My Customized Papers

[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: 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 — 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.

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

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

Carbon Energy, EarlyView.

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

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

Advanced Science, EarlyView.

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

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

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

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

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

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

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

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

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

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

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

arXiv:2508.21012v5 Announce Type: cross @@ -15,7 +37,7 @@ Using small-amplitude oscillatory shear and steady torsional flow at 37 °C, we 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.

[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

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.

[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 — Mon, 05 Jan 2026 00:00:00 GMT

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

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

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

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

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

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

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

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

[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

[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

[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

[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.