My Customized Papers

[RSC - Digital Discovery latest articles] MOFReasoner: Think Like a Scientist-A Reasoning Large Language Model via Knowledge Distillation

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

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

Open Access

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

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

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

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

Publication date: 28 February 2026

Source: Journal of Energy Storage, Volume 148

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

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

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

Publication date: 15 January 2026

Source: Science Bulletin, Volume 71, Issue 1

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

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

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

Publication date: 1 March 2026

Source: Solid State Ionics, Volume 436

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

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

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

Publication date: 1 March 2026

Source: Solid State Ionics, Volume 436

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

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

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

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

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

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

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

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

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

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

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

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

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

Small Methods, EarlyView.

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

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2601.04308v1 Announce Type: new +Abstract: We consider the intrinsic fluctuation conductivity in metals with multiply sheeted Fermi surfaces approaching a superconducting critical point. Restricting our attention to extreme type-II multicomponent superconductors motivates focusing on the ultraclean limit. Using functional-integral techniques, we derive the Gaussian fluctuation action from which we obtain the gauge-invariant electromagnetic linear response kernel. This allows us to compute the optical conductivity tensor. We identify essential conditions required for a nonzero longitudinal conductivity at finite frequencies in a disorder-free and translationally invariant system. Specifically, this is neither related to impurity scattering nor electron-phonon interaction, but derives indirectly from the multicomponent character of the incipient superconducting order and the parent metallic state. Under these conditions, the enhancement of the DC conductivity due to fluctuations close to the critical point follows the same critical behaviour as in the diffusive limit.

[cond-mat updates on arXiv.org] Towards understanding the defect properties in the multivalent A-site Na$_{0.5}$Bi$_{0.5}$TiO$_3$-based perovskite ceramics

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2601.04725v1 Announce Type: new +Abstract: A defect model involving cation and anion vacancies and anti-site defects is proposed that accounts for the non-stoichiometry of multi-valent $A$-site Na$_{0.5}$Bi$_{0.5}$TiO$_3$ based perovskite oxides with $ABO_3$ composition. A series of samples with varying $A$-site non-stoichiometry and $A$:$B$ ratios were prepared to investigate their electrical conductivity. The oxygen partial pressure and temperature dependent conductivities where studied with direct current (dc) and alternating current (ac) techniques, enabling to separate between ionic and electronic conduction. The Na-excess samples, regardless of the $A$:$B$ ratio, exhibit dominant ionic conductivity and $p$-type electronic conduction, with the highest total conductivity reaching $4 \times 10^{-4}$ S/cm at 450$^\circ$C. In contrast, the Bi-excess samples display more insulating characteristics and $n$-type electronic conductivity, with conductivity values within the 10$^{-8}$ S/cm range at 450$^\circ$C. These conductivity results strongly support the proposed defect model, which offers a straightforward description of defect chemistry in NBT-based ceramics and serves as a valuable guide for optimizing sample processing to achieve tailored properties.

[cond-mat updates on arXiv.org] Scalable Dielectric Tensor Predictions for Inorganic Materials using Equivariant Graph Neural Networks

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2601.04755v1 Announce Type: new +Abstract: Accurate prediction of dielectric tensors is essential for accelerating the discovery of next-generation inorganic dielectric materials. Existing machine learning approaches, such as equivariant graph neural networks, typically rely on specially-designed network architectures to enforce O(3) equivariance. However, to preserve equivariance, these specially-designed models restrict the update of equivariant features during message passing to linear transformations or gated equivariant nonlinearities. The inability to implicitly characterize more complex nonlinear structures may reduce the predictive accuracy of the model. In this study, we introduce a frame-averaging-based approach to achieve equivariant dielectric tensor prediction. We propose GoeCTP, an O(3)-equivariant framework that predicts dielectric tensors without imposing any structural restrictions on the backbone network. We benchmark its performance against several state-of-the-art models and further employ it for large-scale virtual screening of thermodynamically stable materials from the Materials Project database. GoeCTP successfully identifies various promising candidates, such as Zr(InBr$_3$)$_2$ (band gap $E_g = 2.41$ eV, dielectric constant $\overline{\varepsilon} = 194.72$) and SeI$_2$ (anisotropy ratio $\alpha_r = 96.763$), demonstrating its accuracy and efficiency in accelerating the discovery of advanced inorganic dielectric materials.

