2025 05:56:09 GMT

Publication date: January 2026

Source: Journal of Catalysis, Volume 453

Author(s): Tereza Benešová, Kristýna Pokorná, Andreas Erlebach, Christopher J. Heard

[ScienceDirect Publication: Journal of Catalysis] Machine learning–assisted discovery of chromium bis(2-pyridyl)amine catalysts for ethylene tri-/tetramerization

ScienceDirect Publication: Journal of Catalysis — Tue, 23 Dec 2025 05:56:09 GMT

Publication date: February 2026

Source: Journal of Catalysis, Volume 454

Author(s): Youcai Zhu, Yue Mu, Xiaoke Shi, Shu Yang, Li Sun, Zhen Liu

[ScienceDirect Publication: Journal of Catalysis] The influence of the organic residue and the solvent in the Schlenk equilibrium for Grignard reagents in THF. A molecular dynamics study with machine learning potentials

ScienceDirect Publication: Journal of Catalysis — Tue, 23 Dec 2025 05:56:09 GMT

Publication date: February 2026

Source: Journal of Catalysis, Volume 454

Author(s): Marco Bortoli, Sigbjørn Løland Bore, Odile Eisenstein, Michele Cascella

[ScienceDirect Publication: Acta Materialia] DuctGPT: A Generative Transformer for Forward Screening of Ductile Refractory Multi-Principal Element Alloys

ScienceDirect Publication: Acta Materialia — Tue, 23 Dec 2025 05:56:09 GMT

Publication date: 1 January 2026

Source: Acta Materialia, Volume 304

Author(s): Sai Pranav Reddy Guduru, Mkpe O. Kekung, Ryan T. Ott, Sougata Roy, Prashant Singh

[ScienceDirect Publication: Acta Materialia] On-the-fly machine learning of interatomic potentials for elastic property modeling in Al–Mg–Zr solid solutions

ScienceDirect Publication: Acta Materialia — Tue, 23 Dec 2025 05:56:09 GMT

Publication date: 15 February 2026

Source: Acta Materialia, Volume 305

Author(s): Lukas Volkmer, Leonardo Medrano Sandonas, Philip Grimm, Julia Kristin Hufenbach, Gianaurelio Cuniberti

[ScienceDirect Publication: Journal of Materiomics] Data-augmented machine learning models for oxynitride glasses via Wasserstein generative adversarial network with gradient penalty and content constraint

ScienceDirect Publication: Journal of Materiomics — Tue, 23 Dec 2025 05:55:54 GMT

Publication date: Available online 8 August 2025

Source: Journal of Materiomics

Author(s): Jing Tian, Yuan Li, Min Guan, Jijie Zheng, Jingyuan Chu, Yong Liu, Gaorong Han

[ScienceDirect Publication: Journal of Materiomics] Machine learning assisted τ_f value prediction of ABO₃-type microwave dielectric ceramics

ScienceDirect Publication: Journal of Materiomics — Tue, 23 Dec 2025 05:55:54 GMT

Publication date: Available online 8 August 2025

Source: Journal of Materiomics

Author(s): Mingyue Yang, Liangyu Mo, Jincheng Qin, Faqiang Zhang, Mingsheng Ma, Yongxiang Li, Zhifu Liu

[ScienceDirect Publication: Journal of Materiomics] Domain knowledge-assisted materials data anomaly detection towards constructing high-performance machine learning models

ScienceDirect Publication: Journal of Materiomics — Tue, 23 Dec 2025 05:55:54 GMT

Publication date: November 2025

Source: Journal of Materiomics, Volume 11, Issue 6

Author(s): Yue Liu, Shuchang Ma, Zhengwei Yang, Duo Wu, Yali Zhao, Maxim Avdeev, Siqi Shi

[ScienceDirect Publication: Journal of Materiomics] PTCDA/CuS cathode enabling stable sulfide-based all-solid-state batteries

ScienceDirect Publication: Journal of Materiomics — Tue, 23 Dec 2025 05:55:54 GMT

Publication date: January 2026

Source: Journal of Materiomics, Volume 12, Issue 1

Author(s): Zhixing Wan, Shuo Wang, Yahao Mu, Ruihua Zhou, Hang Liu, Tingwu Jin, Di Wu, Jianlong Xia, Ce-Wen Nan

[ScienceDirect Publication: Current Opinion in Solid State and Materials Science] Solid electrolyte-driven suppression of H2–H3 phase transition in Ni-rich cathodes for stable high-voltage cycling

ScienceDirect Publication: Current Opinion in Solid State and Materials Science — Tue, 23 Dec 2025 05:55:53 GMT

Publication date: December 2025

Source: Current Opinion in Solid State and Materials Science, Volume 39

Author(s): Hao Chen, Hsiao-Hsuan Wu, Chia-Chen Li

[ScienceDirect Publication: Current Opinion in Solid State and Materials Science] State-of-the-art review of additive friction stir deposition: microstructural evolution, machine learning applications, and future directions

ScienceDirect Publication: Current Opinion in Solid State and Materials Science — Tue, 23 Dec 2025 05:55:53 GMT

Publication date: January 2026

Source: Current Opinion in Solid State and Materials Science, Volume 40

Author(s): Ashish Kumar, Lei Shi, Virendra Pratap Singh, Sudipta Mohapatra, Long Li, Chuansong Wu, Sergey Mironov, Amitava De

[ScienceDirect Publication: Journal of Energy Storage] Transfer learning-enhanced hybrid deep neural network model for accurate lithium-ion batteries health estimation in electric vehicles

ScienceDirect Publication: Journal of Energy Storage — Tue, 23 Dec 2025 05:55:53 GMT

Publication date: 1 February 2026

Source: Journal of Energy Storage, Volume 145

Author(s): Ibrahim AL-Wesabi, Hassan M. Hussein Farh, Abdullrahman A. Al-Shamma'a

[ScienceDirect Publication: Journal of Energy Storage] UV-curable montmorillonite-enhanced gel polymer electrolyte for dendrite-free, enhanced ion transport, and flexible Zn metal batteries

ScienceDirect Publication: Journal of Energy Storage — Tue, 23 Dec 2025 05:55:53 GMT

Publication date: 1 February 2026

Source: Journal of Energy Storage, Volume 145

Author(s): Eunyoung Jung, Gaeun Lee, Byeongjun Kim, Chanwoo Park, Yujin Nam, Jong-Seong Bae, Ji Hyeon Kim, Yong Nam Ahn, Jaehyun Hur

[ScienceDirect Publication: Journal of Energy Storage] Mechanically reinforced graphene heterostructure anodes co-optimizing ultrafast ion diffusion and high storage capacity for Li/Na-ion batteries

ScienceDirect Publication: Journal of Energy Storage — Tue, 23 Dec 2025 05:55:53 GMT

Publication date: 1 February 2026

Source: Journal of Energy Storage, Volume 145

Author(s): Houda Khattab, Hamza Bekkali, Abdelilah Benyoussef, Abdallah El Kenz, Omar Mounkachi

[ScienceDirect Publication: Journal of Energy Storage] Thermal diffusivity and conductivity of sulfide and oxide solid electrolytes: Effects of densification and microstructural evolution

ScienceDirect Publication: Journal of Energy Storage — Tue, 23 Dec 2025 05:55:53 GMT

Publication date: 1 February 2026

Source: Journal of Energy Storage, Volume 145

Author(s): Hayoung Lee, Yuto Seki, Atsuro Okumura, Manabu Kodama

[ScienceDirect Publication: Solid State Ionics] Crossover from insulating into solid electrolyte behavior in bulk CaSO₄⋅0.5H₂O material due to ion exchange processes induced by high-temperature treatment with orthophosphoric acid

ScienceDirect Publication: Solid State Ionics — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: 1 February 2026

Source: Solid State Ionics, Volume 434

Author(s): Ivan Nikulin, Tatiana Nikulicheva, Vitaly Vyazmin, Oleg Ivanov, Nikita Anosov, Olga Telpova

[ScienceDirect Publication: Solid State Ionics] First-principles elucidation of the effects of Al-doping on Li-ion diffusion in LiCoO₂

ScienceDirect Publication: Solid State Ionics — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: 1 February 2026

Source: Solid State Ionics, Volume 434

Author(s): Jordan A. Barr, Scott P. Beckman, Brandon C. Wood, Liwen F. Wan

[ScienceDirect Publication: Computational Materials Science] Machine learning-driven design of polyimides with tailored dielectric constants

ScienceDirect Publication: Computational Materials Science — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: 30 January 2026

Source: Computational Materials Science, Volume 262

Author(s): Rongrong Zheng, Boyang Liang, Wenjia Huo, Xiang Wu, Yaoyao Bai

[ScienceDirect Publication: Computational Materials Science] MAPAL: A python library for mapping features and properties of alloys over compositional spaces

ScienceDirect Publication: Computational Materials Science — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: 30 January 2026

Source: Computational Materials Science, Volume 262

Author(s): Dishant Beniwal, Pratik K. Ray

[ScienceDirect Publication: Computational Materials Science] Machine learning assisted local descriptors predicate oxygen reduction activity of transition metal@Ti_1−xZn_x alloys

ScienceDirect Publication: Computational Materials Science — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: 30 January 2026

Source: Computational Materials Science, Volume 262

Author(s): Tian-Zhe Wan, Shou-Heng Guo, Guang-Qiang Yu, Jun-Zhe Li, Ya-Nan Zhu, Xi-Bo Li

[ScienceDirect Publication: Computational Materials Science] PyVUMAT: A package to develop and deploy machine learning material models in finite element analysis simulations

ScienceDirect Publication: Computational Materials Science — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: 30 January 2026

Source: Computational Materials Science, Volume 262

Author(s): Joshua C. Crone

[ScienceDirect Publication: Computational Materials Science] Predicting hydrogen storage capacity of metal hydrides using novel imputation techniques and tree-based machine learning models

ScienceDirect Publication: Computational Materials Science — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: 30 January 2026

Source: Computational Materials Science, Volume 262

Author(s): Zaid Allal, Hassan N. Noura, Flavien Vernier, Ola Salman, Khaled Chahine

[ScienceDirect Publication: Computational Materials Science] Accelerating magnetic materials discovery using interaction matrix-based machine learning descriptors

ScienceDirect Publication: Computational Materials Science — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: 30 January 2026

Source: Computational Materials Science, Volume 262

Author(s): Apoorv Verma, Junaid Jami, Amrita Bhattacharya

[ScienceDirect Publication: Computational Materials Science] Effects of training machine-learning potentials for radiation damage simulations using different pseudopotentials

ScienceDirect Publication: Computational Materials Science — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: 30 January 2026

Source: Computational Materials Science, Volume 262

Author(s): A. Fellman, J. Byggmästar, F. Granberg, F. Djurabekova, K. Nordlund

[ScienceDirect Publication: Computational Materials Science] Application of artificial neural networks for predicting viscoelastic properties of short-fiber reinforced thermoplastics

ScienceDirect Publication: Computational Materials Science — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: 30 January 2026

Source: Computational Materials Science, Volume 262

Author(s): Sally Rüschendorf, Alexander Kriwet, Fabian Urban, Kai-Uwe Schröder

[ScienceDirect Publication: Computational Materials Science] Structure-driven prediction and mechanism insights into piezoelectric performance of potassium sodium niobate via interpretable machine learning

ScienceDirect Publication: Computational Materials Science — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: February 2026

Source: Computational Materials Science, Volume 263

Author(s): Hui Li, WenKe Lu, Chunlei Li, Jinyi Liu, Zihui Feng, Jiaming Liu, Lan Yang

[ScienceDirect Publication: Computational Materials Science] Band structure modulation of germanium under arbitrary strain directions: A combined approach of strain matrix theory, first-principles calculation and machine learning

ScienceDirect Publication: Computational Materials Science — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: February 2026

Source: Computational Materials Science, Volume 263

Author(s): Hai Wang, Wenqi Huang, Mengjiang Jia

[ScienceDirect Publication: Computational Materials Science] Machine learning-driven prognostication of multifunctional properties of (K,Na)NbO₃-based lead-free ceramics for optimized materials design

ScienceDirect Publication: Computational Materials Science — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: February 2026

Source: Computational Materials Science, Volume 263

Author(s): Manisha Kumari, Alok Shukla

[ScienceDirect Publication: Computational Materials Science] Machine learning for domain transfer between simulated and experimental 2D X-ray diffraction patterns using generative adversarial networks

ScienceDirect Publication: Computational Materials Science — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: February 2026

Source: Computational Materials Science, Volume 263

Author(s): Samantha J. Brozak, David Montes de Oca Zapiain, Brendan Donohoe, Tommy Ao, Nathan P. Brown, Marcus D. Knudson, Carianne Martinez, J. Matthew D. Lane

[ScienceDirect Publication: Computational Materials Science] Machine learning interatomic potentials for monolayer hexagonal boron nitride: Thermal transport and defect effects via deep potential molecular dynamics

ScienceDirect Publication: Computational Materials Science — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: February 2026

Source: Computational Materials Science, Volume 263

Author(s): Guanghao Zhang, Zicheng Wang, Caihua Shi, Ye Han

[ScienceDirect Publication: Artificial Intelligence Chemistry] Machine Learning (ML)-driven quantitative structure-pharmacokinetic relationship (QSPKR) modeling of the tissue-to-plasma partition coefficient (K_p) of drugs across different tissues

ScienceDirect Publication: Artificial Intelligence Chemistry — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: December 2025

Source: Artificial Intelligence Chemistry, Volume 3, Issue 2

Author(s): Souvik Pore, Kunal Roy

[ScienceDirect Publication: Artificial Intelligence Chemistry] Machine learning prediction of pKa of organic acids

ScienceDirect Publication: Artificial Intelligence Chemistry — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: December 2025

Source: Artificial Intelligence Chemistry, Volume 3, Issue 2

Author(s): Juda Baikété, Alhadji Malloum, Jeanet Conradie

[ScienceDirect Publication: Artificial Intelligence Chemistry] GraphSLA: Graph machine learning for predicting small molecule - lncRNA associations

ScienceDirect Publication: Artificial Intelligence Chemistry — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: December 2025

Source: Artificial Intelligence Chemistry, Volume 3, Issue 2

Author(s): Ashish Panghalia, Parth Kumar, Vikram Singh

[ScienceDirect Publication: Artificial Intelligence Chemistry] Machine learning-guided synthesis of prospective organic molecular materials: An algorithm with latent variables for understanding and predicting experimentally unobservable reactions

ScienceDirect Publication: Artificial Intelligence Chemistry — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: December 2025

Source: Artificial Intelligence Chemistry, Volume 3, Issue 2

Author(s): Kazuhiro Takeda, Naoya Ohtsuka, Toshiyasu Suzuki, Norie Momiyama

[ScienceDirect Publication: Artificial Intelligence Chemistry] A method for predicting molecular point group based on graph neural networks

ScienceDirect Publication: Artificial Intelligence Chemistry — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: December 2025

Source: Artificial Intelligence Chemistry, Volume 3, Issue 2

Author(s): Siyuan Zeng, Kuanping Gong, Yongquan Jiang, Yan Yang

[ScienceDirect Publication: Artificial Intelligence Chemistry] Neural networks for neurocomputing circuits: A computational study of tolerance to noise and activation function non-uniformity when machine learning materials properties

ScienceDirect Publication: Artificial Intelligence Chemistry — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: December 2025

Source: Artificial Intelligence Chemistry, Volume 3, Issue 2

Author(s): Ye min Thant, Methawee Nukunudompanich, Chu-Chen Chueh, Manabu Ihara, Sergei Manzhos

[ScienceDirect Publication: Artificial Intelligence Chemistry] Electrostatic map for the hydration-induced frequency shifts of the NH stretching mode of peptide constructed using some machine learning models

ScienceDirect Publication: Artificial Intelligence Chemistry — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: December 2025

Source: Artificial Intelligence Chemistry, Volume 3, Issue 2

Author(s): Hajime Torii, Mikito Tsujimoto, Yukichi Kitamura

[ScienceDirect Publication: Artificial Intelligence Chemistry] Comparative study of machine learning methods for accurate prediction of logP and pKb

ScienceDirect Publication: Artificial Intelligence Chemistry — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: June 2026

Source: Artificial Intelligence Chemistry, Volume 4, Issue 1

Author(s): Juda Baikété, Alhadji Malloum, Jeanet Conradie

[ScienceDirect Publication: Artificial Intelligence Chemistry] Statistical comparison and uncertainty analysis of graph neural networks and machine learning models for molecular property prediction in drug discovery

ScienceDirect Publication: Artificial Intelligence Chemistry — Tue, 23 Dec 2025 05:55:51 GMT

Publication date: June 2026

Source: Artificial Intelligence Chemistry, Volume 4, Issue 1

Author(s): Sarmad Waleed, Shams ul Islam, Muhammad Saleem, Ali Ahmed

[ScienceDirect Publication: eScience] In situ engineered organic–inorganic bilayer solid electrolyte interphase enabling lithium metal batteries over 15,000-hour lifespan

ScienceDirect Publication: eScience — Tue, 23 Dec 2025 05:55:50 GMT

Publication date: Available online 27 September 2025

Source: eScience

Author(s): Bin Zhang, Ke Yue, Ke Wang, Guohao Zhao, Wenbin Huang, Tao Li, Jianmin Luo, Ying Xu, Xinyong Tao

[ScienceDirect Publication: eScience] Machine learning-driven discovery of high-performance Co/Sr-free air electrodes for protonic ceramic electrolysis cells

ScienceDirect Publication: eScience — Tue, 23 Dec 2025 05:55:50 GMT

Publication date: Available online 24 October 2025

Source: eScience

Author(s): Ning Wang, Qingwen Su, Huanxin Xiang, Biyao Zhou, Jiaxin Huang, Fangyuan Zheng, Ruijie Zhu, Yoshitaka Aoki, Ling Meng, Feng Jiao, Baoyin Yuan, Chunmei Tang, Siyu Ye

[ScienceDirect Publication: eScience] Oxygen-induced lithium inter-cage conduction for enhanced performance in all-solid-state batteries

ScienceDirect Publication: eScience — Tue, 23 Dec 2025 05:55:50 GMT

Publication date: Available online 18 November 2025

Source: eScience

Author(s): Yongsun Park, Myeongcho Jang, Jiyu Kim, Joon Ha Chang, Hae-Yong Kim, Seung-Deok Seo, Gwangseok Oh, Min Jae You, Sunho Ko, Jung Ho Kim, Kyung-Wan Nam, Seungho Yu, Sang-Cheol Nam, Ohmin Kwon

[ScienceDirect Publication: Science Bulletin] Machine learning-guided design of high-performance Mg-based thermoelectrics: insights into thermal expansion effects

ScienceDirect Publication: Science Bulletin — Tue, 23 Dec 2025 05:55:50 GMT

Publication date: 30 November 2025

Source: Science Bulletin, Volume 70, Issue 22

Author(s): Da Wan, Shulin Bai, Sirui Fan, Bixuan Li, Xiaoya Huang, Hongyuan Zhao, Ziqi Wang, Zhen Li, Yu Liu, Peng Kang, Lei Zheng, Li-Dong Zhao, Huibin Xu

[ScienceDirect Publication: Science Bulletin] Reconfigurable bipolar and nonlinear photoresponse in 2D VSe₂/WSe₂ photodetectors for high-performance optical neural networks

ScienceDirect Publication: Science Bulletin — Tue, 23 Dec 2025 05:55:50 GMT

Publication date: Available online 15 December 2025

Source: Science Bulletin

Author(s): Zhengui Zhao, Yuan Cheng, Jiacheng Sun, Pengyu Zhang, Tonglu Wang, Jianshi Tang, Yuyan Wang, Junying Zhang

[ScienceDirect Publication: Science Bulletin] Welding surface epitaxial layer enables high-loading sulfide all-solid-state batteries

ScienceDirect Publication: Science Bulletin — Tue, 23 Dec 2025 05:55:50 GMT

Publication date: 15 December 2025

Source: Science Bulletin, Volume 70, Issue 23

Author(s): Zhuomin Qiang, Yanbin Ning, Wei Zhao, Yajie Song, Chaoqun Zhang, Shengwei Dong, Yifei Zhao, Yan Zhang, Yinyong Sun, Biao Deng, Xudong Li, Geping Yin, Shuaifeng Lou

[ScienceDirect Publication: Progress in Materials Science] The role of protein content in body fluids in magnesium alloy bioimplant degradation: A machine learning approach

ScienceDirect Publication: Progress in Materials Science — Tue, 23 Dec 2025 05:55:47 GMT

Publication date: April 2026

Source: Progress in Materials Science, Volume 158

Author(s): M.N. Bharath, R.K. Singh Raman, Alankar Alankar

[ScienceDirect Publication: Materials Today Physics] Deformation mechanisms of multiphase AlCoCuFeNiTi high-entropy alloys revealed by machine learning-assisted molecular dynamics

ScienceDirect Publication: Materials Today Physics — Tue, 23 Dec 2025 05:55:46 GMT

Publication date: December 2025

Source: Materials Today Physics, Volume 59

Author(s): Yu-Sheng Lu, Kenta Yamanaka, Hiroya Ishii, Thi-Xuyen Bui, Yi-Ju Hsieh, Te-Hua Fang

[ScienceDirect Publication: Materials Today Physics] Modeling strategies for hydrogen reduction of high-purity metals: From DFT to ReaxFF and machine learning

ScienceDirect Publication: Materials Today Physics — Tue, 23 Dec 2025 05:55:46 GMT

Publication date: December 2025

Source: Materials Today Physics, Volume 59

Author(s): Zhimeng Shao, Bowen Gao, Zhifang Hu, Honglin Jiang, Qidong Zhang, Zhihe Dou, Yanxi Yin

[ScienceDirect Publication: Materials Today Physics] Physics-informed neural networks with hard-encoded angle-dependent boundary conditions for phonon Boltzmann transport equation

ScienceDirect Publication: Materials Today Physics — Tue, 23 Dec 2025 05:55:46 GMT

Publication date: December 2025

Source: Materials Today Physics, Volume 59

Author(s): Jiahang Zhou, Ruiyang Li, Wenjie Shang, Yi Liu, J.P. Panda, Pan Du, Xin-Yang Liu, Jasmine Liang, Ben-Chi Ma, Jian-Xun Wang, Tengfei Luo

[ScienceDirect Publication: Materials Today Physics] Machine-learning potentials for quantum and anharmonic effects in superconducting $F m \overline{3} m$ LaBeH₈

ScienceDirect Publication: Materials Today Physics — Tue, 23 Dec 2025 05:55:46 GMT

Publication date: December 2025

Source: Materials Today Physics, Volume 59

Author(s): Guiyan Dong, Tian Cui, Zihao Huo, Zhengtao Liu, Wenxuan Chen, Pugeng Hou, Yue-Wen Fang, Defang Duan

[ScienceDirect Publication: Materials Today Physics] A computational framework for interface design using lattice matching, machine learning potentials, and active learning: A case study on LaCoO₃/La₂NiO₄

ScienceDirect Publication: Materials Today Physics — Tue, 23 Dec 2025 05:55:46 GMT

Publication date: December 2025

Source: Materials Today Physics, Volume 59

Author(s): Guangchen Liu, Songge Yang, Yu Zhong

[ScienceDirect Publication: Materials Today Physics] Thermal expansion prediction in oxide glasses via graph neural networks with temperature-encoded virtual nodes

ScienceDirect Publication: Materials Today Physics — Tue, 23 Dec 2025 05:55:46 GMT

Publication date: December 2025

Source: Materials Today Physics, Volume 59

Author(s): Huang Ming, Xiong Jingxian, Peng Yongqian, Mao Haijun, Liu Zhuofeng, Li Wei, Wang Fenglin, Zhang Weijun, Chen Xingyu

[ScienceDirect Publication: Materials Today Physics] Tackling dataset curation challenges towards reliable machine learning: a case study on thermoelectric materials

ScienceDirect Publication: Materials Today Physics — Tue, 23 Dec 2025 05:55:46 GMT

Publication date: December 2025

Source: Materials Today Physics, Volume 59

Author(s): Shoeb Athar, Adrien Mecibah, Philippe Jund

[ScienceDirect Publication: Materials Today Physics] Research progress of machine learning in flexible strain sensors in the context of material intelligence

ScienceDirect Publication: Materials Today Physics — Tue, 23 Dec 2025 05:55:46 GMT

Publication date: December 2025

Source: Materials Today Physics, Volume 59

Author(s): Jie Li, Zhe Li, Yan Lu, Gang Ye, Yan Hong, Li Niu, Jian Fang

[ScienceDirect Publication: Materials Today Physics] A physics-informed machine learning framework for unified prediction of superconducting transition temperatures

ScienceDirect Publication: Materials Today Physics — Tue, 23 Dec 2025 05:55:46 GMT

Publication date: January 2026

Source: Materials Today Physics, Volume 60

Author(s): Ehsan Alibagheri, Mohammad Sandoghchi, Alireza Seyfi, Mohammad Khazaei, S. Mehdi Vaez Allaei

[ScienceDirect Publication: Materials Today Physics] Revisiting thermoelectric transport in 122 Zintl phases: Anharmonic phonon renormalization and phonon localization effects

ScienceDirect Publication: Materials Today Physics — Tue, 23 Dec 2025 05:55:46 GMT

Publication date: January 2026

Source: Materials Today Physics, Volume 60

Author(s): Zhenguo Wang, Yinchang Zhao, Jun Ni, Zhenhong Dai

[ScienceDirect Publication: Materials Today] A facile construction of LiF interlayer and F-doping via PECVD for LATP-based hybrid electrolytes: Enhanced Li-ion transport kinetics and superior lithium metal compatibility

ScienceDirect Publication: Materials Today — Tue, 23 Dec 2025 05:55:46 GMT

Publication date: December 2025

Source: Materials Today, Volume 91

Author(s): Xian-Ao Li, Yiwei Xu, Kepin Zhu, Yang Wang, Ziqi Zhao, Shengwei Dong, Bin Wu, Hua Huo, Shuaifeng Lou, Xinhui Xia, Xin Liu, Minghua Chen, Stefano Passerini, Zhen Chen

[ScienceDirect Publication: Materials Today] Revitalizing multifunctionality of Li-Al-O system enabling mother-powder-free sintering of garnet-type solid electrolytes

ScienceDirect Publication: Materials Today — Tue, 23 Dec 2025 05:55:46 GMT

Publication date: Available online 10 December 2025

Source: Materials Today

Author(s): Hwa-Jung Kim, Jong Hoon Kim, Minseo Choi, Jung Hyun Kim, Hosun Shin, Ki Chang Kwon, Sun Hwa Park, Hyun Min Park, Seokhee Lee, Young Heon Kim, Hyeokjun Park, Seung-Wook Baek

[ScienceDirect Publication: Nano Energy] Monoclinic Li₂ZrO₃ with cationic vacancy–based ion transport channels enhanced composite polymer electrolytes for high-rate solid-state lithium metal batteries

ScienceDirect Publication: Nano Energy — Tue, 23 Dec 2025 05:55:41 GMT

Publication date: January 2026

Source: Nano Energy, Volume 147

Author(s): Qianyi Xu, Yanru Wang, Xiang Feng, Timing Fang, Xueyan Li, Longzhou Zhang, Lijie Zhang, Daohao Li, Dongjiang Yang

[ScienceDirect Publication: Nano Energy] Sulfonated ether-free polybenzimidazole membrane with fast and selective ion transport enabling ultrahigh cycle stability in vanadium redox flow batteries

ScienceDirect Publication: Nano Energy — Tue, 23 Dec 2025 05:55:41 GMT

Publication date: January 2026

Source: Nano Energy, Volume 147

Author(s): Hui Yan, Wei Wei, Xin Li, Qi-an Zhang, Ying Li, Ao Tang

[ScienceDirect Publication: Nano Energy] Calendar aging of sulfide all-solid-state batteries

ScienceDirect Publication: Nano Energy — Tue, 23 Dec 2025 05:55:41 GMT

Publication date: January 2026

Source: Nano Energy, Volume 147

Author(s): Yujing Wu, Ziqi Zhang, Dengxu Wu, Fuqiang Xu, Mu Zhou, Hong Li, Liquan Chen, Fan Wu

[ScienceDirect Publication: Nano Energy] Energy-efficient, high-accuracy sensing in loose-fitting textile sensor matrix for LLM-enabled human-robot collaboration

ScienceDirect Publication: Nano Energy — Tue, 23 Dec 2025 05:55:41 GMT

Publication date: January 2026

Source: Nano Energy, Volume 147

Author(s): Pengfei Deng, Yang Meng, Qilong Cheng, Yuanqiu Tan, Zhihong Chen, Tian Li

[ScienceDirect Publication: Nano Energy] Lithium superionic solid electrolyte: Phosphorus-free sulfide glass of LiSbGe_(4-x)/4S_4-xCl_x

ScienceDirect Publication: Nano Energy — Tue, 23 Dec 2025 05:55:41 GMT

Publication date: January 2026

Source: Nano Energy, Volume 147

Author(s): Yuna Kim, Woojung Lee, Jiyun Han, Yeong Mu Seo, Dokyung Kim, Young Joo Lee, Byung Gon Kim, Munseok S. Chae, Sung Jin Kim, In Young Kim

[ScienceDirect Publication: Nano Energy] Advancing high-safety and low-cost all-solid-state batteries with polyanion cathodes: Challenges and recent progress

ScienceDirect Publication: Nano Energy — Tue, 23 Dec 2025 05:55:41 GMT

Publication date: February 2026

Source: Nano Energy, Volume 148

Author(s): Ali Yaghtin, Atiyeh Nekahi, Jeremy I.G. Dawkins, Xia Li, Karim Zaghib, Sixu Deng

[ScienceDirect Publication: Nano Energy] Enabling high-accuracy lithium-ion battery status prediction via machine learning-integrated perovskite sensors

ScienceDirect Publication: Nano Energy — Tue, 23 Dec 2025 05:55:41 GMT

Publication date: February 2026

Source: Nano Energy, Volume 148

Author(s): Jiaxin Sun, Xianjie Xu, Zhefu Mu, Zijun Huang, Guo Chen, Xinkai Qi, Hongwei Liu, Lei Zhu, Xiuquan Gu, Xinjian He, Sheng Huang

[ScienceDirect Publication: Nano Energy] Phase boundary engineering in micro-sized Sn/SnSb anode enabling superior sodium storage: Synergistic stress relief and fast ion transport

ScienceDirect Publication: Nano Energy — Tue, 23 Dec 2025 05:55:41 GMT

Publication date: February 2026

Source: Nano Energy, Volume 148

Author(s): Yuhong Liang, Chengcheng He, Zhengyang Zhao, Longqing Zhang, Rui Sun, Qian Ning, Huibing He, Yang Ren, Jing Xu, Qiang Zhang, Yajie Song, Xucai Yin

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

ScienceDirect Publication: Matter — Tue, 23 Dec 2025 05:55:41 GMT

Publication date: Available online 10 October 2025

Source: Matter

Author(s): Zhipeng Miao, Yahui Han, Qi Pan, Yipei Wang, Haibin Wang, Yunhang Xie, Jie Yu, Yapeng Shi, Rui Zhang, Yanlin Song, Pengwei Li

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

ScienceDirect Publication: Matter — Tue, 23 Dec 2025 05:55:41 GMT

Publication date: Available online 14 October 2025

Source: Matter

Author(s): Yanmin Zhu, Loza F. Tadesse

[ScienceDirect Publication: Matter] Precisely deciphering solid electrolyte interphase

ScienceDirect Publication: Matter — Tue, 23 Dec 2025 05:55:41 GMT

Publication date: 5 November 2025

Source: Matter, Volume 8, Issue 11

Author(s): Enhui Wang, Shaohua Ge, Wenbin Li, Beibei Fu, Fangyi Zhou, Weihua Chen

[ScienceDirect Publication: Matter] Rapid scalable plasma processing of thin-film Li–La–Zr–O solid-state electrolytes

ScienceDirect Publication: Matter — Tue, 23 Dec 2025 05:55:41 GMT

Publication date: 5 November 2025

Source: Matter, Volume 8, Issue 11

Author(s): Gabriel Badillo Crane, Thomas W. Colburn, Sarah E. Holmes, Justus Just, Yi Cui, Reinhold H. Dauskardt

[ScienceDirect Publication: Joule] Impact of metallic interlayers at the lithium-Li₆PS₅Cl solid electrolyte interface

ScienceDirect Publication: Joule — Tue, 23 Dec 2025 05:55:41 GMT

Publication date: 19 November 2025

Source: Joule, Volume 9, Issue 11

Author(s): Souhardh Kotakadi, Jack Aspinall, Matthew Burton, Yi Liang, Yuichi Aihara, Mauro Pasta

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

ScienceDirect Publication: Joule — Tue, 23 Dec 2025 05:55:41 GMT

Publication date: 17 December 2025

Source: Joule, Volume 9, Issue 12

Author(s): Do-Hyeon Kim, Young-Han Lee, Jeong-Myeong Yoon, Pugalenthiyar Thondaiman, Byung Chul Kim, In-Chul Choi, Jeong-Hee Choi, Ki-Joon Jeon, Cheol-Min Park

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

ScienceDirect Publication: Joule — Tue, 23 Dec 2025 05:55:41 GMT

Publication date: Available online 19 December 2025

Source: Joule

[cond-mat updates on arXiv.org] CrystalFormer-CSP: Thinking Fast and Slow for Crystal Structure Prediction

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2512.18251v1 Announce Type: new -Abstract: Crystal structure prediction is a fundamental problem in materials science. We present CrystalFormer-CSP, an efficient framework that unifies data-driven heuristic and physics-driven optimization approaches to predict stable crystal structures for given chemical compositions. The approach combines pretrained generative models for space-group-informed structure generation and a universal machine learning force field for energy minimization. Reinforcement fine-tuning can be employed to further boost the accuracy of the framework. We demonstrate the effectiveness of CrystalFormer-CSP on benchmark problems and showcase its usage via web interface and language model integration.

[cond-mat updates on arXiv.org] Spin Reorientation Driven Renormalization of Spin-Phonon Coupling in Fe$_4$GeTe$_2$

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2512.18544v1 Announce Type: new -Abstract: Quasi-2D van der Waals ferromagnet Fe$_4$GeTe$_2$, featuring the simultaneous presence of high Curie temperature ($T_\mathrm{C}$ $\sim 270$ K) and a spin-reorientation transition at $T_\mathrm{SR}$ $\sim 110$ K, is a rare system where strong interplay of spin dynamics, lattice vibrations, and electronic structure leads to a wide range of interesting phenomena. Here, we investigate the lattice response of exfoliated Fe$_4$GeTe$_2$ nanoflakes using temperature-dependent Raman spectroscopy. Polarization-resolved measurements reveal that, while one Raman mode exhibits a purely out-of-plane character, the rest display mixed symmetry, reflecting interlayer vibrational nonuniformity and symmetry-driven mode degeneracies. Below $T_\mathrm{C}$, phonons harden, and the linewidth narrows, consistent with reduced anharmonicity, while across the spin reorientation transition at $T_\mathrm{SR}$ they display anomalous softening, linewidth broadening, and a peak in lifetime, which are signatures of strengthened spin-phonon coupling. Complementary DFT+DMFT calculations and atomistic spin dynamical simulations reveal temperature-dependent spin excitations whose energies overlap with the Raman-active phonons, providing a natural route for the observed magnon-phonon interaction. Together, these insights establish Fe$_4$GeTe$_2$ as a versatile platform for exploring intertwined spin, lattice, and electronic degrees of freedom, with relevance for dynamic spintronic and magneto-optic functionalities near technologically meaningful temperatures.

[cond-mat updates on arXiv.org] Tackling dataset curation challenges towards reliable machine learning: a case study on thermoelectric materials

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2512.18653v1 Announce Type: new -Abstract: Machine Learning (ML) driven discovery of novel and efficient thermoelectric (TE) materials warrants experimental TE datasets of high volume, diversity, and quality. While the largest publicly available dataset, Starrydata2, has a high data volume, it contains inaccurate data due to the inherent limitations of Large Language Model (LLM)-assisted data curation, ambiguous nomenclature and complex formulas of materials in the literature. Another unaddressed issue is the inclusion of multi-source experimental data, with high standard deviations and without synthesis information. Using half-Heusler (hH) materials as an example, this work is aimed at first highlighting these errors and inconsistencies which cannot be filtered with conventional dataset curation workflows. We then propose a statistical round-robin error-based data filtering method to address these issues, a method that can be applied to filter any other material property. Lastly, a hybrid dataset creation workflow, involving data from Starrydata2 and manual extraction, is proposed and the resulting dataset is analyzed and compared against Starrydata2.

[cond-mat updates on arXiv.org] Topological surface phonons modulate thermal transport in semiconductor thin films

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2512.18757v1 Announce Type: new -Abstract: While phonon topology in crystalline solids has been extensively studied, its influence on thermal transport-especially in nanostructures-remains elusive. Here, by combining first-principles-based machine learning potentials with the phonon Boltzmann transport equation and molecular dynamics simulations, we systematically investigate the role of topological surface phonons in the in-plane thermal transport of semiconductor thin films (Si, 4H -SiC, and c-BN). These topological surface phonons, originating from nontrivial acoustic phonon nodal lines, not only serve as key scattering channels for dominant acoustic phonons but also contribute substantially to the overall thermal conductivity. Remarkably, for these thin semiconductor films below 10 nm this contribution can be as large as over 30% of the in-plane thermal conductivity at 300 K, and the largest absolute contribution can reach 82 W/m-K, highlighting their significant role in nanoscale thermal transport in semiconductors. Furthermore, we demonstrate that both temperature and biaxial strain provide effective means to modulate this contribution. Our work establishes a direct link between topological surface phonons and nanoscale thermal transport, offering the first quantitative assessment of their role and paving the way for topology-enabled thermal management in semiconductors.

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

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2512.19449v1 Announce Type: new -Abstract: Aqueous zinc (Zn) batteries (AZBs) face significant challenges due to the limited compatibility of Zn anodes with conventional separators, leading to dendrite growth, hydrogen evolution reaction (HER), and poor cycling stability. While separator design is crucial for optimizing battery performance, its potential remains underexplored. The commonly used glass fiber (GF) filters were not originally designed as battery separators. To address their limitations, nanochitin derived from waste shrimp shells was used to fabricate separators with varying concentrations of amine and carboxylic functional groups. This study investigates how the type and concentration of these groups influence the separator's properties and performance. In a mild acidic electrolyte that protonates the amine groups, the results showed that the density of both ammonium and carboxylic groups in the separators significantly affected water structure and ionic conductivity. Quasi-Elastic Neutron Scattering (QENS) revealed that low-functionalized chitin, particularly with only ammonium groups, promotes strongly bound water with restricted mobility, thereby enhancing Zn plating and stripping kinetics. These separators exhibit exceptional Zn stability over 2000 hours at low current densities (0.5 mA/cm2), maintaining low overpotentials and stable polarization. Additionally, the full cell consisting of Zn||NaV3O8.1.5H2O showed a cycle life of over 2000 cycles at 2 A/g, demonstrating the compatibility of the nanochitin-based separators with low concentrations of functional surface groups. These results demonstrate the importance of a simple separator design for improving the overall performance of AZBs.