[cond-mat updates on arXiv.org] Lateral Graphene-Metallene Interfaces at the Nanoscale

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2601.04838v1 Announce Type: new +Abstract: Metallenes are atomically thin, nonlayered two-dimensional materials. While they have appealing properties, their isotropic metallic bonding makes their stabilization difficult and presents considerable challenges to their synthesis and practical applications. However, their stabilization can still be achieved by suspending them in the pores of two-dimensional template materials, making the properties of lateral interfaces of metallenes scientifically relevant. Here, we combined density-functional theory and universal machine-learning interatomic potentials to study lateral interfaces between graphene and 45 metallenes with various profiles. We optimized the interfaces and analyzed their energies, electronic structures, and stabilities at room temperature, defect formations, and structural deformations. While broad trends were identified using machine-learning analysis of all interfaces, density-functional theory was the main tool for studying the microscopic properties of selected elements. We found that the interfaces are the most stable energetically and with respect to lattice mismatch, defect formation, and lateral strain when their profiles were geometrically smooth. The most stable interfaces are found for transition metals. In addition, we demonstrate how universal machine-learning interatomic potentials now offer the accuracy required for the modeling of graphene-metallene interfaces. By systematically expanding the understanding of metallenes' interface properties, we hope these results guide and accelerate their synthesis to enable future applications and benefit from metallenes' appealing properties.

[cond-mat updates on arXiv.org] Stable Machine Learning Potentials for Liquid Metals via Dataset Engineering

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2601.05003v1 Announce Type: new +Abstract: Liquid metals are central to energy-storage and nuclear technologies, yet quantitative knowledge of their thermophysical properties remains limited. While atomistic simulations offer a route to computing liquid properties directly from atomic motion, the most accurate approach, ab initio molecular dynamics (AIMD), is computationally costly and restricted to short time and length scales. Machine learning interatomic potentials (MLPs) offer AIMD accuracy at far lower cost, but their application to liquids is limited by training datasets that inadequately sample atomic configurations, leading to unphysical force predictions and unstable trajectories. Here we introduce a physically motivated dataset-engineering strategy that constructs liquidlike training data synthetically rather than relying on AIMD configurations. The method exploits the established icosahedral short-range order of metallic liquids, twelvefold, near-close-packed local coordination, and generates "synthetic-liquid" structures by systematic perturbation of crystalline references. MLPs trained on these datasets close the sampling gaps that lead to unphysical predictions, remain numerically stable across temperatures, and reproduce experimental liquid densities, diffusivities, and melting temperatures for multiple elemental metals. The framework links atomic-scale sampling to long-term MD stability and provides a practical route to predictive modeling of liquid-phase thermophysical behavior beyond the limits of direct AIMD.

[cond-mat updates on arXiv.org] Hierarchical Crystal Structure Prediction of Zeolitic Imidazolate Frameworks Using DFT and Machine-Learned Interatomic Potentials

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2601.05097v1 Announce Type: new +Abstract: Crystal structure prediction (CSP) is emerging as a powerful method for the computational design of metal-organic frameworks (MOFs). In this article we demonstrate the high-throughput exploration of the crystal energy landscape of zinc imidazolate (ZnIm2), a highly polymorphic member of the zeolitic imidazolate (ZIF) family, with at least 24 reported structural and topological forms, with new polymorphs still being regularly discovered. With the aid of custom-trained machine-learned interatomic potentials (MLIPs) we have performed a high-throughput sampling of over 3 million randomly-generated crystal packing arrangements and identified 9626 energy minima characterized by 1493 network topologies, including 864 topologies that have not been reported before. Comparisons with previously reported structures revealed 13 topological matches to the experimentally-observed structures of ZnIm2, demonstrating the power of the CSP method in sampling experimentally-relevant ZIF structures. Finally, through a combination of topological analysis, density and porosity considerations, we have identified a set of structures representing promising targets for future experimental screening. Finally, we demonstrate how CSP can be used to assist in the identification of the products of the mechanochemical synthesis.