[cond-mat updates on arXiv.org] Long-range electrostatics for machine learning interatomic potentials is easier than we thought

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2512.18029v1 Announce Type: cross -Abstract: The lack of long-range electrostatics is a key limitation of modern machine learning interatomic potentials (MLIPs), hindering reliable applications to interfaces, charge-transfer reactions, polar and ionic materials, and biomolecules. In this Perspective, we distill two design principles behind the Latent Ewald Summation (LES) framework, which can capture long-range interactions, charges, and electrical response just by learning from standard energy and force training data: (i) use a Coulomb functional form with environment-dependent charges to capture electrostatic interactions, and (ii) avoid explicit training on ambiguous density functional theory (DFT) partial charges. When both principles are satisfied, substantial flexibility remains: essentially any short-range MLIP can be augmented; charge equilibration schemes can be added when desired; dipoles and Born effective charges can be inferred or finetuned; and charge/spin-state embeddings or tensorial targets can be further incorporated. We also discuss current limitations and open challenges. Together, these minimal, physics-guided design rules suggest that incorporating long-range electrostatics into MLIPs is simpler and perhaps more broadly applicable than is commonly assumed.

[cond-mat updates on arXiv.org] Microstructure-based Variational Neural Networks for Robust Uncertainty Quantification in Materials Digital Twins

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2512.18104v1 Announce Type: cross -Abstract: Aleatoric uncertainties - irremovable variability in microstructure morphology, constituent behavior, and processing conditions - pose a major challenge to developing uncertainty-robust digital twins. We introduce the Variational Deep Material Network (VDMN), a physics-informed surrogate model that enables efficient and probabilistic forward and inverse predictions of material behavior. The VDMN captures microstructure-induced variability by embedding variational distributions within its hierarchical, mechanistic architecture. Using an analytic propagation scheme based on Taylor-series expansion and automatic differentiation, the VDMN efficiently propagates uncertainty through the network during training and prediction. We demonstrate its capabilities in two digital-twin-driven applications: (1) as an uncertainty-aware materials digital twin, it predicts and experimentally validates the nonlinear mechanical variability in additively manufactured polymer composites; and (2) as an inverse calibration engine, it disentangles and quantitatively identifies overlapping sources of uncertainty in constituent properties. Together, these results establish the VDMN as a foundation for uncertainty-robust materials digital twins.

[cond-mat updates on arXiv.org] Lattice-Renormalized Tunneling Models for Superconducting Qubit Materials

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2512.18156v1 Announce Type: cross -Abstract: We present a lattice-renormalized formalism for configurational tunneling two-level systems (TLS) that overcomes limitations of minimum-energy-path and light-particle models. Derived from the nuclear Hamiltonian, our formulation introduces composite phonon coordinates to capture lattice distortions between degenerate potential wells. This approach resolves deficiencies in prior models and enables accurate computation of tunnel splittings and excitation spectra for hydrogen-based TLS in bcc Nb. Our results bound experimental tunnel splittings and reveal strong anharmonic couplings between tunneling atoms and lattice phonons, establishing a direct link between TLS dynamics and phonon-mediated strain interactions. The formalism further generalizes to multi-level systems (MLS), providing insight into defect-induced decoherence in superconducting qubits and guiding strategies for materials design to suppress TLS-related loss.

[cond-mat updates on arXiv.org] An Agentic Framework for Autonomous Materials Computation

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2512.19458v1 Announce Type: cross -Abstract: Large Language Models (LLMs) have emerged as powerful tools for accelerating scientific discovery, yet their static knowledge and hallucination issues hinder autonomous research applications. Recent advances integrate LLMs into agentic frameworks, enabling retrieval, reasoning, and tool use for complex scientific workflows. Here, we present a domain-specialized agent designed for reliable automation of first-principles materials computations. By embedding domain expertise, the agent ensures physically coherent multi-step workflows and consistently selects convergent, well-posed parameters, thereby enabling reliable end-to-end computational execution. A new benchmark of diverse computational tasks demonstrates that our system significantly outperforms standalone LLMs in both accuracy and robustness. This work establishes a verifiable foundation for autonomous computational experimentation and represents a key step toward fully automated scientific discovery.

[cond-mat updates on arXiv.org] Understanding the Lithium Ion Transport in Concentrated Block-Copolymer Electrolytes on a Microscopic Level

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2010.11673v3 Announce Type: replace -Abstract: Block-copolymer electrolytes with lamellar microstructure show promising results regarding the ion transport in experiments. Motivated by these observations we study block-copolymers consisting of a polystyrene (PS) block and a poly(ethylene oxide) (PEO) block which were assembled in a lamellar structure. The lamella was doped with various amounts of lithium-bis(trifluoromethane)sulfonimide (LiTFSI) until very high loadings with ratios of EO monomers to cations up to 1:1 were reached. We present insights into the structure and ion transport from extensive Molecular Dynamics simulations. For high salt concentrations most cations are not coordinated by PEO but rather by TFSI and THF. More specifically, LiTFSI partially separates from the PEO domain and forms a network-like structure in the middle of the lamella. This central salt-rich layer plays a decisive role to enable remarkably good cationic mobilities as well as high transport numbers in agreement with the experimental results.

[cond-mat updates on arXiv.org] Observation of multiple flat bands and van Hove singularities in the distorted kagome metal NdTi3Bi4

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2311.11488v2 Announce Type: replace -Abstract: Kagome materials have attracted enormous research interest recently owing to their diverse topological phases and manifestation of electronic correlation. Here, we present the electronic structure of a distorted ferromagnetic kagome metal, NdTi3Bi4, exhibiting a transition temperature of 9 K. Our investigation employs a combination of angle-resolved photoemission spectroscopy (ARPES) measurements and density functional theory (DFT) calculations. We discover the presence of two flat bands which are found to originate from the kagome structure formed by Ti atoms with major contribution from Ti dxy and Ti dx2-y2 orbitals. We also observed multiple van Hove singularities (VHSs) in its electronic structure, with one VHS lying near the Fermi level. The ARPES data reveals the existence of Dirac cone at the K point, a finding which is corroborated by our DFT calculations. These findings present detailed electronic structure capable of hosting correlation-driven phenomenon in this novel ferromagnetic kagome metal.

[cond-mat updates on arXiv.org] Deep Variational Free Energy Calculation of Hydrogen Hugoniot

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2507.18540v2 Announce Type: replace -Abstract: We develop a deep variational free energy framework to compute the equation of state of hydrogen in the warm dense matter region. This method parameterizes the variational density matrix of hydrogen nuclei and electrons at finite temperature using three deep generative models: a normalizing flow model for the Boltzmann distribution of the classical nuclei, an autoregressive transformer for the distribution of electrons in excited states, and a permutational equivariant flow model for the unitary backflow transformation of electron coordinates in Hartree-Fock states. By jointly optimizing the three neural networks to minimize the variational free energy, we obtain the equation of state and related thermodynamic properties of dense hydrogen for the temperature range where electrons occupy excited states. We compare our results with other theoretical and experimental results on the deuterium Hugoniot curve, aiming to resolve existing discrepancies. Our results bridge the gap between the results obtained by path-integral Monte Carlo calculations at high temperature and ground-state electronic methods at low temperature, thus providing a valuable benchmark for hydrogen in the warm dense matter region.

[cond-mat updates on arXiv.org] Universal Boundary-Modes Localization from Quantum Metric Length

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2509.05114v3 Announce Type: replace -Abstract: The presence of localized boundary modes is an unambiguous hallmark of topological quantum matter. While these modes are typically protected by topological invariants such as the Chern number, here we demonstrate that the {\it quantum metric length} (QML), a quantity inherent in multi-band topological systems, governs the spatial extent of flat-band topological boundary modes. We introduce a framework for constructing topological flat bands from degenerate manifolds with large quantum metric and find that the boundary modes exhibit dual phases of spatial behaviors: a conventional oscillatory decay arising from bare band dispersion, followed by another exponential decay controlled by quantum geometry. Crucially, the QML, derived from the quantum metric of the degenerate manifolds, sets a lower bound on the spatial spread of boundary states in the flat-band limit. Applying our framework to concrete models, we validate the universal role of the QML in shaping the long-range behavior of topological boundary modes. Furthermore, by tuning the QML, we unveil extraordinary non-local transport phenomena, including QML-shaped quantum Hall plateaus and anomalous Fraunhofer patterns. Our theoretical framework paves the way for engineering boundary-modes localization in topological flat-band systems.

[cond-mat updates on arXiv.org] Deep learning directed synthesis of fluid ferroelectric materials

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2512.16671v2 Announce Type: replace -Abstract: Fluid ferroelectrics, a recently discovered class of liquid crystals that exhibit switchable, long-range polar order, offer opportunities in ultrafast electro-optic technologies, responsive soft matter, and next-generation energy materials. Yet their discovery has relied almost entirely on intuition and chance, limiting progress in the field. Here we develop and experimentally validate a deep-learning data-to-molecule pipeline that enables the targeted design and synthesis of new organic fluid ferroelectrics. We curate a comprehensive dataset of all known longitudinally polar liquid-crystal materials and train graph neural networks that predict ferroelectric behaviour with up to 95% accuracy and achieve root mean square errors as low as 11 K for transition temperatures. A graph variational autoencoder generates de novo molecular structures which are filtered using an ensemble of high-performing classifiers and regressors to identify candidates with predicted ferroelectric nematic behaviour and accessible transition temperatures. Integration with a computational retrosynthesis engine and a digitised chemical inventory further narrows the design space to a synthesis-ready longlist. 11 candidates were synthesised and characterized through established mixture-based extrapolation methods. From which extrapolated ferroelectric nematic transitions were compared against neural network predictions. The experimental verification of novel materials augments the original dataset with quality feedback data thus aiding future research. These results demonstrate a practical, closed-loop approach to discovering synthesizable fluid ferroelectrics, marking a step toward autonomous design of functional soft materials.

[cond-mat updates on arXiv.org] Anti-Correlated Noise in Epoch-Based Stochastic Gradient Descent: Implications for Weight Variances in Flat Directions

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2306.05300v3 Announce Type: replace-cross -Abstract: Stochastic Gradient Descent (SGD) has become a cornerstone of neural network optimization due to its computational efficiency and generalization capabilities. However, the gradient noise introduced by SGD is often assumed to be uncorrelated over time, despite the common practice of epoch-based training where data is sampled without replacement. In this work, we challenge this assumption and investigate the effects of epoch-based noise correlations on the stationary distribution of discrete-time SGD with momentum. Our main contributions are twofold: First, we calculate the exact autocorrelation of the noise during epoch-based training under the assumption that the noise is independent of small fluctuations in the weight vector, revealing that SGD noise is inherently anti-correlated over time. Second, we explore the influence of these anti-correlations on the variance of weight fluctuations. We find that for directions with curvature of the loss greater than a hyperparameter-dependent crossover value, the conventional predictions of isotropic weight variance under stationarity, based on uncorrelated and curvature-proportional noise, are recovered. Anti-correlations have negligible effect here. However, for relatively flat directions, the weight variance is significantly reduced, leading to a considerable decrease in loss fluctuations compared to the constant weight variance assumption. Furthermore, we present a numerical experiment where training with these anti-correlations enhances test performance, suggesting that the inherent noise structure induced by epoch-based training may play a role in finding flatter minima that generalize better.

[cond-mat updates on arXiv.org] Unified Micromechanics Theory of Composites

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2503.14529v2 Announce Type: replace-cross -Abstract: We consider the matrix composite materials (CM) of either random (statistically homogeneous or inhomogeneous), periodic, or deterministic (neither random nor periodic) structures. CMs exhibit linear or nonlinear behavior, coupled or uncoupled multi-physical phenomena, locally elastic, weakly nonlocal (strain gradient and stress gradient), or strongly nonlocal (strain-type and displacement-type, peridynamics) phase properties. A modified Computational Analytical Micromechanics (CAM) approach introduces an exact Additive General Integral Equation (AGIE) for CMs of any structure and phase properties mentioned above. The unified iteration solution of static AGIEs is adapted to the body force with compact support serving as a fundamentally new universal training parameter. The approach also establishes a critical threshold for filtering out unsuitable sub-datasets of effective parameters through a novel Representative Volume Element (RVE) concept, which extends Hill's classical framework. This RVE concept eliminates sample size, boundary layer, and edge effects, making it applicable to CMs of any structure and phase properties, regardless of local or nonlocal, linear or nonlinear. Incorporating this new RVE concept into machine learning and neural network techniques enables the construction of any unpredefined surrogate nonlocal operators. The methodology is structured as a modular, block-based framework, allowing independent development and refinement of software components. This flexible, robust AGIE-CAM framework integrates data-driven, multi-scale, and multi-physics modeling, accelerating research in CM of any microtopology and phase properties considered. The AGIE-CAM framework represents a groundbreaking paradigm shift in the micromechanics of composites, redefining the very philosophy that underpins our understanding of their behavior at the microscopic level.

[cond-mat updates on arXiv.org] Separating water content from network dynamics in cell nuclei with Brillouin microscopy

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2504.17362v2 Announce Type: replace-cross -Abstract: Probing forces, deformations and generally speaking the mechanical properties of cells is the hallmark of mechanobiology. In the last two decades many techniques have been developed to this end that are largely based on deforming the cells and measuring the reaction force. In cells, an alternative approach has been implemented mid 2010's, based on Brillouin Light Scattering (BLS) that produces a spectrum that can be interpreted as the response of the sample to an infinitesimal uniaxial compression at picosecond timescales. In all of these measurements, the response of the cell is quantified with a colloquial "stiffness" that encompasses both the contribution of load-bearing structures and volume changes, much to confusion. To clarify the interpretation of the hypersonic data obtained from BLS spectra, we vary the relative volume fraction of intracellular water and solid network by applying osmotic compressions to single cells. In the nucleus, we observe a non-linear increase in the sound velocity and attenuation with increasing osmotic pressure that we fit to a poroelastic model, providing an estimate of the friction coefficient between the water phase and the network. By comparing BLS data to volume measurements, our approach demonstrates clearly that BLS shift alone is mostly sensitive to water content while the additional analysis of the linewidth allows identifying the contribution of the biopolymer-based network dynamics in living cells.

[cond-mat updates on arXiv.org] BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2505.01912v2 Announce Type: replace-cross -Abstract: Data-driven molecular discovery leverages artificial intelligence/machine learning (AI/ML) and generative modeling to filter and design novel molecules. Discovering novel molecules requires accurate out-of-distribution (OOD) predictions, but ML models struggle to generalize OOD. Currently, no systematic benchmarks exist for molecular OOD prediction tasks. We present $\mathbf{BOOM}$, $\mathbf{b}$enchmarks for $\mathbf{o}$ut-$\mathbf{o}$f-distribution $\mathbf{m}$olecular property predictions: a chemically-informed benchmark for OOD performance on common molecular property prediction tasks. We evaluate over 150 model-task combinations to benchmark deep learning models on OOD performance. Overall, we find that no existing model achieves strong generalization across all tasks: even the top-performing model exhibited an average OOD error 3x higher than in-distribution. Current chemical foundation models do not show strong OOD extrapolation, while models with high inductive bias can perform well on OOD tasks with simple, specific properties. We perform extensive ablation experiments, highlighting how data generation, pre-training, hyperparameter optimization, model architecture, and molecular representation impact OOD performance. Developing models with strong OOD generalization is a new frontier challenge in chemical ML. This open-source benchmark is available at https://github.com/FLASK-LLNL/BOOM

[cond-mat updates on arXiv.org] PLaID++: A Preference Aligned Language Model for Targeted Inorganic Materials Design

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2509.07150v3 Announce Type: replace-cross -Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a promising approach to improve correctness in LLMs, however, in many scientific problems, the objective is not necessarily to produce the correct answer, but instead to produce a diverse array of candidates which satisfy a set of constraints. We study this challenge in the context of materials generation. To this end, we introduce PLaID++, an LLM post-trained for stable and property-guided crystal generation. We find that performance hinges on our crystallographic representation and reward formulation. First, we introduce a compact, symmetry-informed Wyckoff text representation which improves computational efficiency and encourages generalization from physical priors. Second, we demonstrate that temperature scaling acts as an entropy regularizer which counteracts mode collapse and encourages exploration. By encoding symmetry constraints directly into text and guiding model outputs towards desirable chemical space, PLaID++ generates structures that are thermodynamically stable, unique, and novel at a $\sim$50\% greater rate than prior methods and conditionally generates structures with desired space group properties. Our work demonstrates the potential of adapting post-training techniques from natural language processing to materials design, paving the way for targeted and efficient discovery of novel materials.

[cond-mat updates on arXiv.org] Nonreciprocal yet Symmetric Multi-Species Active Matter: Emergence of Chirality and Species Separation

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2512.18749v1 Announce Type: new -Abstract: Nonreciprocal active matter systems typically feature an asymmetric role among interacting agents, such as a pursuer-evader relationship. We propose a multi-species nonreciprocal active matter model that is invariant under permutations of the particle species. The nonreciprocal, yet symmetric, interactions emerge from a constant phase shift in the velocity alignment interactions, rather than from an asymmetric coupling matrix. This system possessing permutation symmetry displays rich collective behaviors, including a species-mixed chiral phase with quasi-long-range polar order and a species separation phase characterized by vortex cells. The system also displays a coexistence phase of the chiral and the species separation phases, in which intriguing dynamic patterns emerge. These rich collective behaviors are a consequence of the interplay between nonreciprocity and permutation symmetry.

[cond-mat updates on arXiv.org] Enhanced sinterability and in vitro bioactivity of diopside through fluoride doping

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2512.19014v1 Announce Type: new -Abstract: In this work, diopside (CaMgSi2O6) was doped with fluoride at a level of 1 mol.%, without the formation of any second phase, by a wet chemical precipitation method. The sintered structure of the synthesized nanopowders was studied by X-ray diffraction, Fourier transform infrared spectroscopy and field-emission scanning electron microscopy. Also, the samples' in vitro apatite-forming ability in a simulated body fluid was comparatively evaluated by electron microscopy, inductively coupled plasma spectroscopy and Fourier transform infrared spectroscopy. According to the results, the material's sinterability was improved by fluoride doping, as realized from the further development of sintering necks. It was also found that compared to the undoped bioceramic, a higher amount of apatite was deposited on the surface of the doped sample. It is concluded that fluoride can be considered as a doping agent in magnesium-containing silicates to improve biological, particularly bioactivity, behaviors.

[cond-mat updates on arXiv.org] Bridging the divide: Economic exchange and segregation in dual-income cities

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2512.18680v1 Announce Type: cross -Abstract: Segregation is a growing concern around the world. One of its main manifestations is the creation of ghettos, whose inhabitants have difficult access to well-paid jobs, which are often located far from their homes. In order to study this phenomenon, we propose an extension of Schelling's model of segregation to take into account the existence of economic exchanges. To approximate a geographical model of the city, we consider a small-world network with a defined real estate market. The evolution of the system has also been studied, finding that economic exchanges follow exponential laws and relocations are approximated by power laws. In addition to this, we consider the existence of delays in the actions of the agents, which mainly affect the happiness of those with fewer economic resources. Besides, the size of the economic exchange plays a crucial role in overall segregation. Despite its simplicity, we find that our model reproduces real-world situations such as the separation between favoured and handicapped economic areas, the importance of economic contacts between them to improve the distribution of wealth, and the existence of efficient and cheap transport to break the poverty cycles typical of disadvantaged zones.

[cond-mat updates on arXiv.org] Information Supercurrents in Chiral Active Matter

cond-mat updates on arXiv.org — Tue, 23 Dec 2025 05:00:00 GMT

arXiv:2512.16884v2 Announce Type: replace -Abstract: Recent minimalist modeling has demonstrated that overdamped polar chiral active matter can support emergent, inviscid Euler turbulence, despite the system's strictly dissipative microscopic nature. In this letter, we establish the statistical mechanical foundation for this emergent inertial regime by deriving a formal isomorphism between the model's agent dynamics and the overdamped Langevin equation for disordered Josephson junctions. We identify the trapped agent state as carrying non-dissipative (phase rigidity) information supercurrents, analogous to a macroscopic superconducting phase governed by the Adler equation. The validity of this mapping is confirmed analytically and empirically by demonstrating a disorder-broadened Adler-Ohmic crossover in the system's slip velocity, corresponding to the saddle-node bifurcation of phase-locking systems. These results define the new minimal chiral flocking model as a motile, disordered Josephson array, bridging active turbulence and superconductivity.

[Communications Materials] In situ polymerization for high performance solid-state lithium-sulfur batteries

Communications Materials — Tue, 23 Dec 2025 00:00:00 GMT

Communications Materials, Published online: 23 December 2025; doi:10.1038/s43246-025-01035-3

Solid-state lithium-sulfur batteries promise high energy density, long-term performance, and enhanced safety, but face challenges with interfacial issues due to poor solid–solid contact. Here, the authors review the benefits and challenges of in situ polymerization, discussing its potential to enhance electrode-electrolyte integration and improve battery performance, and proposing future prospects for multifunctional polymer solid-state electrolytes.

[ChemRxiv] Molecular Dynamics Simulations for Organic Chemists – It’s About Time!

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

Molecular dynamics simulations model chemical reactions as continuous changes in molecular structure over time in-stead of static minima and transition states. This perspective argues that time-dependent structural change is a crucial, but often overlooked, mechanistic feature as many reactions simply do not follow a single, equilibrated minimum-energy path. We highlight examples where traditional transition state theory fails, typically cases involving short-lived interme-diates, non-equilibrium solvation, momentum-controlled selectivity, post-transition state bifurcations, and “hidden” dy-namic intermediates and show how molecular dynamics can reveal the actual sequence of structural change which gov-erns a reaction outcome. We also discuss emerging machine learning-based molecular dynamics which have found appli-cations in photochemistry and solvent modelling. While molecular dynamics will not replace methods based on transi-tion state theory, it offers organic chemists a time-resolved view of molecular structure which can be crucial to under-standing a given reaction. However, a central barrier for organic chemists is to understand when and why to apply an ad-vanced computational technique such as molecular dynamics simulations. In this perspective, we aim to introduce the methodology in sufficient detail to enable organic chemists to make this assessment and gain an appreciation for the im-portance of time in reaction mechanisms.

[ChemRxiv] Characterisation and immobilisation study of the Haloperoxidase from Curvularia inaequalis: application to phenol derivatives

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

In the past decades, usage of enzymes to catalyse halogenation reactions has emerged as a greener approach compared to usual ones using toxic reagents and yielding to a bad atom economy. Vanadium dependent haloperoxidases (VDHals) are the most commonly used enzymes for such transformations in organic chemistry due to their robustness. Among them, haloperoxidase from Curvularia inaequalis (CiVCPO) is the most cited in the literature but only barely characterized aside from its substrate spectrum and kinetic parameters. In the present study, we evaluated the melting temperature, thermostability, thermoactivity and solvent stability of CiVCPO in order to have a better overview of its potential in organic synthesis. We have also performed the first immobilisation study of this enzyme on 3 types of supports: 3 EziG™ glass beads coated with different polymers, 5 Relizyme™ polymethacrylate beads with different functional groups, the commercial Amberlite IRA900 and Dowex 50WX8, and 3 metal-organic frameworks from the UiO-66(Zr) family. As a result, we could retain 50% of total immobilized activity on 2 Relizyme supports (ethylamine (EA403) & iminodiacetic (IDA403), with 55% activity kept over 5 recycling steps. The unconventional UiO-66(Zr) family also proved to be an interesting material for this enzyme. Finally, we show that free enzyme and supported enzyme are suitable for bromination and chlorination of a range of phenolic compounds with excellent yields with up to 65% conversion in 24 h in the tested conditions.

[Nature Nanotechnology] Nanostructured niobium-doped nickel-rich multiphase positive electrode active material for high-power lithium-based batteries

Nature Nanotechnology — Tue, 23 Dec 2025 00:00:00 GMT

Nature Nanotechnology, Published online: 23 December 2025; doi:10.1038/s41565-025-02092-y

A two-step doping strategy for preparing Nb-doped Ni-rich positive electrode active materials forms nanosized grains and enables reversible multiphase transitions, improving lithium-ion transport and high-power performance of Li-based batteries.

[ChemRxiv] Study of anion transport by amide-based macrocycles of different sizes

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

Synthetic anion receptors can transport anions across lipid bilayer membranes. Many anion transporters have been reported, including macrocyclic compounds. However, there are no systematic studies on how the size of macrocycles impacts their anion transport activity and selectivity. Therefore, we prepared eight amide-based macrocyclic compounds with ring sizes ranging from 18 to 26 atoms, as well as four bis-amides. We studied these compounds as anion receptors using chloride titrations and molecular modelling, and as anion transporters using liposomes with various fluorescent probes. Neither the size of the macrocycles nor the affinity for chloride were found to be determining factors for chloride transport activity; preorganisation appears to play a more important role. Fluorination was found to have a clear positive effect on anion transport rates, with the bis-amides performing surprisingly well compared to the macrocycles. The smallest non-fluorinated macrocycle exhibited selectivity for chloride over hydroxide and nitrate, whereas the pentafluorophenyl bis-amide demonstrated effective transport of bicarbonate and nitrate, likely due to anion-pi interactions in addition to hydrogen bonding. The insights from this study will shape the design of future anionophores.

[ChemRxiv] Ion-Conductive Vitrimers Based on Backbone-Type Triazolium Poly(ionic liquid)s: Counterion-Dependent Dynamics and Backbone Flexibility

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

To simultaneously achieve high ionic conductivity and recyclability, vitrimers were prepared using backbone-type triazolium poly(ionic liquid)s (TPILs) that integrate ionic transport with dynamic network rearrangement via trans-N-alkylation. TPIL elastomers bearing I⁻, BF₄⁻, PF₆⁻, and TFSI⁻ counteranions were synthesized from “clickable” ionic liquid monomers, and their glass transition temperature (Tg), ionic conductivity, and vitrimeric dynamics were compared. Only the I⁻-based network exhibited stress relaxation at 170 °C, indicating that nucleophilic anions are important for bond exchange. However, a trade-off was observed between ionic transport and dynamic network rearrangement. Furthermore, mixed-anion TPIL elastomers using I⁻+TFSI⁻ exhibited lower Tg and higher ionic conductivity than I⁻-based elastomer, while still maintaining vitrimer-like relaxation. The segmental relaxation was decoupled from Arrhenius-type bond-exchange dynamics. Ionic conduction was dominated by segmental motion, with minimal contribution from cross-link exchange. This design combining flexible polymer backbones and cooperative anion engineering can create recyclable, highly conductive polymer electrolytes.

[ChemRxiv] Advances in Calcium Isotope Purification and Analysis Using Cutting-Edge Signal Amplifiers for Matrix-Diverse Reference Materials

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

Stable calcium (Ca) isotopes are increasingly applied across geosciences, medical sciences, ecology, paleontology, and archaeology. However, the deployment speed of Ca isotope applications worldwide is hampered by three major challenges: 1) the necessity for complex Ca purification procedures prior to analysis; 2) expensive instrumentation (typically TIMS or ICP-MS) requiring specific configurations and fine-tuning to generate reliable data; and 3) the exhaustion of some of the most widely used reference materials for cross-laboratory comparisons. In this study we present methodological advances aimed at lifting some of these barriers. First, we refined existing chromatography methods for purifying Ca by developing a branching procedure based on commercially available labware to allow faster method transfer and to minimize resin and reagent consumption for a variety of sample matrices. Our adjustments drastically improved strontium (Sr) separation from Ca, including for Sr-rich samples such as seawater. Second, we explored the potential of 10¹³Ω Faraday cup amplifiers for improving Ca isotope measurements. Our results show improved precision in 43Ca measurements under low ionic transmission configurations with δ43/42Ca standard deviation value reduced by half. This expands the list of ICP-MS configurations capable of producing reliable Ca isotope measurements and delineates a path for less sample-destructive methods (i.e., lower Ca analytical requirements). These amplifiers also markedly enhanced the correction of Sr²⁺ interferences typically affecting Ca ion beams. In this configuration, accurate and precise Ca isotopic measurements were obtained without prior Sr removal for Sr/Ca concentration ratios up to 10⁻². Lastly, using these technical advancements we analyzed existing and new international certified reference materials (SRM1486, SRM1400, IAPSO, CACB-1, DOLT-5, DORM-5, TORT-3), complementing existing and out-of-stock standards of the Ca isotope toolbox, notably for Ca carbonate and marine soft tissues. Together, these advances open the door of Ca isotope research to more laboratories and pave the way for future developments and applications.

[ChemRxiv] Predicting Sequence Dependent Fluorescence with Classic Machine Learning Models

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

Terminally labeled DNA oligonucleotides have wide applications in modern biology and biotechnological applications. It has been observed that the fluorescent intensity of light released from these fluorescent labels is heavily influenced by the terminal sequence of nucleotides. Recent studies have assayed and published the raw fluorescent values of Cy3 and Cy5 as a function of the most adjacent 5 nucleotides resulting in 1024 data points. While experimentally tractable, an increase in the sequence space will vastly increase the experimental and time cost. Machine Learning is well suited to addressing the issue of experimental tractability however there is a wide design space in the choice of algorithms. In this work we use classic machine learning models such as Support Vector Machine, Multilayer Perceptrons and Random Forests to both predict the raw intensity value and classify the intensity magnitude of the fluorophore using the sequence as input. We demonstrate that the performance of these models is heavily dependent on the numerical transformation of the sequence and that Random Forest consistently outperforms all other models in both regression and classification tasks irrespective of the sequence transformation.

[ChemRxiv] Machine learning driven advances in molecular dynamics of bulk and interfacial aqueous systems

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

Molecular dynamics (MD) simulations have been widely applied to investigate various physical and chemical processes in aqueous and interfacial environments, which are crucial for the design of energy materials and for understanding several chemical processes at the heart of life itself. However, the applicability of MD simulations has been constrained by several inherent challenges, including the accuracy of force fields, limitations in simulation size and timescales. One promising solution to these challenges is the integration of machine learning (ML) methods, both for improved description of the nature of interactions in aqueous systems as well as for enhanced sampling. In this review, we discuss the principles, implementation, and applications of ML force fields (MLFFs) and ML enhanced sampling methods to the study of aqueous, interfacial systems. We discuss five key categories of applications that use MLFFs, ML-enhanced sampling, and ML-driven data analytics. We first discuss how MLFFs are enabling quantum level accuracy at classical level cost for large scale simulations of complex aqueous and interfacial systems, and then highlight how coupling them with enhanced sampling and advanced data analytics, especially graph based approaches for featurizing such systems, can be used both for enhancing simulations and for understanding them by yielding reliable low dimensional reaction coordinates that improve the interpretation of high dimensional MD data. The discussed applications include investigations into the structure and dynamics of bulk water and aqueous interfaces, proton transfer, catalysis, phase transitions, and the prediction of vibrational spectra. In each case, we highlight how ML-based methods enable simulations that were previously computationally prohibitive and provide new physical insights into aqueous solutions and interfaces. For instance, MLFFs allow nanosecond-scale simulations with thousands of atoms while maintaining quantum chemistry accuracy. Additionally, ML-enhanced sampling facilitates the crossing of large reaction barriers and enables the exploration of extensive configuration spaces. Moreover, ML models trained on simulation data uncover previously overlooked factors, such as the role of solvent dynamics in phase transitions. The combination of MLFFs with enhanced sampling techniques makes the calculation of high-dimensional free energy surfaces feasible, significantly improving our understanding of chemical reactions. Finally, we discuss the current challenges in this field and outline potential future research directions to further advance the integration of ML in MD simulations.

[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

[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

[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.

[ChemRxiv] Maximizing Machine Learning Interatomic Potential Transferability for the Discovery of the Novel Stellated Octadecagon Bi18-Pt24 Cage Structure

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

Achieving true transferability remains the central challenge for Machine Learning Interatomic Potentials (ML-IAPs) in modeling complex bimetallic nanoclusters across their vast potential energy surfaces. We systematically investi- gate data selection strategies to optimize the Chebyshev Interaction Model for Efficient Simulation (ChIMES) po- tential for the Bi-Pt nanoclusters by comparing three innovative sampling methods: Principal Component Analysis (PCA)/k-means (structural diversity), t-distributed Stochastic Neighbor Embedding (t-SNE)/k-means (force-space di- versity), and hierarchical clustering. Quantitatively, the PCA/k-means strategy proved most effective for global ac- curacy, yielding the lowest force errors and achieving energy root mean square errors (RMSE) values competitive with DFT, demonstrating excellent accuracy (19.16 meV/atom). Structural validation on 34 unique DFT-optimized isomers further confirmed the potential’s high fidelity, with the best model PCA/k-means reproducing structures with an average root mean square deviation (RMSD) of 0.10 Å. However, the t-SNE methods, by maximizing diversity in the force space, demonstrated superior extrapolative power, leading to the more precise prediction of a novel stel- lated octadecagon Bi18Pt24 cage structure, demonstrating the potential for exploring previously unseen morphologies. Our results establish a clear methodology for strategic data sampling that successfully maximizes ML-IAP transfer- ability, providing an accurate and computationally efficient tool that accelerates the theoretical discovery of complex bimetallic architectures.

[ChemRxiv] High-Performance Semiempirical Excited-State Molecular Dynamics: A Step Toward Data-Driven Photodynamics

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

We introduce excited-state molecular dynamics (ESMD) in PYSEQM, a GPU-accelerated semiempirical quantum chemistry engine implemented in PyTorch. The new module enables Born-Oppenheimer (single-surface) dynamics using configuration-interaction singles and random phase approximation for excited-states, allowing long trajectories and large statistical ensembles to be simulated efficiently on a single GPU. We also implement an extended-Lagrangian ESMD (XL-ESMD) scheme that propagates auxiliary electronic variables, enabling relaxed ground and excited-state convergence thresholds without compromising energy conservation. The ESMD implementation scales smoothly from small chromophores to a nearly 900-atom dendrimer (taking 6.5 s per ESMD step). PYSEQM also supports batched execution, allowing many geometries or trajectories to be evaluated in a single GPU launch, substantially increasing throughput and making ensemble-based protocols routine. As a demonstration, we compute absorption, emission, and infrared spectra from trajectories propagated on the ground and first excited states, illustrating practical utility of PYSEQM. The XL-ESMD scheme yields identical spectra at significantly lower computational cost, establishing the role of extended Lagrangian based dynamics for efficient ESMD simulations. Beyond raw performance, PYSEQM’s Pytorch foundation provides automatic differentiation for forces, efficient GPU batching, and seamless interfacing with machine learning models. These capabilities position PYSEQM as practical platform for machine learning-augmented excited-state dynamics and lay the foundation for future data-driven nonadiabatic ESMD modeling of ultrafast spectroscopic probes.

[Applied Physics Letters Current Issue] Mixed ionic–electronic conductor based on B 16 framework

Applied Physics Letters Current Issue — Tue, 23 Dec 2025 00:00:00 GMT

Mixed ionic–electronic conductors (MIECs) are essential materials for next-generation energy storage and conversion systems. However, several limitations—including the scarcity of intrinsic MIEC materials, low ionic conductivity, and a narrow operational temperature range—restrict their practical use. Based on the experimentally synthesized MgB₄ crystal, this study designs a B₁₆ covalent framework with three-dimensionally connected channels by theoretically removing Mg atoms through computational methods. Using first-principles calculations and machine learning molecular dynamics simulations, we systematically explore its compounds' potential as intrinsic MIEC materials. The results indicate that the B₁₆ framework can stably host and transport Li⁺, Be²⁺, and Al³⁺ ions with different valences. The formations of LiB₂, BeB₄, and AlB₈ enter superionic states at 1200, 1700, and 1400 K, respectively, with ionic conductivities reaching 10⁻² S/cm, and the optimal electronic conductivities approaching 10⁴ S/cm. The framework shows good structural stability: the volume change after ion insertion is less than 12.4%, and its mechanical properties (for example, a shear modulus of 47.8 GPa) are comparable to those of traditional electrode materials like LiFePO₄ and NCM. Through defect engineering, LiB₂ with 12.5% crystal defects can lower the superionic transition temperature to 400 K while maintaining a high ionic conductivity of 1.65 × 10⁻¹ S/cm. This study offers a viable approach to designing MIEC materials and demonstrates significant potential for applications such as high-temperature solid-state batteries and electrochemical sensors.

[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).

[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

[Wiley: Advanced Functional Materials: Table of Contents] Sulfur‐Doped Bi2O3 Nanorods with Asymmetric S−Bi−O Active Sites for Highly Efficient Electrosynthesis of Formic Acid

Wiley: Advanced Functional Materials: Table of Contents — Mon, 22 Dec 2025 14:14:10 GMT

Advanced Functional Materials, EarlyView.

[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

[Wiley: Small: Table of Contents] Revealing Electronic Structure–Chemisorption Relationships for Accelerated Discovery of Aqueous Zinc Battery Additives Through Machine Learning

Wiley: Small: Table of Contents — Mon, 22 Dec 2025 12:24:02 GMT

Small, EarlyView.

[Wiley: Small: Table of Contents] Ultra‐High Capacity Density Lithium Metal Battery is Effectuated via Coupling Single Lithium‐Ion Conductor and Lithium‐Ion Sieve Within Yttrium‐Organic Framework

Wiley: Small: Table of Contents — Mon, 22 Dec 2025 11:34:32 GMT

Small, EarlyView.

[Wiley: Small: Table of Contents] Boosting Gaseous Mercury Detection via Photooxidation‐Enrichment Fluorescent Membrane with Machine Learning

Wiley: Small: Table of Contents — Mon, 22 Dec 2025 11:29:41 GMT

Small, EarlyView.

[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: Physical Sciences] SR-LLM: An incremental symbolic regression framework driven by LLM-based retrieval-augmented generation

Proceedings of the National Academy of Sciences: Physical Sciences — 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. ...

[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 ...

[Wiley: Angewandte Chemie International Edition: Table of Contents] Potential‐Tailored Aryl–Sodium Reagent with Moderate Ionic Binding Strength Enables Precise yet Fast Hard Carbon Presodiation

Wiley: Angewandte Chemie International Edition: Table of Contents — Mon, 22 Dec 2025 05:36:53 GMT

Angewandte Chemie International Edition, EarlyView.

[npj Computational Materials] Machine learning interatomic potential can infer electrical response

npj Computational Materials — Mon, 22 Dec 2025 00:00:00 GMT

npj Computational Materials, Published online: 22 December 2025; doi:10.1038/s41524-025-01911-z

Machine learning interatomic potential can infer electrical response

[Applied Physics Letters Open Issues] High-throughput sensing of single-cell properties using parallel multi-stage cell deformation

Applied Physics Letters Open Issues — Mon, 22 Dec 2025 00:00:00 GMT

Biophysical properties of single cells serve as label-free, noninvasive biomarkers for phenotyping. However, most techniques measure quantities dependent on multiple biophysical properties rather than individual ones, limiting their biological/clinical relevance. Here, we present a single-cell biophysical phenotyping technique that quantifies size (Dc) and elastic modulus (E) of cells deforming at different levels along constriction microchannels. A physical model is developed to resolve features of multiple deformation stages into Dc and E. Parallel-channel device design achieves a reasonably high throughput of ∼10⁴ cells/min. The measurement employs lock-in amplification-assisted electrokinetic sensing via embedded electrodes across three channel sections with varying constriction widths, inducing distinct cell deformations. We demonstrate the technique by profiling normal and cancerous breast cell lines (MCF-10A, MCF-7, and MDA-MB-231), as well as drug-treated cancer cells (cytochalasin D, cetuximab, and lysophosphatidic acid). The biophysical phenotyping enables cell classification with high accuracy (>95.45% via principal component analysis; >97.32% via machine learning). This approach offers robust and high-throughput cell classification, with potential applications in basic research and clinical diagnostics.

[Applied Physics Letters Open Issues] Coherent phonon tunneling-driven ultralow and non-monotonic thermal conductivity in quasi-0D Cs 3 Cu 2 I 5

Applied Physics Letters Open Issues — Mon, 22 Dec 2025 00:00:00 GMT

Low-dimensional copper halides have emerged as promising thermoelectric materials due to their phonon-glass electron-crystal behavior, yet their thermal transport mechanisms remain insufficiently understood. Using ab initio calculations and a unified thermal transport theory, we identify that Cs₃Cu₂I₅ possesses an ultralow lattice thermal conductivity (κ_L) of 0.126 W/(m K) at room temperature (RT)—one of the lowest among metal halides. Above RT, coherent phonon tunneling dominates over particle-like propagation, resulting in glass-like κ_L in all directions. Intriguingly, the competing contributions of coherent and incoherent terms induce an anomalous non-monotonic temperature dependence of κ_L along the c axis, with an initial decrease followed by an unexpected rise. This behavior arises from strong anharmonicity and dense flat phonon dispersions, driven by hierarchical bonding and structural complexity. Our work uncovers unconventional heat transport mechanisms in complex crystals with strong anharmonicity.