[cond-mat updates on arXiv.org] Beyond the imbalance: site-resolved dynamics probing resonances in many-body localization

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2601.05177v1 Announce Type: new +Abstract: We explore the limitations of using imbalance dynamics as a diagnostic tool for many-body localization (MBL) and show that spatial averaging can mask important microscopic features. Focusing on the strongly disordered regime of the random-field XXZ chain, we use state-of-the-art numerical techniques (Krylov time evolution and full diagonalization) to demonstrate that site-resolved spin autocorrelators reveal a rich and complex dynamical behavior that is obscured by the imbalance observable. By analyzing the time evolution and infinite-time limits of these local probes, we reveal resonant structures and rare local instabilities within the MBL phase. These numerical findings are supported by an analytical, few-site toy model that captures the emergence of a multiple-peak structure in local magnetization histograms, which is a hallmark of local resonances. These few-body local effects provide a more detailed understanding of ergodicity-breaking dynamics, and also allow us to explain the finite-size effects of long-time imbalance, and its sensitivity to the initial conditions in quench protocols. Overall, our experimentally testable predictions highlight the necessity of a refined, site-resolved approach to fully understand the complexities of MBL and its connection to rare-region effects.

[cond-mat updates on arXiv.org] Chiral Graviton Modes in Fermionic Fractional Chern Insulators

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2601.05196v1 Announce Type: new +Abstract: Chiral graviton modes are hallmark collective excitations of Fractional Quantum Hall (FQH) liquids. However, their existence on the lattice, where continuum symmetries that protect them from decay are lost, is still an open and urgent question, especially considering the recent advances in the realization of Fractional Chern Insulators (FCI) in transition metal dichalcogenides and rhombohedral pentalayer graphene. Here we present a comprehensive theoretical and numerical study of graviton-modes in fermionic FCI, and thoroughly demonstrate their existence. We first derive a lattice stress tensor operator in the context of the fermionic Harper-Hofstadter(HH) model which captures the graviton in the flat band limit. Importantly, we discover that such lattice stress-tensor operators are deeply connected to lattice quadrupolar density correlators, readily generalizable to generic Chern bands. We then explicitly show the adiabatic connection between FQH and FCI chiral graviton modes by interpolating from a low flux HH model to a Checkerboard lattice model that hosts a topological flat band. In particular, using state-of-the-art matrix product state and exact diagonalization simulations, we provide strong evidence that chiral graviton modes are long-lived excitations in FCIs despite the lack of continuous symmetries and the scattering with a two-magnetoroton continuum. By means of a careful finite-size analysis, we show that the lattice generates a finite but small intrinsic decay rate for the graviton mode. We discuss the relevance of our results for the exploration of graviton modes in FCI phases realized in solid state settings, as well as cold atom experiments.

[cond-mat updates on arXiv.org] Exact Multimode Quantization of Superconducting Circuits via Boundary Admittance

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2601.04407v1 Announce Type: cross +Abstract: We show that the Schur complement of the nodal admittance matrix, which reduces a multiport electromagnetic environment to the driving-point admittance $Y_{\mathrm{in}}(s)$ at the Josephson junction, naturally leads to an eigenvalue-dependent boundary condition determining the dressed mode spectrum. This identification provides a four-step quantization procedure: (i) compute or measure $Y_{\mathrm{in}}(s)$, (ii) solve the boundary condition $sY_{\mathrm{in}}(s) + 1/L_J = 0$ for dressed frequencies, (iii) synthesize an equivalent passive network, (iv) quantize with the full cosine nonlinearity retained. Within passive lumped-element circuit theory, we prove that junction participation decays as, we prove that junction participation decays as $O(\omega_n^{-1})$ at high frequencies when the junction port has finite shunt capacitance, ensuring ultraviolet convergence of perturbative sums without imposed cutoffs. The standard circuit QED parameters, coupling strength $g$, anharmonicity $\alpha$, and dispersive shift $\chi$, emerge as controlled limits with explicit validity conditions.