[Applied Physics Letters Open Issues] Machine learning-assisted performance prediction of ZnMSnO-based thin-film transistors

Applied Physics Letters Open Issues — Mon, 22 Dec 2025 00:00:00 GMT

Amorphous oxide semiconductors (AOS) are highly promising for optoelectronic devices due to their exceptional electrical properties and optical transparency. However, a significant barrier to their development is the lack of a comprehensive materials database, which hinders systematic studies and the discovery of emergent AOS materials. This study addresses this gap by constructing a dedicated database of zinc-tin-based doped oxide semiconductors (Zn–M–Sn–O) and their thin-film transistor (TFT) performance parameters, compiled from an extensive review of existing literature. Our research aims to perform a systematic analysis of the correlations between key material properties and device performance. We summarize and analyze existing Zn–M–Sn–O based optoelectronic devices, extracting key features such as material compositions, processing parameters, and performance metrics. These features are then used to construct feature vectors. By applying a machine learning algorithm to this dataset, we establish a performance prediction model for Zn–M–Sn–O TFTs. This machine learning-assisted approach allows us to efficiently screen materials and predict the optimal M element for high-performance devices. This methodology significantly accelerates the discovery and development of advanced AOS materials, paving the way for next-generation optoelectronic technologies.

[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.

[ChemRxiv] Missense mutations in cancer: in silico predictions, developing treatments, and overcoming cell resistance.

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

Targeted therapies built around specific genetic driver mutations have become a cornerstone of precision oncology. These mutations, often found in oncogenes such as KRAS or tumor suppressors such as TP53, contribute to tumor initiation, progression, and therapeutic resistance. Recent successes with KRAS inhibitors targeting G12C and G12D mutations highlight the clinical potential of mutation-specific drug design. Concurrently, advances in machine learning have enhanced the prediction of missense variant effects by integrating amino acid dynamics, structural perturbations, and pathogenicity scoring. This review synthesizes current computational tools and emerging therapeutic strategies, including small-molecule inhibitors, protein degraders, proximity-based therapeutics, and gene or cellular therapies, to provide a comprehensive framework linking structure–function relationships to the rational design of next-generation cancer treatments.

[ChemRxiv] Domain Oriented Universal Machine Learning Potential Enables Fast Exploration of Chemical Space of Battery Electrolytes

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

Li-ion batteries, widely used in electronic devices, electric vehicles, and aviation, demand high energy density, fast charging capabilities, and broad operating temperature ranges. Computations combined with experiments have gained increasing attention for electrolyte development. However, the inherent complexity of electrolytes poses a significant challenge. Classical molecular dynamics often fails due to inaccuracies in force field parameters, while ab initio calculationsarelimitedbyhighcomputationalcosts. Recently, machinelearning molecular dynamics has emerged as an efficient and accurate alternative. However, its application is hindered by limited transferability of machine learning potentials. In this work, we developed a universal machine learning potential for electrolytes using an iterative training approach on randomly composed datasets, enabling the accurate computation of key properties for a broad range of electrolytes via molecular dynamics. Furthermore, coordination dynamics analysis of Li ion, by quantifying the coordination lifetime, provides a direct, quantitative measure of solvation strength. The universal machine learning potential for electrolytes facilitates the prediction and optimization of electrolyte properties, offering a powerful tool for electrolyte design.

[ChemRxiv] Large-scale structure- and sequence-based comparative analysis enables functional annotation of animal venom peptides

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

Animal venoms constitute one of the most chemically diverse and pharmacologically rich peptide repertoires in nature, yet the vast majority of venom peptides remain structurally and functionally unannotated due to limited material availability and a paucity of experimentally solved structures. This knowledge gap has significantly constrained both mechanistic understanding and translational applications of venom-derived molecules. Here we conduct a comprehensive structure- and sequence-based comparative analysis of close to 4,000 venom peptides (i.e. teretoxins and conotoxins) from marine snails using AlphaFold2. While conotoxins have been studied for over six decades, teretoxins remain largely unexplored. Our study provides the first comprehensive structural classification and functional annotation of the entire known teretoxin repertoire, substantially expanding the venom peptide landscape. Structural clustering analysis revealed that both teretoxins and conotoxins form a large number of structural clusters (225 and 307 clusters, respectively), with each cluster characterized by conserved cysteine frameworks and disulfide connectivity. Importantly, combining structural clusters with phylogenetic analysis revealed that structure prediction together with cysteine frameworks offer an improved strategy for venom peptide classification. We used predicted disulfide connectivity from derived from AlphaFold2 models to annotate the majority of uncharacterized conotoxins on ConoServer database, addressing a long-standing classification bottleneck in venom peptide classification efforts. As a concrete demonstration of functional prediction, co-clustering of predicted teretoxin and conotoxin structures led to the identification of teretoxins structurally homologous to conotoxins with described activities, suggesting shared functional activities such as Kunitz-type peptide activity, neurotoxicity, and ion channel inhibition. Using docking and molecular dynamics (MD) simulations on our Kunitz-type peptide co-cluster, we observed that conotoxin P07849 and teretoxin Tar2.9 engage trypsin in a manner similar to the Kunitz Domain 1 (KD1) of the Alzheimer’s amyloid beta-protein precursor (APPI), a previously unappreciated protease inhibitory role for this venom peptide family. Overall, this work establishes a scalable, structure-centric framework that combines structure prediction, structural clustering, co-clustering, and phylogenetics for deorphanizing venom peptides, enabling predicted annotation and functional inference for future experimental, pharmacological, and therapeutic exploration.

[ChemRxiv] Performance of dispersion models in predicting ambient hydrocarbon concentrations at a regional air quality monitor in an oil and gas producing region

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

The performance of four widely used dispersion models (AERMOD, a single equation Gaussian formulation, and two versions of CALPUFF) for predicting ambient hydrocarbon concentrations at a regional air quality monitor in the Eagle Ford Shale oil and gas production region was assessed. Model performance is found to vary considerably based on the performance objective, meteorological conditions, and temporal resolution. Among the models evaluated in this work, the methods used to estimate the dispersion coefficients and whether the model was plume- or puff-based strongly influenced model performance. Uncertainties in meteorological and emissions inputs also played an important role in model performance, but the significance of their impact varied depending on the performance objective. Techniques to identify and address model uncertainties and for selecting the best performing model for a given application are suggested.

[ChemRxiv] Machine Learning-Guided Scope Selection to Balance Performance and Substrate Similarity

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

The determination of a reaction substrate scope enables downstream users to decide whether the reaction in question is suitable for their envisioned application. The information content of the scope, as demonstrated by its performance and diversity, is crucial to inform the quality of this decision. Herein, we report a broadly applicable and easy to use machine learning algorithm, ScopeBO, for the selection of scopes that balance these two aspects. We use the Vendi score as a metric for scope diversity and establish a scope score that quantifies scope performance within the context of a specific chemical search space. The hyperparameters of ScopeBO are optimized using these metrics, and its performance is validated with several reaction datasets, demonstrating favorable performance against that of alternative selection methods. Through this quantitative optimization, ScopeBO provides an approach towards objective and standardized scope selection that maximizes the information content of the evaluated substrates.

[ChemRxiv] Relationship Between Local Disorder and Atomic Motion in an Antiperovskite Solid Electrolyte

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

Solid-state electrolytes are an alternative to conventional liquid systems for safer and more efficient batteries, whereas a shift from Li to Na would unlock systems with increased resource availability and simplified supply chains. Antiperovskite Na2[NH2][BH4] recently emerged as a candidate that showcases that internal polyanion dynamics can facilitate Na transport. However, experimental validation of the predicted mechanisms of atomic motion remains scarce. To this end, we investigate the intricate relationship between local atomic disorder and dynamic motion. Maps of atomic disorder from total scattering data reproduce the predictions of polyanion rotation and Na translation by ab initio molecular dynamics (AIMD). The comparison also reveals the concurrence of BD4- translations, which were overlooked in previous analyses, and the existence of static disorder due to the different degrees of freedom of each anion, even while maintaining their ideal shape. This complex interplay refines mechanisms governing ion dynamics that determine solid-state electrolyte functionality. Realistic design strategies for rotor-based electrolytes must explicitly account for polyanion translation and static disorder, rather than optimizing rotational freedom in isolation. The combination of total scattering experiments with AIMD provides a route to screen potential polyanion-based candidates for favorable multi-modal disorder, thus steering the discovery of new phases with transformational ionic conductivity.

[ChemRxiv] Multi-Objective Catalyst Discovery in High-Entropy Alloy Composition Space: The Role of Noble Metals on the Pareto Front for Oxygen Reduction Reaction

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

Discovering new materials for electrocatalytic energy conversion reactions is a key step toward energy sustainability. However, for catalysts to be viable in practice, they must perform in multiple, potentially conflicting objectives. We demonstrate this challenge for the acidic oxygen reduction reaction (ORR), where activity, stability, and material cost must be balanced. Using the continuous composition space of high-entropy alloys (HEAs) together with our established models for activity and dissolution, we identify a Pareto-optimal set of ORR catalysts within the Ag–Au–Cu–Ir–Pd–Pt–Rh–Ru system via multi-objective Bayesian optimization. Additionally, we introduce a fine-tuned machine learning model that predicts adsorption energies for alloys spanning 12 elements and 9 adsorbates. Our results show that alloying expands the hypervolume spanned by the Pareto front, consisting of low- to medium-entropy alloys composed primarily of Ag, Au, Cu, Pd, and Pt. We further propose an approach for analyzing the Pareto front by quantifying the loss in hypervolume when critical elements (Au, Pd, and Pt) are removed, clarifying their relative contributions to optimal performance. This work highlights the need to consider all relevant objectives in catalyst optimization and the advantage of HEAs as a powerful platform for multi-objective catalyst discovery.

[ChemRxiv] Agent-based framework for modeling hyperlocal urban air quality

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

Urban air quality exhibits significant spatial and temporal heterogeneity at hyperlocal scales, necessitating advanced modeling paradigms that can bridge the gap between computationally intensive physics-based models and empirically-driven statistical approaches. This paper introduces a novel agent-based modeling framework specifically designed for hyperlocal air quality assessment, capable of providing descriptive, predictive, and prescriptive analysis. The proposed framework discretizes urban environments into interacting agents, with pollutant dynamics governed by a parameterized mass balance that preserves fundamental physics while maintaining computational efficiency. The framework is demonstrated through a case study in Chennai, India, using mobile monitoring data across a 25 km route with 250 m spatial resolution. Geospatial features (traffic, land use, meteorology) are encoded as agent properties through physically interpretable parameters. This enables transparent attribution of pollution sources and transport pathways, thereby strengthening the framework’s descriptive capabilities. The approach successfully captures complex spatio-temporal pollution dynamics and describes pollution hotspots by attributing them to source and transport influences. Predictive capabilities are demonstrated through spatio-temporal interpolation and temporal forecasting. Spatio-temporal formulation of the framework enables it to outperform regular unidimensional methods. The discrete agent structure facilitates prescriptive applications, demonstrated here through identification of least-exposure routes between locations. The unification of descriptive, predictive and prescriptive capabilities within a single interpretable framework makes it a potentially valuable tool for urban environmental management and real-time decision support systems.

[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.

[ACS Nano: Latest Articles (ACS Publications)] [ASAP] Feature-Selective Preprocessing with Electrically Robust Boron Nitride-Based Dynamic Memristors for Reliable Lightweight Neural Networks

ACS Nano: Latest Articles (ACS Publications) — Sun, 21 Dec 2025 18:05:25 GMT

ACS Nano

DOI: 10.1021/acsnano.5c16967

[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

[npj Computational Materials] Free-energy perturbation in the exchange-correlation space accelerated by machine learning: application to silica polymorphs

npj Computational Materials — Sat, 20 Dec 2025 00:00:00 GMT

npj Computational Materials, Published online: 20 December 2025; doi:10.1038/s41524-025-01874-1

Free-energy perturbation in the exchange-correlation space accelerated by machine learning: application to silica polymorphs

[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.

[Wiley: Small: Table of Contents] Unravelling Electronic Structure and Molecular Vibrations of Proteins in Virus Using Novel Correlated Plasmon‐Enhanced Raman Spectroscopy With Machine Learning

Wiley: Small: Table of Contents — Fri, 19 Dec 2025 11:08:23 GMT

Small, EarlyView.

[Recent Articles in Phys. Rev. B] Vision transformer neural quantum states for impurity models

Recent Articles in Phys. Rev. B — Fri, 19 Dec 2025 10:00:00 GMT

Author(s): Xiaodong Cao, Zhicheng Zhong, and Yi Lu

Transformer neural networks, known for their ability to recognize complex patterns in high-dimensional data, offer a promising framework for capturing many-body correlations in quantum systems. We employ an adapted vision transformer (ViT) architecture to model quantum impurity models, optimizing it…

[Phys. Rev. B 112, 235155] Published Fri Dec 19, 2025

[Recent Articles in Phys. Rev. B] Renormalized quantum anharmonicity enhanced electron-phonon coupling in the ambient-pressure compound $\mathrm{Rb}{\mathrm{H}}_{6}$

Recent Articles in Phys. Rev. B — Fri, 19 Dec 2025 10:00:00 GMT

Author(s): Zhongyu Wan, Guo-Hua Zhong, Ruiqin Zhang, and Hai-Qing Lin

Hydrogen-based compounds are promising candidates for room-temperature superconductivity. However, hydrogen-related anharmonic quantum effects have created a huge gap between experiments and theories. The compound $\mathrm{Rb}{\mathrm{H}}_{6}$ exemplifies the impacts of quantum fluctuations and latt…

[Phys. Rev. B 112, L220504] Published Fri Dec 19, 2025

[Wiley: Angewandte Chemie International Edition: Table of Contents] Dual‐Function Antibacterial and Antibiofilm Agent Based on a Confinement‐Activated Fluorescent System in Water

Wiley: Angewandte Chemie International Edition: Table of Contents — Fri, 19 Dec 2025 05:42:28 GMT

Angewandte Chemie International Edition, EarlyView.

[Wiley: Advanced Science: Table of Contents] Quantifying Additive Manufacturing Vapor Plumes Using Laser‐Induced Breakdown Spectroscopy, Synchrotron X‐Ray Radiography and Simulations

Wiley: Advanced Science: Table of Contents — Fri, 19 Dec 2025 03:30:01 GMT

Advanced Science, EarlyView.

[npj Computational Materials] Publisher Correction: Machine learning potentials for alloys: a detailed workflow to predict phase diagrams and benchmark accuracy

npj Computational Materials — Fri, 19 Dec 2025 00:00:00 GMT

npj Computational Materials, Published online: 19 December 2025; doi:10.1038/s41524-025-01913-x

Publisher Correction: Machine learning potentials for alloys: a detailed workflow to predict phase diagrams and benchmark accuracy

[npj Computational Materials] Alloy design integrating natural language processing and machine learning: breakthrough development of low-cost, high-performance Ni-based single-crystal superalloys

npj Computational Materials — Fri, 19 Dec 2025 00:00:00 GMT

npj Computational Materials, Published online: 19 December 2025; doi:10.1038/s41524-025-01906-w

Alloy design integrating natural language processing and machine learning: breakthrough development of low-cost, high-performance Ni-based single-crystal superalloys

[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.

[RSC - Digital Discovery latest articles] Adsorb-Agent: Autonomous Identification of Stable Adsorption Configurations via Large Language Model Agent

RSC - Digital Discovery latest articles — Fri, 19 Dec 2025 00:00:00 GMT

Digital Discovery, 2025, Accepted Manuscript
DOI: 10.1039/D5DD00298B, Paper

Open Access

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

Janghoon Ock, Radheesh Sharma Meda, Tirtha Vinchurkar, Yayati Jadhav, Amir Barati Farimani
Adsorption energy is a key reactivity descriptor in catalysis. Determining adsorption energy requires evaluating numerous adsorbate-catalyst configurations, making it computationally intensive. Current methods rely on exhaustive sampling, which does not...
The content of this RSS Feed (c) The Royal Society of Chemistry

[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Instant Prediction of Moire Superlattice Relaxation in Twisted Bilayers of Transition Metal Dichalcogenides Using Different Neural Network Architectures

The Journal of Physical Chemistry C: Latest Articles (ACS Publications) — Thu, 18 Dec 2025 11:50:58 GMT

The Journal of Physical Chemistry C

DOI: 10.1021/acs.jpcc.5c07169

[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

[Recent Articles in Phys. Rev. B] One-defect one-potential strategy for accurate machine learning prediction of phonons in defect-containing supercells

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

Author(s): Junjie Zhou, Xinpeng Li, Menglin Huang, and Shiyou Chen

Atomic vibrations play a critical role in phonon-assisted electronic transitions at defects in solids. However, accurate phonon calculations in defect-laden systems are often hindered by the high computational cost of large-supercell first-principles calculations. Recently, foundation models, such a…

[Phys. Rev. B 112, 235205] 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 ...

[Proceedings of the National Academy of Sciences: Physical Sciences] Heavy-tailed update distributions arise from information-driven self-organization in nonequilibrium learning

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.
SignificanceArtificial neural networks can adapt to tasks while freely exploring possible solutions, similar to how humans balance curiosity with goal-driven behavior. We show that during training, such networks naturally operate near a critical state. ...

[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.

[Wiley: Advanced Functional Materials: Table of Contents] Homogeneous Microphase Structure and Polymer‐Dominated Ion Transport Network Enable Durable Quasi‐Solid‐State Sodium Metal Batteries

Wiley: Advanced Functional Materials: Table of Contents — Thu, 18 Dec 2025 05:55:56 GMT

Advanced Functional Materials, EarlyView.

[Nature Machine Intelligence] A psychometric framework for evaluating and shaping personality traits in large language models

Nature Machine Intelligence — Thu, 18 Dec 2025 00:00:00 GMT

Nature Machine Intelligence, Published online: 18 December 2025; doi:10.1038/s42256-025-01115-6

Serapio-García, Safdari and colleagues develop a method based on psychometric tests to measure and validate personality-like traits in LLMs. Large, instruction-tuned models give reliable personality measurement results, and specific personality profiles can be mimicked in downstream tasks.

[Wiley: Small: Table of Contents] Probing Lattice Anharmonicity and Thermal Transport in Ultralow‐κ Materials Using Machine Learning Interatomic Potentials

Wiley: Small: Table of Contents — Wed, 17 Dec 2025 20:22:25 GMT

Small, EarlyView.

[Wiley: Small: Table of Contents] Ion Migration Control in Lead‐Free Halide Perovskite Transistors for Logic and Neuromorphic Circuits

Wiley: Small: Table of Contents — Wed, 17 Dec 2025 19:52:40 GMT

Small, EarlyView.

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Convergent Evolution: Self-Assembly of Small Molecule, Polymeric, and Inorganic Contrast Agents toward Advanced MRI

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Wed, 17 Dec 2025 19:33:22 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.4c11767

[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

[ACS Nano: Latest Articles (ACS Publications)] [ASAP] An Ultralong-Circulating Tantalum-Based Computed Tomography Contrast Agent for Vascular Imaging in Large Animals

ACS Nano: Latest Articles (ACS Publications) — Wed, 17 Dec 2025 17:49:52 GMT

ACS Nano

DOI: 10.1021/acsnano.5c13773

[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.

[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Learning to learn ecosystems from limited data

Wed, 17 Dec 2025 08:00:00 GMT

Proceedings of the National Academy of Sciences, Volume 122, Issue 51, December 2025.
SignificanceIn recent years, machine learning has been successfully applied to complex and nonlinear dynamical systems for improved prediction of the future state, but ecological systems represent a great challenge because of the scarcity of the ...

[Wiley: Advanced Functional Materials: Table of Contents] Smart Wound Management System Capable of On‐Chip Machine Learning and Closed‐Loop Therapeutic Feedback

Wiley: Advanced Functional Materials: Table of Contents — Wed, 17 Dec 2025 06:06:36 GMT

Advanced Functional Materials, EarlyView.

[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

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Highly Accurate and Fast Prediction of MOF Free Energy via Machine Learning

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Tue, 16 Dec 2025 17:08:56 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c13960

[Wiley: Angewandte Chemie International Edition: Table of Contents] Mechanically Robust Bilayer Solid Electrolyte Interphase Enabled by Sequential Decomposition Mechanism for High‐Performance Micron‐Sized SiOx Anodes

Wiley: Angewandte Chemie International Edition: Table of Contents — Tue, 16 Dec 2025 15:14:44 GMT

Angewandte Chemie International Edition, Volume 64, Issue 51, December 15, 2025.

[Wiley: Angewandte Chemie International Edition: Table of Contents] Machine Learning‐Driven Automated Synthesis of Polysubstituted Gentisaldehydes

Wiley: Angewandte Chemie International Edition: Table of Contents — Tue, 16 Dec 2025 15:14:44 GMT

Angewandte Chemie International Edition, Volume 64, Issue 51, December 15, 2025.

[Wiley: Angewandte Chemie International Edition: Table of Contents] Uphill Anion Transporters with Ultrahigh Efficiency Based on N‐Heterocyclic Carbene Metal Complexes

Wiley: Angewandte Chemie International Edition: Table of Contents — Tue, 16 Dec 2025 15:14:44 GMT

Angewandte Chemie International Edition, Volume 64, Issue 51, December 15, 2025.

[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 ...

[Applied Physics Letters Current Issue] Ultrafast laser-induced anharmonic lattice dynamics and nonlinear optical modulation in croconic acid

Applied Physics Letters Current Issue — Tue, 16 Dec 2025 00:00:00 GMT

Ultrafast laser excitation offers a powerful means to modulate material properties on femtosecond timescales. Here, we investigate croconic acid, a hydrogen-bonded organic ferroelectric, using real-time time-dependent density functional theory to uncover the microscopic mechanisms of light-induced structural transitions and nonlinear optical responses. High-order harmonic generation in croconic acid is found to be highly sensitive to proton displacement within hydrogen bonds, with polarization switching reshaping internal electronic asymmetry and modulating intersite electron currents. Subangstrom-scale lattice distortions induce marked enhancements or suppressions in the harmonics, highlighting the extreme sensitivity of the nonlinear response to hydrogen-bond configuration. These results reveal a light-driven electron–proton–lattice interaction mechanism in organic ferroelectrics, providing a route toward tunable ultrafast photonic and optoelectronic devices based on molecular materials.

[Applied Physics Letters Current Issue] Spin-splitting-torque-driven field-free perpendicular magnetization switching in RuO 2 /synthetic antiferromagnet heterostructures for spintronic convolutional neural networks

Applied Physics Letters Current Issue — Tue, 16 Dec 2025 00:00:00 GMT

With the growing demand for low-power and high-speed spintronic devices, the development of advanced material systems with efficient spin control capabilities has emerged as a central focus in spintronics research. Here, we propose a fully antiferromagnetic device architecture based on a magnetically compensated RuO₂/synthetic antiferromagnet heterostructure, achieving fully electrical writing and reading functionalities. This design, characterized by its negligible stray field and deterministic field-free switching, is inherently suitable for large-scale neuromorphic integration. In a proof-of-concept demonstration, we showcase the implementation of an all-spintronic convolutional neural network using this architecture, achieving a high recognition accuracy of 98.7% on the handwritten digit classification task.

[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] Integrative Analysis of Transcriptomic Data Reveals a Predictive Gene Signature for Chemoradiotherapy Response in Rectal Cancer

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

Locally advanced rectal cancer (LARC) is treated with neoadjuvant chemoradiotherapy (nCRT), but only a minority of patients achieve a pathological complete response (pCR). Predictive biomarkers of response could help guide treatment decisions, yet none have reached clinical practice. In this exploratory study, we integrated six publicly available transcriptomic datasets and applied machine learning to derive a 186-gene signature predictive of nCRT response. The signature showed good performance in cross-validation (AUC 0.80) and was associated with consensus molecular (CMS4) and immune (iCMS3) subtypes enriched in responders.

[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.

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Anharmonic Coupling in a Strong Intramolecular H-Bond System: Contributions to Static and Time-Resolved Vibrational Spectra

The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications) — Mon, 15 Dec 2025 17:03:38 GMT

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03487

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Rhodium-Catalyzed Atroposelective Alkyne Oxyamidation Using Non-Nitrene NH Acyloxyamide Reagents

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Mon, 15 Dec 2025 15:14:00 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c18495

[Proceedings of the National Academy of Sciences: Physical Sciences] CHARGE-MAP: An integrated framework to study the multicriteria EV charging infrastructure expansion problem

Proceedings of the National Academy of Sciences: Physical Sciences — Mon, 15 Dec 2025 08:00:00 GMT

Proceedings of the National Academy of Sciences, Volume 122, Issue 51, December 2025.
SignificanceThe surge in electric vehicle (EV) adoption has presented us the challenge of developing accessible and cost-effective charging infrastructures. To address this challenge, we present a frameworkcharge-map. It consists of: i) an agent-based ...

[Wiley: Chinese Journal of Chemistry: Table of Contents] 2‐Trifluoromethyl‐Benzimidazolium Salt as a Dual‐Function Reagent for Deoxytrifluoromethylation of Benzyl Alcohols

Wiley: Chinese Journal of Chemistry: Table of Contents — Mon, 15 Dec 2025 07:33:14 GMT

Chinese Journal of Chemistry, Volume 44, Issue 2, Page 177-182, 15 January 2026.

[Applied Physics Letters Current Issue] Covalent bond chemistry enabling M 2 CN 2 MXenes as anode materials for halide-ion batteries

Applied Physics Letters Current Issue — Mon, 15 Dec 2025 00:00:00 GMT

The development of halide-ion batteries is limited by the lack of efficient electrode materials. Two-dimensional M₂CN₂ MXenes are promising anode candidates due to their structural flexibility and low molar mass, yet their stability and storage mechanism remain unclear. Using first-principles calculations, we identify Ti₂CN₂, Nb₂CN₂, and Ta₂CN₂ as stable MXenes. Ti₂CN₂ exhibits excellent performance with low voltages (0.07 V for F⁻) and high specific capacities (394.8 mAh/g for F⁻). The storage mechanism involves covalent bonding between surface N and halide-ions, where adsorption strength is governed by the energy difference between occupied σ^* and unoccupied π^* orbitals and their electron overlap. Moreover, O or Zr doping significantly enhances halide-ion diffusion kinetics. This work elucidates the covalent bond-mediated storage in M₂CN₂ MXenes and guides the design of high-performance halide-ion battery electrodes.

[Applied Physics Letters Current Issue] Measurement of multiple mechanical properties from multi-dimensional signals in nanosecond laser ablation via PINN

Applied Physics Letters Current Issue — Mon, 15 Dec 2025 00:00:00 GMT

Accurate evaluation of mechanical properties in steels under ageing or service conditions remains a major challenge. We propose a thermo-mechanical coupling framework for nanosecond laser ablation based on energy conservation, which is embedded into a physics-informed neural network (PINN) to enable simultaneous inversion of multiple mechanical properties. A thermo-mechanical coupling coefficient is defined to uniformly describe the dynamic allocation of input laser energy among thermal diffusion, mechanical work, and plasma shielding across different deformation stages under laser irradiation. Furthermore, hard-to-measure physical characteristics in the coupled equation are replaced with experimentally accessible features obtained through the simultaneous acquisition of spectroscopic, shockwave, and surface-wave signals. Using 210 experimental datasets, the framework simultaneously recovers Young's modulus, yield strength, ultimate tensile strength, and micro-Vickers hardness with high accuracy (R² = 0.9927, 0.9912, 0.9916, and 0.9959, respectively), significantly outperforming the baseline method (ultrasonic velocity regression for E, R² = 0.0012). Comparisons with linear normalization and unconstrained neural networks demonstrate that PINN achieves near-unity accuracy through the embedding of conservation-law constraints. Partial dependency analysis further uncovers the nonlinear coupling laws between input features and mechanical properties. The proposed paradigm, integrating conservation laws, measurable features, and physics-informed learning, offers a universal approach for non-contact, high-precision, and physically consistent multi-to-multi inversion of multiple material properties under nanosecond laser ablation conditions.

[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...
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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

Dysphagia in older adults causes serious complications, and efficient and scalable screening are needed. This prospective multicenter study developed interpretable machine learning (ML) models for the early identification and staging of dysphagia. Nine ML models were built using the clinical data from 1,235 patients and externally validated on 720 patients. All patients were older adults from seven Suzhou hospitals whose dysphagia was confirmed via Videofluoroscopic Swallowing Studies. Features were selected via random forest, and model interpretability was analyzed with SHapley Additive exPlanations (SHAP).

[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.

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Unraveling the Humidity-Induced Phase Transition in CALF-20 via Machine Learning Potentials

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Sat, 13 Dec 2025 14:29:51 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c18944

[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.

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Vinylsilanes as Chain-Transfer Agents in Ethylene Polymerization: Direct Synthesis of Heterotelechelic Polyolefins

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Fri, 12 Dec 2025 16:44:30 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c15808

[The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications)] [ASAP] Compression-Induced Lattice Tilting Quenches Ion Migration at Metal Halide Perovskite Grain Boundaries: A Machine Learning Molecular Dynamics Study

The Journal of Physical Chemistry Letters: Latest Articles (ACS Publications) — Fri, 12 Dec 2025 15:21:13 GMT

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03637

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Machine Learning-Assisted Crystal Structure Prediction of Solid-State Electrolytes Reveals Superior Ionic Conductivity in Metastable Edge-Sharing Phases

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Fri, 12 Dec 2025 14:36:09 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c15665

[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

[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Spiking world model with multicompartment neurons for model-based reinforcement learning

Fri, 12 Dec 2025 08:00:00 GMT

Proceedings of the National Academy of Sciences, Volume 122, Issue 50, December 2025.
SignificanceDendritic computation is key to the brain’s ability to integrate information over long timescales. Inspired by this, this study proposes a spiking neural network model that embeds dendritic mechanisms to enhance long-term memory and planning. ...

[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.

[Nature Machine Intelligence] LLM use in scholarly writing poses a provenance problem

Nature Machine Intelligence — Fri, 12 Dec 2025 00:00:00 GMT

Nature Machine Intelligence, Published online: 12 December 2025; doi:10.1038/s42256-025-01159-8

LLM use in scholarly writing poses a provenance problem

[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] Contextualized biomedical language processing enhances ICU survival prediction

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

Accurate prediction of intensive care unit (ICU) survival remains challenging due to heterogeneous clinical data. This study shows that contextualized biomedical language processing markedly enhances ICU survival prediction. Multimodal models integrating structured laboratory data with unstructured text (chief complaints and ICD entries) were trained and validated using MIMIC-IV, MIMIC-III, and eICU datasets. The BioBERT-enhanced convolutional neural network achieved AUROCs of 0.889 (Strict Cohort, n=5,795) and 0.974 (Lenient Cohort, n=58,615) during external validation.

[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.

[The Journal of Physical Chemistry C: Latest Articles (ACS Publications)] [ASAP] Dual Structure-Directing Agents for Superstructure Formation in PtCoCu Ternary Alloy Electrocatalysts

The Journal of Physical Chemistry C: Latest Articles (ACS Publications) — Thu, 11 Dec 2025 15:46:59 GMT

The Journal of Physical Chemistry C

DOI: 10.1021/acs.jpcc.5c06519

[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.

[Wiley: Advanced Science: Table of Contents] H2S Is a Potential Universal Reducing Agent for Prx6‐Type Peroxiredoxins

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] Luminescent Nanocucurbits Enable Spatiotemporal Co‐Delivery of Hydrophilic and Hydrophobic Chemotherapeutic Agents

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

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.

[Wiley: Advanced Functional Materials: Table of Contents] Surface Fluidic Microneedle Patches for Lymphatic Delivery of Diagnostic and Therapeutic Agents (Adv. Funct. Mater. 50/2025)

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] Bonyzymes: Efficient Anti‐Inflammatory, Antibacterial and Osteogenic Agents for Peri‐Implantitis Reconstruction Treatment

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] Surface Fluidic Microneedle Patches for Lymphatic Delivery of Diagnostic and Therapeutic Agents

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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[Journal of Chemical Theory and Computation: Latest Articles (ACS Publications)] [ASAP] A Universal Augmentation Framework for Long-Range Electrostatics in Machine Learning Interatomic Potentials

Journal of Chemical Theory and Computation: Latest Articles (ACS Publications) — Wed, 10 Dec 2025 10:12:04 GMT

Journal of Chemical Theory and Computation

DOI: 10.1021/acs.jctc.5c01400

[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.

[Applied Physics Reviews Current Issue] Construction of polar topological nanodevices for neuromorphic computing

Applied Physics Reviews Current Issue — Wed, 10 Dec 2025 00:00:00 GMT

The research field of polar topological domains has witnessed rapid expansion in recent years, inspired by the vast application potentials for future topological electronic devices. Nonetheless, such topological devices remain elusive. In this study, we implemented the polar topological domain structures as neuromorphic computing elements, and present 12-state non-volatile ferroelectric topological nanodevices that demonstrate exceptional neuromorphic computing capabilities through the controlled formation and erasure of walls. These nanodevices exhibit near-linear long-term potentiation and long-term depression characteristics under repetitive voltage pulses, achieving a remarkable dynamic range. Simulations using a convolutional neural network model with these devices attain 95% recognition accuracy on the Modified National Institute of Standards and Technology handwritten digits dataset within 100 epochs. These results expand the functional scope of polar topological electronic devices to future neuromorphic computing systems.

[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...
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[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Machine Learning-Guided Decoding Bioelectronic Signals of Photosynthetic Cyanobacterial Cells by Conducting Polymers

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Tue, 09 Dec 2025 15:33:56 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c13150

[Recent Articles in Phys. Rev. Lett.] Connection between Memory Performance and Optical Absorption in Quantum Reservoir Computing

Recent Articles in Phys. Rev. Lett. — Tue, 09 Dec 2025 10:00:00 GMT

Author(s): Niclas Götting, Steffen Wilksen, Alexander Steinhoff, Frederik Lohof, and Christopher Gies

Quantum reservoir computing (QRC) offers a promising paradigm for harnessing quantum systems for machine learning tasks, especially in the era of noisy intermediate-scale quantum devices. While information-theoretical benchmarks like short-term memory capacity (STMC) are widely used to evaluate QRC …

[Phys. Rev. Lett. 135, 240403] Published Tue Dec 09, 2025

[Wiley: Advanced Energy Materials: Table of Contents] Ultrahigh‐Rate Lithium Storage in MoS2 Enabled by Isotropic Ion Transport and Fe‐Atomic Site Conversion

Wiley: Advanced Energy Materials: Table of Contents — Tue, 09 Dec 2025 08:32:12 GMT

Advanced Energy Materials, EarlyView.

[Wiley: Advanced Energy Materials: Table of Contents] Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

Wiley: Advanced Energy Materials: Table of Contents — Tue, 09 Dec 2025 07:26:17 GMT

Advanced Energy Materials, EarlyView.

[Wiley: Advanced Intelligent Discovery: Table of Contents] Enhancing Synaptic Plasticity in Strontium Titanate‐Based Sensory Processing Devices: A Study on Oxygen Vacancy Modulation and Performance in Artificial Neural Networks

Wiley: Advanced Intelligent Discovery: Table of Contents — Tue, 09 Dec 2025 01:16:19 GMT

Advanced Intelligent Discovery, Volume 1, Issue 3, December 2025.

[RSC - Digital Discovery latest articles] Multi-agentic AI framework for end-to-end atomistic simulations

RSC - Digital Discovery latest articles — Tue, 09 Dec 2025 00:00:00 GMT

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00435G, Paper

Open Access

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

Aikaterini Vriza, Uma Kornu, Aditya Koneru, Henry Chan, Subramanian K. R. S. Sankaranarayanan
Autonomous multi-agent AI system coordinates specialized agents to perform complex materials property calculations from natural language prompts.
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[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] Quantifying Phase Contributions to Ion Transport in Organic–Inorganic Composite Electrolytes

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Mon, 08 Dec 2025 19:45:10 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c11634

[Proceedings of the National Academy of Sciences: Proceedings of the National Academy of Sciences: Table of Contents] Chatbot Voting Advice Applications inform but seldom sway young unaligned voters

Mon, 08 Dec 2025 08:00:00 GMT

Proceedings of the National Academy of Sciences, Volume 122, Issue 50, December 2025.
SignificanceMany people struggle to identify where political parties stand on the issues that matter most to them. This study introduces a Voting Advice Application (VAA) Bot powered by generative AI, that provides information about party positions using ...

[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.

[APL Machine Learning Current Issue] Multi-resolution physics-aware recurrent convolutional neural network for complex flows

APL Machine Learning Current Issue — Thu, 04 Dec 2025 00:00:00 GMT

We present MRPARCv2, Multi-resolution Physics-Aware Recurrent Convolutional Neural Network, designed to model complex flows by embedding the structure of advection–diffusion–reaction equations and leveraging a multi-resolution architecture. MRPARCv2 introduces hierarchical discretization and cross-resolution feature communication to improve the accuracy and efficiency of flow simulations. We evaluate the model on a challenging 2D turbulent radiative layer dataset from The Well multi-physics benchmark repository and demonstrate significant improvements when compared to the single resolution baseline model, in both Variance Scaled Root Mean Squared Error and physics-driven metrics, including turbulent kinetic energy spectra and mass–temperature distributions. Despite having 30% fewer trainable parameters, MRPARCv2 outperforms its predecessor by up to 50% in roll-out prediction error and 86% in spectral error. A preliminary study on uncertainty quantification was performed, and we also analyzed the model’s performance under different levels of abstractions of the flow, specifically on sampling subsets of field variables. We find that the absence of physical constraints on the equation of state (EOS) in the network architecture leads to degraded accuracy. A variable substitution experiment confirms that this issue persists regardless of which physical quantity is predicted directly. Our findings highlight the advantages of multi-resolution inductive bias for capturing multi-scale flow dynamics and suggest the need for future PIML models to embed EOS knowledge to enhance physical fidelity.

[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.

[iScience] Meteorological and Socioeconomic Impacts on China Ozone Past and Future Analysis

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

Chen et al. quantify seasonal ozone dynamics across China using machine learning and climate projections, revealing northern warm‑season O3 peaks driven by temperature, humidity, and mid‑tropospheric winds, and regionally divergent future trajectories under SSPs, highlighting the need for location‑specific, climate‑aware ozone control.

[Applied Physics Reviews Current Issue] Gate-tunable dual-mode BiOI photodetector for precise object identification

Applied Physics Reviews Current Issue — Thu, 04 Dec 2025 00:00:00 GMT

The controllable growth of large-sized and high-quality semiconductor single crystals is an important guarantee for the realization of high-performance electronic and optoelectronic devices. Herein, we synthesized layered BiOI transparent single crystals through a tellurium-assisted chemical vapor transport strategy. Systematic investigation reveals that tellurium acts as a critical transport agent, directly modulating the crystallization dynamics and enabling the growth of high-quality 1-cm single crystals with precise size control. The layered BiOI crystals demonstrate excellent broadband (254–940 nm) photoresponse performance, achieving a remarkable responsivity of 123.7 A·W⁻¹ and specific detectivity of 7.2 × 10¹³ Jones. Notably, the implementation of gate voltage regulation allows dynamic control of carrier transport mechanisms, achieving efficient regulation of the photoresponse of the device. This unique gate-tunable characteristic enables dual-mode operation in image recognition systems, simultaneously supporting both high-sensitivity detection and programmable contrast enhancement. The combination of scalable crystal growth and multifunctional optoelectronic properties positions BiOI as a promising candidate for next-generation intelligent photodetection technologies.