[cond-mat updates on arXiv.org] Higher-Order Knowledge Representations for Agentic Scientific Reasoning

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2601.04878v1 Announce Type: cross +Abstract: Scientific inquiry requires systems-level reasoning that integrates heterogeneous experimental data, cross-domain knowledge, and mechanistic evidence into coherent explanations. While Large Language Models (LLMs) offer inferential capabilities, they often depend on retrieval-augmented contexts that lack structural depth. Traditional Knowledge Graphs (KGs) attempt to bridge this gap, yet their pairwise constraints fail to capture the irreducible higher-order interactions that govern emergent physical behavior. To address this, we introduce a methodology for constructing hypergraph-based knowledge representations that faithfully encode multi-entity relationships. Applied to a corpus of ~1,100 manuscripts on biocomposite scaffolds, our framework constructs a global hypergraph of 161,172 nodes and 320,201 hyperedges, revealing a scale-free topology (power law exponent ~1.23) organized around highly connected conceptual hubs. This representation prevents the combinatorial explosion typical of pairwise expansions and explicitly preserves the co-occurrence context of scientific formulations. We further demonstrate that equipping agentic systems with hypergraph traversal tools, specifically using node-intersection constraints, enables them to bridge semantically distant concepts. By exploiting these higher-order pathways, the system successfully generates grounded mechanistic hypotheses for novel composite materials, such as linking cerium oxide to PCL scaffolds via chitosan intermediates. This work establishes a "teacherless" agentic reasoning system where hypergraph topology acts as a verifiable guardrail, accelerating scientific discovery by uncovering relationships obscured by traditional graph methods.

[cond-mat updates on arXiv.org] A joint voxel flow - phase field framework for ultra-long microstructure evolution prediction with physical regularization

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2601.04898v1 Announce Type: cross +Abstract: Phase-field (PF) modeling is a powerful tool for simulating microstructure evolution. To overcome the high computational cost of PF in solving complex PDEs, machine learning methods such as PINNs, convLSTM have been used to predict PF evolution. However, current methods still face shortages of low flexibility, poor generalization and short predicting time length. In this work, we present a joint framework coupling voxel-flow network (VFN) with PF simulations in an alternating manner for long-horizon temporal prediction of microstructure evolution. The VFN iteratively predicts future evolution by learning the flow of pixels from past snapshots, with periodic boundaries preserved in the process. Periodical PF simulations suppresses nonphysical artifacts, reduces accumulated error, and extends reliable prediction time length. The VFN is about 1,000 times faster than PF simulation on GPU. In validation using grain growth and spinodal decomposition, MSE and SSIM remain 6.76% and 0.911 when predicted 18 frames from only 2 input frames, outperforming similar predicting methods. For an ultra-long grain growth prediction for 82 frames from 2 input frames, grain number decreases from 600 to 29 with NMSE of average grain area remaining 1.64%. This joint framework enables rapid, generalized, flexible and physically consistent microstructure forecasting from image-based data for ultra-long time scales.

[cond-mat updates on arXiv.org] Robust Reasoning as a Symmetry-Protected Topological Phase

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2601.05240v1 Announce Type: cross +Abstract: Large language models suffer from "hallucinations"-logical inconsistencies induced by semantic noise. We propose that current architectures operate in a "Metric Phase," where causal order is vulnerable to spontaneous symmetry breaking. Here, we identify robust inference as an effective Symmetry-Protected Topological phase, where logical operations are formally isomorphic to non-Abelian anyon braiding, replacing fragile geometric interpolation with robust topological invariants. Empirically, we demonstrate a sharp topological phase transition: while Transformers and RNNs exhibit gapless decay, our Holonomic Network reveals a macroscopic "mass gap," maintaining invariant fidelity below a critical noise threshold. Furthermore, in a variable-binding task on $S_{10}$ ($3.6 \times 10^6$ states) representing symbolic manipulation, we demonstrate holonomic generalization: the topological model maintains perfect fidelity extrapolating $100\times$ beyond training ($L=50 \to 5000$), consistent with a theoretically indefinite causal horizon, whereas Transformers lose logical coherence. Ablation studies indicate this protection emerges strictly from non-Abelian gauge symmetry. This provides strong evidence for a new universality class for logical reasoning, linking causal stability to the topology of the semantic manifold.