[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: Small: Table of Contents] Reagentless Real‐Time ATP Monitoring with New DNA Aptamers

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.

[RSC - Digital Discovery latest articles] Evaluating the transfer learning from metals to oxides with GAME-Net-Ox

RSC - Digital Discovery latest articles — Wed, 03 Dec 2025 00:00:00 GMT

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00331H, Paper

Open Access

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

Thomas Van Hout, Oliver Loveday, Jordi Morales-Vidal, Santiago Morandi, Núria López
GAME-Net-Ox, an extension of the GAME-Net graph neural network, enables fast prediction of adsorption energies for molecules with key organic functional groups on conductive and semiconductive rutile metal oxides and metal surfaces.
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[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.

[iScience] ABCA1 acts as a protective modulator in amyotrophic lateral sclerosis

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

Amyotrophic lateral sclerosis (ALS) is a progressive motor neuron disease lacking reliable biomarkers and effective therapeutic targets. We performed an integrative multiscale analysis combining global epidemiology, whole-blood transcriptomics, machine learning, and Mendelian randomization (MR). We developed a nine-gene diagnostic signature (AUC = 0.75 in external validation) and identified ATP-binding cassette transporter A1 (ABCA1) as a central feature. MR analyses supported a protective causal relationship between increased ABCA1 expression and reduced ALS risk (OR = 0.93, p = 0.02).

[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.

[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

[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 ...

[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

This study developed a deep learning-based method for high resolution bathymetry prediction in the Philippine Sea, aiming to improve the accuracy of seafloor depth estimation using multi-source marine geodetic data. The method integrates geographic coordinates with auxiliary features such as bathymetry, sea-land marks, seafloor slope and orientation, gravity anomaly, vertical gravity gradient, mean dynamic topography, deflection of the vertical, mean sea surface and sedimentary thickness. These inputs were extracted from an 8×8 arcminute region around each training point and the model was trained to predict depth residuals.

[Wiley: Small Methods: Table of Contents] Water‐Dispersible Metal Oxide Nanoparticles Synthesized Via Hydrogen‐Bond‐Mediated Aqueous Solution: Gd2O3 for High‐Performance T1 Magnetic Resonance Imaging Contrast Agent

Wiley: Small Methods: Table of Contents — Thu, 27 Nov 2025 07:52:59 GMT

Small Methods, EarlyView.

[iScience] Interpretable Machine Learning for Urothelial Cells Classification and Risk Scoring in Urine Cytology

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

Urine cytology is widely used for detecting urothelial carcinoma (UC), though its performance is constrained by limited sensitivity and substantial inter-observer variability. An interpretable machine learning framework was developed to classify urothelial cells and to estimate slide-level risk of high-grade urothelial carcinoma. 10,230 expert-annotated urothelial cells were used to extract 20 quantitiative feature representing cytomorphologic criteric defined by the Paris System. Ordinal logistic regression and random forest models were trained and validated, achiving over 90% accuracy for classifying cells into Normal, Atypical, or Suspicious categories.

[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 - Digital Discovery latest articles] Toward accelerating rare-earth metal extraction using equivariant neural networks

RSC - Digital Discovery latest articles — Wed, 26 Nov 2025 00:00:00 GMT

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00286A, Paper

Open Access

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

Ankur K. Gupta, Caitlin V. Hetherington, Wibe A. de Jong
A high-throughput workflow leveraging equivariant GNNs and a diverse dataset of rare-earth complexes to predict binding affinities and accelerate critical metal separation.
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[RSC - Digital Discovery latest articles] Automated synthesis and fragment descriptor-based machine learning for retention time prediction in supercritical fluid chromatography

RSC - Digital Discovery latest articles — Wed, 26 Nov 2025 00:00:00 GMT

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00437C, Paper

Open Access

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

Sitanan Sartyoungkul, Balasubramaniyan Sakthivel, Pavel Sidorov, Yuuya Nagata
Integration of automated synthesis and fragment descriptor-based machine learning enables accurate prediction of SFC retention times and accelerates column characterization.
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The content of this RSS Feed (c) The Royal Society of Chemistry

[RSC - Digital Discovery latest articles] Mol2Raman: a graph neural network model for predicting Raman spectra from SMILES representations

RSC - Digital Discovery latest articles — Tue, 25 Nov 2025 00:00:00 GMT

Digital Discovery, 2025, Advance Article
DOI: 10.1039/D5DD00210A, Paper

Open Access

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

Salvatore Sorrentino, Alessandro Gussoni, Francesco Calcagno, Gioele Pasotti, Davide Avagliano, Ivan Rivalta, Marco Garavelli, Dario Polli
Mol2Raman is a graph neural network that predicts Raman spectra from molecular SMILES. Trained on >31k DFT-calculated spectra, it localizes peaks within 15 cm⁻¹ with 64% accuracy, outperforming current SOTA deep learning algorithms on Raman spectra.
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The content of this RSS Feed (c) The Royal Society of Chemistry

[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, Advance Article
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.
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[Cell Reports Physical Science] Repurposing clinical iron oxide agents for mild hyperthermia-assisted cancer therapy

Cell Reports Physical Science — Tue, 25 Nov 2025 00:00:00 GMT

Zha et al. have developed a photothermal- or ultrasound-assisted therapeutic platform that endows a clinically approved iron oxide reagent with profound anticancer efficacy. The platform can induce immunogenic cell death in multiple xenograft models, reverse immunosuppressive tumor microenvironments, and trigger robust anticancer immune responses with enhanced immunological memory.

[JACS Au: Latest Articles (ACS Publications)] [ASAP] Constant-Potential MD with Neural Network Potentials Reveals Cation Effects on CO2 Reduction at Au-Water Interfaces

JACS Au: Latest Articles (ACS Publications) — Mon, 24 Nov 2025 12:22:43 GMT

JACS Au

DOI: 10.1021/jacsau.5c01198

[Proceedings of the National Academy of Sciences: Physical Sciences] Modeling feasible locomotion of nanobots for cancer detection and treatment

Proceedings of the National Academy of Sciences: Physical Sciences — Mon, 24 Nov 2025 08:00:00 GMT

Proceedings of the National Academy of Sciences, Volume 122, Issue 48, December 2025.
SignificanceWe present a mathematical model of nanorobots moving in a colloidal environment within the human body to locate a single, targeted cancer site and deliver localized treatment. The capabilities and behavior of individual agents are inspired by ...

[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.

[RSC - Chem. Sci. latest articles] Development of a glutamine-responsive MRI contrast agent

RSC - Chem. Sci. latest articles — Thu, 20 Nov 2025 00:00:00 GMT

Chem. Sci., 2026, Advance Article
DOI: 10.1039/D5SC05987A, Edge Article

Open Access

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

Charles A. Wilson, Austin T. Bruchs, Saman Fatima, David G. Boggs, Jennifer Bridwell-Rabb, Lisa Olshansky
This report details the development of a conformationally switchable artificial metalloprotein (swArM) that provides differential MRI contrast signal in the presence and absence of the key biomarker glutamine.
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[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.

[RSC - Digital Discovery latest articles] Democratizing machine learning in chemistry with community-engaged test sets

RSC - Digital Discovery latest articles — Wed, 19 Nov 2025 00:00:00 GMT

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00424A, Paper

Open Access

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

Jason L. Wu, David M. Friday, Changhyun Hwang, Seungjoo Yi, Tiara C. Torres-Flores, Martin D. Burke, Ying Diao, Charles M. Schroeder, Nicholas E. Jackson
Machine learning (ML) is increasingly central to chemical discovery, yet most efforts remain confined to distributed and isolated research groups, limiting external validation and community engagement.
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The content of this RSS Feed (c) The Royal Society of Chemistry

[iScience] An Explainable Machine Learning Model Predicts 30-Day Readmission after Vertebral Augmentation

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

Osteoporotic vertebral compression fracture (OVCF) patients face high 30-day readmission risks after vertebral augmentation procedures (VAPs). Using electronic health records (EHRs) of 3,947 OVCF patients who underwent VAPs (2019–2024), we developed an interpretable machine learning model to identify readmission predictors. Eight algorithms were evaluated via 10-fold cross-validation, and XGBoost showed the best performance (area under the curve [AUC], sensitivity, specificity, F1 score, and decision curve analysis).

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

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

SmartMat, Volume 6, Issue 6, December 2025.

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

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

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

Open Access

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

Bing Ma, Na Qin, Qianqian Yan, Wei Zhou, Sheng Zhang, Xiao Wang, Lipiao Bao, Xing Lu
AI and machine learning combined with multiscale simulations accelerate framework materials design. This review summarizes AI-assisted strategies for synthesis prediction, condition optimization, and inverse functional design.
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[RSC - Digital Discovery latest articles] Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost

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

Digital Discovery, 2026, Advance Article
DOI: 10.1039/D5DD00294J, Paper

Open Access

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

Ilgar Baghishov, Jan Janssen, Graeme Henkelman, Danny Perez
Simultaneously tuning DFT convergence, data selection, and energy-force weights reveals a Pareto front of optimal MLIPs. This minimizes costs by tailoring the MLIP to the specific accuracy requirements of the target application.
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[Applied Physics Reviews Current Issue] Synergistic integration of metasurfaces and quantum photonics: Pathways to next-generation technologies

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

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

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

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

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

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

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

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

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

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

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

[Journal of the American Chemical Society: Latest Articles (ACS Publications)] [ASAP] An O2-Independent Copper(II) Phototherapeutic Agent for Photoactivating H2O2 to Enhance Antitumor Immunotherapy

Journal of the American Chemical Society: Latest Articles (ACS Publications) — Sun, 16 Nov 2025 13:54:08 GMT

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c14960

[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.

[Proceedings of the National Academy of Sciences: Physical Sciences] How public involvement can improve the science of AI

Proceedings of the National Academy of Sciences: Physical Sciences — Fri, 14 Nov 2025 08:00:00 GMT

Proceedings of the National Academy of Sciences, Volume 122, Issue 48, December 2025.
As AI systems from decision-making algorithms to generative AI are deployed more widely, computer scientists and social scientists alike are being called on to provide trustworthy quantitative evaluations of AI safety and reliability. These calls have ...

[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.

[RSC - Digital Discovery latest articles] Design of simple-structured conjugated polymers for organic solar cells by machine learning-assisted structural modification and experimental validation

RSC - Digital Discovery latest articles — Tue, 11 Nov 2025 00:00:00 GMT

Digital Discovery, 2025, 4,3774-3781
DOI: 10.1039/D5DD00418G, Paper

Open Access

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

Shogo Tadokoro, Ryosuke Kamimura, Fumitaka Ishiwari, Akinori Saeki
We explore simple-structured p-type polymers for organic photovoltaics using machine learning (ML) based on the primitive use of the molecular size and synthetic accessibility. Experimental validation shows good agreement with the ML prediction.
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[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

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Near-Infrared-II Aggregation-Induced Emission Photosensitizers with Mitochondrial Respiration Perturbation Activity Amplifies Ferroptosis, Necroptosis, and Apoptosis for Cancer Immunotherapy

Chinese Chemical Society: CCS Chemistry: Table of Contents — Tue, 04 Nov 2025 04:36:34 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202505952

Activation of programmed cell death (PCD) networks in cancer cells represents an emerging paradigm in precision oncology. However, conventional antitumor agents remain constrained by insufficient cellular targeting and limited capacity to engage multiple ...

[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.

[RSC - Digital Discovery latest articles] Machine learning of polyurethane prepolymer viscosity: a comparison of chemical and physicochemical approaches

RSC - Digital Discovery latest articles — Tue, 04 Nov 2025 00:00:00 GMT

Digital Discovery, 2025, 4,3652-3661
DOI: 10.1039/D5DD00287G, Paper

Open Access

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

Joseph A. Pugar, Calvin Gang, Isabelle Millan, Karl Haider, Newell R. Washburn
A dual machine learning framework predicts polyurethane prepolymer viscosity using either chemical composition or physicochemical descriptors, balancing accuracy for known chemistries with generalizability to new formulations.
The content of this RSS Feed (c) The Royal Society of Chemistry

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Biodegradable Cesium Nanosalts Activating Antitumor Immunity via Inducing Cellular Pyroptosis and Interfering with Metabolism

Chinese Chemical Society: CCS Chemistry: Table of Contents — Mon, 03 Nov 2025 01:13:27 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202506187

Nanosalts deserve to be taken seriously as an important kind of superior antitumor agent. However, nanosalts for tumor therapy are still a little-touched “Blue Ocean.” Broadening the material library of nanosalts is particularly important to extend their ...

[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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[RSC - Digital Discovery latest articles] FFLAME: a fragment-to-framework learning approach for MOF potentials

RSC - Digital Discovery latest articles — Thu, 30 Oct 2025 00:00:00 GMT

Digital Discovery, 2025, 4,3466-3477
DOI: 10.1039/D5DD00321K, Paper

Open Access

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

Xiaoqi Zhang, Yutao Li, Xin Jin, Berend Smit
FFLAME, a fragment-centric strategy for training transferable MOF machine learning potentials, learns from building blocks, lowers data needs, and achieves near-target accuracy with minimal fine-tuning even for unseen MOFs.
The content of this RSS Feed (c) The Royal Society of Chemistry

[RSC - Digital Discovery latest articles] Machine learning generalised DFT+U projectors in a numerical atom-centred orbital framework

RSC - Digital Discovery latest articles — Thu, 30 Oct 2025 00:00:00 GMT

Digital Discovery, 2025, 4,3701-3727
DOI: 10.1039/D5DD00292C, Paper

Open Access

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

Amit Chaudhari, Kushagra Agrawal, Andrew J. Logsdail
We present machine learning-based workflows using symbolic regression and support vector machines to simultaneously optimise Hubbard U values and projectors, enabling accurate and efficient simulations of defects and polarons in complex metal oxides.
The content of this RSS Feed (c) The Royal Society of Chemistry

[RSC - Digital Discovery latest articles] Leveraging large language models for enzymatic reaction prediction and characterization

RSC - Digital Discovery latest articles — Thu, 30 Oct 2025 00:00:00 GMT

Digital Discovery, 2025, 4,3588-3609
DOI: 10.1039/D5DD00187K, Paper

Open Access

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

Lorenzo Di Fruscia, Jana M. Weber
We present a systematic study of multitask Large Language Models, fine-tuned to predict enzyme commission numbers and to perform forward and retrosynthesis of enzymatic reactions.
The content of this RSS Feed (c) The Royal Society of Chemistry

[RSC - Digital Discovery latest articles] Cross-laboratory validation of machine learning models for copper nanocluster synthesis using cloud-based automated platforms

RSC - Digital Discovery latest articles — Thu, 30 Oct 2025 00:00:00 GMT

Digital Discovery, 2025, 4,3683-3692
DOI: 10.1039/D5DD00335K, Paper

Open Access

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

Ricardo Montoya-Gonzalez, Rosa de Guadalupe González-Huerta, Martha Leticia Hernández-Pichardo, Subha R. Das
Cloud laboratory robotic synthesis of copper nanoclusters combined with ML enables predictive models from just 40 experiments
The content of this RSS Feed (c) The Royal Society of Chemistry

[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.

[RSC - Digital Discovery latest articles] Machine learning anomaly detection of automated HPLC experiments in the cloud laboratory

RSC - Digital Discovery latest articles — Wed, 29 Oct 2025 00:00:00 GMT

Digital Discovery, 2025, 4,3445-3454
DOI: 10.1039/D5DD00253B, Paper

Open Access

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

Filipp Gusev, Benjamin C. Kline, Ryan Quinn, Anqin Xu, Ben Smith, Brian Frezza, Olexandr Isayev
Autonomous experiments are vulnerable to unforeseen adverse events. We developed a transferable ML framework that flags affected HPLC runs in real time and provides expert-level quality control without human oversight.
The content of this RSS Feed (c) The Royal Society of Chemistry

[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.

[RSC - Digital Discovery latest articles] An improved machine learning strategy using structural features to predict the glass transition temperature of oxide glasses

RSC - Digital Discovery latest articles — Thu, 23 Oct 2025 23:00:00 GMT

Digital Discovery, 2025, 4,3764-3773
DOI: 10.1039/D5DD00326A, Paper

Open Access

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

Satwinder Singh Danewalia, Kulvir Singh
We present a physics-informed machine learning approach to predict the glass transition temperature (T_g) of sodium borosilicate glasses.
The content of this RSS Feed (c) The Royal Society of Chemistry

[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.

[RSC - Digital Discovery latest articles] GoFlow: efficient transition state geometry prediction with flow matching and E(3)-equivariant neural networks

RSC - Digital Discovery latest articles — Mon, 20 Oct 2025 23:00:00 GMT

Digital Discovery, 2025, 4,3492-3501
DOI: 10.1039/D5DD00283D, Paper

Open Access

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

Leonard Galustian, Konstantin Mark, Johannes Karwounopoulos, Maximilian P.-P. Kovar, Esther Heid
We introduce GoFlow, a package to generate 3D transition state geometries using flow matching.
The content of this RSS Feed (c) The Royal Society of Chemistry

[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.

[RSC - Digital Discovery latest articles] Do Llamas understand the periodic table?

RSC - Digital Discovery latest articles — Mon, 13 Oct 2025 23:00:00 GMT

Digital Discovery, 2025, 4,3455-3465
DOI: 10.1039/D5DD00374A, Paper

Open Access

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

Ge Lei, Samuel J. Cooper
We observe a 3D spiral structure in the hidden states of LLMs that aligns with the conceptual structure of the periodic table.
The content of this RSS Feed (c) The Royal Society of Chemistry

[Applied Physics Reviews Current Issue] Carbon-based memristors for neuromorphic computing

Applied Physics Reviews Current Issue — Mon, 13 Oct 2025 00:00:00 GMT

Driven by the rapid advancement of the Internet of Things and artificial intelligence, computational power demands have experienced an exponential surge, thereby accentuating the inherent limitations of the conventional von Neumann architecture. Neuromorphic computing memristors are emerging as a promising solution to overcome this bottleneck. Among various material-based memristors, carbon-based memristors (CBMs) are particularly attractive due to their biocompatibility, flexibility, and stability, which make them well suited for next-generation neuromorphic applications. This review summarizes the recent advancements in CBMs and proposes potential application scenarios in neuromorphic computing. Representative CBMs and preparation methods of carbon-based materials in different dimensions (0D, 1D, 2D, and 3D) are presented, followed by structural, storage, and synaptic plasticity testing and switching mechanisms. The neural network architecture built by CBMs is summarized for image processing, wearable electronics, and three-dimensional integration. Finally, the future challenges and application prospects of CBMs are reviewed and summarized.

[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 ...

[RSC - Digital Discovery latest articles] Constructing and explaining machine learning models for the exploration and design of boron-based Lewis acids

RSC - Digital Discovery latest articles — Sun, 05 Oct 2025 23:00:00 GMT

Digital Discovery, 2025, 4,3623-3634
DOI: 10.1039/D5DD00212E, Paper

Open Access

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

Juliette Fenogli, Laurence Grimaud, Rodolphe Vuilleumier
Bridging ML and chemical intuition, interpretable models predict Lewis acidity with high accuracy and reveal design rules for tailoring boron-based Lewis acids.
The content of this RSS Feed (c) The Royal Society of Chemistry

[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.

[RSC - Digital Discovery latest articles] ReactPyR: a python workflow for ReactIR allows for quantification of the stability of sensitive compounds in air

RSC - Digital Discovery latest articles — Thu, 02 Oct 2025 23:00:00 GMT

Digital Discovery, 2025, 4,3533-3539
DOI: 10.1039/D5DD00305A, Paper

Open Access

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

Nicola L. Bell, Emanuele Berardi, Marina Gladkikh, Richard Drummond Turnbull, Freya Turton
ReactPyR enables automated ReactIR workflows to quantify air-sensitivity in organometallic reagents, delivering reproducible kinetic insights and guiding stabilisation strategies for safer, more efficient handling of highly reactive species.
The content of this RSS Feed (c) The Royal Society of Chemistry

[Applied Physics Reviews Current Issue] Extracellular vesicles as the drug delivery vehicle for gene-based therapy

Applied Physics Reviews Current Issue — Wed, 01 Oct 2025 00:00:00 GMT

Extracellular vesicles (EVs) are membrane-bound nanoparticles naturally secreted by cells, playing a vital role in intercellular communication and holding significant promise as therapeutic agents. These natural carriers deliver various molecules into cells, including proteins and nucleic acids. There are numerous methods to load and modify EVs, encompassing physical, chemical, and biological approaches. EVs demonstrate the capacity to target specific cells within organs, even requiring blood–tissue transition. The protein corona significantly influences EV availability and cargo delivery, with biomolecules residing both within and conjugated to the EV membrane. Furthermore, embedding EVs within biomaterials such as hydrogels, scaffolds, and nanofibers can enhance their stability, targeting specificity, and therapeutic potential. By addressing cargo loading and cell/tissue-specific targeting, EVs offer a novel therapeutic strategy for various diseases, including cancer, autoimmune disorders, and neurodegenerative diseases. Furthermore, EVs show promise as vaccination tools, delivering messenger RNA and proteins of various pathogens. Advances in EV biology and engineering would provide improved strategies for vesicle targeting, enhanced cargo loading, and safe and effective delivery. The convergence of technological advancements, interdisciplinary collaboration, and an enhanced understanding of EVs promises to revolutionize therapeutic approaches to a wide range of diseases, establishing EV-based treatments as a cornerstone of future medicine.

[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.

[AI for Science - latest papers] 4D-MISR: a unified model for low-dose super-resolution imaging via feature fusion

AI for Science - latest papers — Wed, 17 Sep 2025 23:00:00 GMT

While an electron microscope offers crucial atomic-resolution insights into structure–property relationships, radiation damage severely limits its use on beam-sensitive materials like proteins and 2D materials. To overcome this challenge, we push beyond the electron dose limits of the conventional method by adapting principles from multi-image super-resolution that had been widely used in remote sensing. Our method fuses multiple low-resolution, sub-pixel-shifted views and enhances this reconstruction with a convolutional neural network that integrates features from synthetic, multi-angle observations. We developed a dual-path, attention-guided network for four-dimensional scanning transmission electron microscopy (4D-STEM) that achieves atomic-scale super-resolution from ultra-low-dose data. This provides robust atomic-scale visualization across amorphous, semi-crystalline, and crystalline beam-sensitive specimens. Systematic evaluations on representative materials demonstrate the comparable spatial resolution to conventional ptychography under ultra low-dose conditions. Our work one-step expands the capabilities of 4D-STEM, offering a new and generalizable method for the structural analysis of any radiation-vulnerable material.

[Recent Articles in PRX Energy] Thermodynamic Modeling of Complex Solid Solutions in the $\mathrm{Lu}$-$\mathrm{H}$-$\mathrm{N}$ System via Graph Neural Network Accelerated Monte Carlo Simulations

Recent Articles in PRX Energy — Tue, 02 Sep 2025 10:00:00 GMT

Author(s): Pin-Wen Guan, Catalin D. Spataru, Vitalie Stavila, Reese Jones, Peter A. Sharma, and Matthew D. Witman

A thermodynamic modeling framework captures interstitial lattice disorder in complex metal hydrides, yielding pressure- and temperature-dependent phase diagrams that align with experiments and show how nitrogen doping can lower dehydrogenation temperatures for optimized hydrogen-storage alloys.

[PRX Energy 4, 033013] Published Tue Sep 02, 2025

[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

[Wiley: Advanced Intelligent Discovery: Table of Contents] Application of Neural Networks for Advanced Ir Spectroscopy Characterization of Ceria Catalysts Surfaces

Wiley: Advanced Intelligent Discovery: Table of Contents — Mon, 25 Aug 2025 07:01:05 GMT

Advanced Intelligent Discovery, EarlyView.

[Chem] Photocatalytic hydrogenation of alkenes using ammonia-borane

Chem — Mon, 25 Aug 2025 00:00:00 GMT

Hydrogenation of olefins is a key reaction in chemistry; however, it typically requires the use of flammable hydrogen gas with transition metal catalysis. This work presents a novel approach employing ammonia-borane, a stable and non-toxic reagent, as a hydrogen surrogate material under photocatalytic conditions, which efficiently circumvents the use of hydrogen and metal catalysis.

[Chem] Stereoselective C(sp3)-Si/Ge bond formation via nickel-catalyzed decarboxylative couplings

Chem — Thu, 21 Aug 2025 00:00:00 GMT

A long-standing gap in the catalytic synthesis of Si- and Ge-glycosides—particularly Ge analogs—has been filled by a stereoselective Ni-catalyzed decarboxylative coupling. This strategy enables selective C(sp³)–Si/Ge bond formation from redox-active esters and (silyl/germyl) zinc reagents, operating through a distinct Ni(0)/Ni(I)/Ni(II) cycle involving SET-mediated N–O bond cleavage. Ge- and Si-glycosides demonstrated promising bioactivity for the first time. These findings expand the synthetic repertoire for bioactive glycomimetics and provide new mechanistic insights into NHPI ester activation in cross-coupling chemistry.

[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.

[Chem] Organosilicon precursors for efficient aromatic copper-mediated radiocyanation

Chem — Wed, 20 Aug 2025 00:00:00 GMT

We present a copper-mediated labeling of aryl silanes with carbon-11, a radioactive isotope used in positron emission tomography imaging. The approach enables late-stage and efficient tagging of organic molecules, helping to accelerate the development of new imaging agents for biomedical imaging.

[Chem] Manganese low-energy photocatalysis for remodeling nitrogenation of alkenes

Chem — Tue, 19 Aug 2025 00:00:00 GMT

A manganese low-energy photoredox catalytic platform via the in situ assembly of a manganese(II) salt, a bidentate N ligand, a nucleophilic azide reagent, and an alcohol is established. This catalytic platform enables the oxidative remodeling nitrogenation of alkenes, efficiently synthesizing ketonitriles, ketones, or nitriles with excellent functional group tolerance. Additionally, the feasibility for late-stage functionalization of drug molecule derivatives and streamlined synthesis of anabasine demonstrates the potential applications of this protocol in synthetic organic chemistry and biomedicine.

[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] CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Wiley: Advanced Intelligent Discovery: Table of Contents — Fri, 25 Jul 2025 08:24:33 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

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Metalloligand Enabling Cobalt-Catalyzed anti-Markovnikov Hydrosilylation of Alkynes with Tertiary Silanes

Chinese Chemical Society: CCS Chemistry: Table of Contents — Tue, 15 Jul 2025 04:05:27 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202505983

Metal-catalyzed hydrosilylation of alkynes with hydrosilanes is a useful method for the preparation of vinylsilanes that are useful reagents in organic synthesis and the silicone industry. 3d metal catalysts affecting the hydrosilylation reactions of ...

[Wiley: Advanced Intelligent Discovery: Table of Contents] Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

Wiley: Advanced Intelligent Discovery: Table of Contents — Mon, 14 Jul 2025 15:08:07 GMT

Advanced Intelligent Discovery, EarlyView.

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Inorganic Iodide Catalyzed Alkylation of Amines with Primary Alcohols

Chinese Chemical Society: CCS Chemistry: Table of Contents — Fri, 11 Jul 2025 03:57:49 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202505864

Alkylation of amines with readily accessible reagents is one of the most efficient strategies for synthesizing substituted amines. Primary alcohols, being widely abundant, are excellent electrophiles that can undergo dehydration to react with various ...

[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.

[Wiley: Advanced Intelligent Discovery: Table of Contents] A Machine Learning Perspective on the Brønsted–Evans–Polanyi Relation in Water‐Gas Shift Catalysis on MXenes

Wiley: Advanced Intelligent Discovery: Table of Contents — Wed, 18 Jun 2025 08:09:26 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] 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 ...

[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] Radical Amidoalkynylation of Electron-Rich Alkenes with Bifunctional Alkynylsulfonamide Reagents

Chinese Chemical Society: CCS Chemistry: Table of Contents — Mon, 09 Jun 2025 02:05:34 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202505686

Radical amidoalkynylations of olefins offer a powerful platform for the rapid construction of both C–N and C–O bonds, generating vicinal aminoalkynes which are frequently found in biologically active molecules. Herein, we developed a practical two-...

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Development of Nanomolar Affinity Miniprotein Inhibitors Targeting α-Synuclein Aggregation as Promising Therapeutic Agents for Parkinson’s Disease

Chinese Chemical Society: CCS Chemistry: Table of Contents — Tue, 03 Jun 2025 05:02:30 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202505587

Parkinson’s disease (PD) is a debilitating neurodegenerative disorder characterized by the accumulation of α-synuclein (α-syn) aggregates in the brain. Developing effective therapies targeting α-syn has been challenging due to its intrinsically disordered ...

[Chinese Chemical Society: CCS Chemistry: Table of Contents] Harnessing Photoredox Cascade to Enhance Photodynamic Oncotherapy by Nanoformulated Macrocyclic Photosensitizer

Chinese Chemical Society: CCS Chemistry: Table of Contents — Tue, 03 Jun 2025 04:56:28 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202505567

Optically active nanoagents hold a dominant position in advanced phototheranostics, but there remain challenges in structural optimization and performance maximization for biomedical applications. Herein, a π-extended triphenylamine-containing ...

[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] Enantioconvergent Negishi Cross-Coupling of Racemic sec-Alkylzinc Reagent with Aryl Halides Enabled by Bulky N-Heterocyclic Carbene-Pd Catalyst

Chinese Chemical Society: CCS Chemistry: Table of Contents — Mon, 19 May 2025 04:21:11 GMT

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202505591

Transition-metal-catalyzed cross-coupling reactions have revolutionized synthetic approaches for forging C–C bonds. However, catalytic enantioconvergent couplings of racemic secondary organometallics with aryl electrophiles remain a significant challenge. ...

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Author(s): Peter Thalmeier

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

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

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

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

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

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

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

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

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

Author(s): Zhongqing Wu

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Author(s): Nitish Nair and Michael S. Strano

We have performed a parametric study of self-propagating chain reactions along a one-dimensional bead-spring array. The coupling between beads is modeled using harmonic and anharmonic Fermi-Pasta-Ulam (FPU)-$β$ and ${φ}^{4}$ potentials. The parameters that define the system are the activation energy…

[Phys. Rev. B 80, 174301] Published Fri Nov 13, 2009

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

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

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

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

ACS Materials Letters

DOI: 10.1021/acsmaterialslett.5c01047

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

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

Journal of the American Chemical Society

DOI: 10.1021/jacs.5c16369

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

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

Small Methods, EarlyView.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Open Access

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

[Wiley: Advanced Science: Table of Contents] Intelligent Sensing: The Emerging Integration of Machine Learning and Soft Sensors Based on Hydrogels and Ionogels

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

ACS Energy Letters

DOI: 10.1021/acsenergylett.5c03415

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

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

Small, EarlyView.

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

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

Advanced Materials, EarlyView.

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

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

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

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

[iScience] River plastic hotspot detection from space

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

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

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

ACS Nano

DOI: 10.1021/acsnano.5c15161

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

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

Journal of Chemical Theory and Computation

DOI: 10.1021/acs.jctc.5c01610

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

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

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03196

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

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

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03397

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

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

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c02968

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

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

The Journal of Physical Chemistry C

DOI: 10.1021/acs.jpcc.5c05232

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

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

ACS Nano

DOI: 10.1021/acsnano.5c16117

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

Fri, 26 Dec 2025 08:00:00 GMT

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

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

Advanced Science, EarlyView.

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

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

Carbon Energy, EarlyView.

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

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

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

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

ACS Energy Letters

DOI: 10.1021/acsenergylett.5c03429

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

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

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

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

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

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

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

Wed, 24 Dec 2025 08:00:00 GMT

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

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

Advanced Energy Materials, EarlyView.

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

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

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

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

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

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

Journal of Chemical Theory and Computation

DOI: 10.1021/acs.jctc.5c01712

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

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

The Journal of Physical Chemistry C

DOI: 10.1021/acs.jpcc.5c06757

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

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

The Journal of Physical Chemistry C

DOI: 10.1021/acs.jpcc.5c07031

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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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

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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

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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

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

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

ACS Nano

DOI: 10.1021/acsnano.5c14722

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

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

ACS Nano

DOI: 10.1021/acsnano.5c17718

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

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

Advanced Intelligent Discovery, EarlyView.

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

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

ACS Nano

DOI: 10.1021/acsnano.5c12987

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

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

The Journal of Physical Chemistry C

DOI: 10.1021/acs.jpcc.5c06140

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

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

Accounts of Chemical Research

DOI: 10.1021/acs.accounts.5c00667

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

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

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

[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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Yangxin Fan, Yinghui Wu, Roger H. French, Danny Perez, Michael G. Taylor, Ping Yang
Solubility quantifies the concentration of a molecule that can dissolve in a given solvent.
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[Wiley: Advanced Energy Materials: Table of Contents] Machine Learning Descriptors for Mapping Structure‐Property‐Performance Relationships of Perovskite Solar Cells

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

Advanced Energy Materials, EarlyView.

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

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

ACS Materials Letters

DOI: 10.1021/acsmaterialslett.5c01262

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

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

ACS Energy Letters

DOI: 10.1021/acsenergylett.5c02904

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

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

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

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

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

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[AI for Science - latest papers] Investigating CO adsorption on Cu(111) and Rh(111) surfaces using machine learning exchange-correlation functionals

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Open Access

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.

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.
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[RSC - Chem. Sci. latest articles] A solid composite electrolyte based on three-dimensional structured zeolite networks for high-performance solid-state lithium metal batteries

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

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

Open Access

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

[Proceedings of the National Academy of Sciences: Physical Sciences] Local equations describe unreasonably efficient stochastic algorithms in random K-SAT

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

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

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

ACS Materials Letters

DOI: 10.1021/acsmaterialslett.5c01502

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

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

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

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

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

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

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

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

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

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

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

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

Open Access

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[iScience] Physical Cognition in Altered Gravity: Link Between Sensorimotor and Cognitive Adaptability

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Cardiovascular medicine; Respiratory medicine; Machine learning

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202506542

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

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

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202506542

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

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

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

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

Advanced Intelligent Discovery, EarlyView.

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

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

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

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

Health informatics; disease; artificial intelligence applications

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

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

Earth sciences; oceanography; geodesy; machine learning

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

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

Health sciences; Cancer; Machine learning

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

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

Advanced Intelligent Discovery, EarlyView.

[RSC - Chem. Sci. latest articles] Data-driven approach to elucidate the correlation between photocatalytic activity and rate constants from excited states

RSC - Chem. Sci. latest articles — Tue, 25 Nov 2025 00:00:00 GMT

Chem. Sci., 2026, 17,176-186
DOI: 10.1039/D5SC06465A, Edge Article

Open Access

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Ryuga Kunisada, Manami Hayashi, Tabea Rohlfs, Taiki Nagano, Koki Sano, Naoto Inai, Naoki Noto, Takuya Ogaki, Yasunori Matsui, Hiroshi Ikeda, Olga García Mancheño, Takeshi Yanai, Susumu Saito
A data-driven framework integrating machine learning and quantum chemical calculations enables elucidation of how rate constants from excited states govern the photocatalytic activity of organic photosensitizers.
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[Wiley: Chinese Journal of Chemistry: Table of Contents] Locked Coplanar Conformation Boosts Rapid Electron/Ion Transport in Linear Polyimide Cathodes for Sodium‐Ion Storage

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

Chinese Journal of Chemistry, EarlyView.

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

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

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

Joule — Fri, 21 Nov 2025 00:00:00 GMT

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

Joule — Fri, 21 Nov 2025 00:00:00 GMT

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

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

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

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

Advanced Intelligent Discovery, EarlyView.

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

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

Orthopedics; Machine learning

[RSC - Chem. Sci. latest articles] The agentic age of predictive chemical kinetics

RSC - Chem. Sci. latest articles — Wed, 19 Nov 2025 00:00:00 GMT

Chem. Sci., 2026, 17,27-35
DOI: 10.1039/D5SC07692G, Perspective

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

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

SmartMat, Volume 6, Issue 6, December 2025.

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

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

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

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 (Auto-Filtered)

[cond-mat updates on arXiv.org] Atomic-scale visualization of d-wave altermagnetism

[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

[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

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

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

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

[cond-mat updates on arXiv.org] Novel fast Li-ion conductors for solid-state electrolytes from first-principles

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

[cond-mat updates on arXiv.org] Machine Learning H-theorem

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

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

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

[tandf: Materials Research Letters: Table of Contents] Physical-information machine learning for strength and ductility prediction of metastable β titanium alloys

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

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

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

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

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

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

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[RSC - Digital Discovery latest articles] A case study on hybrid machine learning and quantum-informed modelling for solubility prediction of drug compounds in organic solvents

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[iScience] Large language models for predicting one-year major adverse cardiovascular events in acute coronary syndrome

[iScience] Diverse Intracellular Trafficking of Insulin Analogs by Machine Learning-based Colocalization and Diffusion Analysis

[Applied Physics Letters Current Issue] Rigid-flexible free-standing multichannel carbon nanofiber-silicon composite anodes due to PS-induced channel ordering

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

[ScienceDirect Publication: Journal of Energy Storage] A hierarchical sandwich Li6.4Ga0.2La3Zr2O12/ZIF-8@SiO2/PVDF-HFP heterostructure with high ionic conductivity for dendrite-free solid-state lithium batteries

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

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

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

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

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

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

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

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

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

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

[cond-mat updates on arXiv.org] Common sublattice-pure van Hove singularities in the kagome superconductors $\textit{A}$V$_{3}$Sb$_{5}$ ($\textit{A}$ = K, Rb, Cs)

[cond-mat updates on arXiv.org] A Universal Model for the Resting Potential in Nanofluidic Systems

[cond-mat updates on arXiv.org] Anharmonic lattice dynamics study of phonon transport in layered and molecular-crystal indium iodides

[cond-mat updates on arXiv.org] Spin-correlation Driven Ferroelectric Quantum Criticality in a Perovskite Quantum Spin-liquid System, Ba3CuSb2O9

[cond-mat updates on arXiv.org] Score-based diffusion models for accurate crystal-structure inpainting and reconstruction of hydrogen positions

[cond-mat updates on arXiv.org] New RVE concept and FFT methods in micromechanics of composites subjected to body force with compact support

[cond-mat updates on arXiv.org] AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials

[cond-mat updates on arXiv.org] Quantum Extreme Reservoir Computing for Phase Classification of Polymer Alloy Microstructures

[cond-mat updates on arXiv.org] Projected branes as platforms for crystalline, superconducting, and higher-order topological phases

[cond-mat updates on arXiv.org] Efficient Identification of Critical Transitions via Flow Matching: A Scalable Generative Approach for Many-Body Systems

[cond-mat updates on arXiv.org] Graph atomic cluster expansion for foundational machine learning interatomic potentials

[cond-mat updates on arXiv.org] DeFecT-FF: Accelerated Modeling of Defects in Cd-Zn--Te-Se-S Compounds Combining High-Throughput DFT and Machine Learning Force Fields

[cond-mat updates on arXiv.org] Time-Temperature-Transformation (TTT) Diagrams to rationalize the nucleation and quenchability of metastable $\alpha$-Li$_3$PS$_4$

[cond-mat updates on arXiv.org] Linear magnetoresistance of two-dimensional massless Dirac fermions in the quantum limit

[cond-mat updates on arXiv.org] Insights Into Radiation Damage in YBa$_2$Cu$_3$O$_{7-\delta}$ From Machine-Learned Interatomic Potentials

[cond-mat updates on arXiv.org] Controllable diatomic molecular quantum thermodynamic machines

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

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

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