[cond-mat updates on arXiv.org] Machine learning short-ranged many-body interactions in colloidal systems using descriptors based on Voronoi cells

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2502.19044v2 Announce Type: replace +Abstract: Machine learning (ML) strategies are opening the door to faster computer simulations, allowing us to simulate more realistic colloidal systems. Since the interactions in colloidal systems are often highly many-body, stemming from e.g. depletion and steric interactions, one of the challenges for these algorithms is capturing the many-body nature of these interactions. In this paper, we introduce a new ML-based strategy for fitting many-body interactions in colloidal systems where the many-body interaction is highly local. To this end, we develop Voronoi-based descriptors for capturing the local environment and fit the effective potential using a simple neural network. To test this algorithm, we consider a simple two-dimensional model for a colloid-polymer mixture, where the colloid-colloid interactions and colloid-polymer interactions are hard-disk like, while the polymers themselves interact as ideal gas particles. We find that a Voronoi-based description is sufficient to accurately capture the many-body nature of this system. Moreover, we find that the Pearson correlation function alone is insufficient to determine the predictive power of the network emphasizing the importance of additional metrics when assessing the quality of ML-based potentials.

[cond-mat updates on arXiv.org] Characterizing the cage state of glassy systems and its sensitivity to frozen boundaries

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2507.16339v2 Announce Type: replace +Abstract: Understanding the role that structure plays in the dynamical arrest observed in glassy systems remains an open challenge. Over the last decade, machine learning (ML) strategies have emerged as an important tool for probing this structure-dynamics relationship, particularly for predicting heterogeneous glassy dynamics from local structure. A recent advancement is the introduction of the cage state, a structural quantity that captures the average positions of particles while rearrangements are forbidden. During the caging regime, linear models trained on the cage state have been shown to outperform more complex ML methods trained on initial configurations only. In this paper, we explore the properties associated with the cage state in more detail to better understand why it serves as such an effective predictor for the dynamics. Specifically, we examine how the cage state in a binary hard-sphere mixture is influenced by both packing fraction and boundary conditions. Our results reveal that, as the system approaches the glassy regime, the cage state becomes increasingly influenced by long-range structural effects. This influence is evident both in its predictive power for particle dynamics and in the internal structure of the cage state, suggesting that the CS might be associated with some form of an amorphous growing structural length scale.

[cond-mat updates on arXiv.org] Li+/H+ exchange in solid-state oxide Li-ion conductors

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2509.13477v2 Announce Type: replace +Abstract: Understanding the moisture stability of oxide Li-ion conductors is important for their practical applications in solid-state batteries. Unlike sulfide or halide conductors, oxide conductors generally better resist degradation when in contact with water, but can still undergo topotactic \ch{Li+}/\ch{H+} exchange (LHX). Here, we combine density functional theory (DFT) calculations with a machine-learning interatomic potential model to investigate the thermodynamic driving force of the LHX reaction for two representative oxide Li-ion conductor families: garnets and NASICONs. Li-stuffed garnets exhibit a strong driving force for proton exchange due to their high Li chemical potential. In contrast, NASICONs demonstrate a higher resistance against proton exchange due to the lower Li chemical potential and the lower O-H bond covalency for polyanion-bonded oxygens. Our findings reveal a critical trade-off: Li stuffing enhances conductivity but increases moisture susceptibility. This study underscores the importance of designing Li-ion conductors that possess both high conductivity and high stability in practical environments.

[cond-mat updates on arXiv.org] A universal machine learning model for the electronic density of states

cond-mat updates on arXiv.org — Fri, 09 Jan 2026 05:00:00 GMT

arXiv:2508.17418v2 Announce Type: replace-cross +Abstract: In the last few years several ``universal'' interatomic potentials have appeared, using machine-learning approaches to predict energy and forces of atomic configurations with arbitrary composition and structure, with an accuracy often comparable with that of the electronic-structure calculations they are trained on. Here we demonstrate that these generally-applicable models can also be built to predict explicitly the electronic structure of materials and molecules. We focus on the electronic density of states (DOS), and develop PET-MAD-DOS, a rotationally unconstrained transformer model built on the Point Edge Transformer (PET) architecture, and trained on the Massive Atomistic Diversity (MAD) dataset. We demonstrate our model's predictive abilities on samples from diverse external datasets, showing also that the DOS can be further manipulated to obtain accurate band gap predictions. A fast evaluation of the DOS is especially useful in combination with molecular simulations probing matter in finite-temperature thermodynamic conditions. To assess the accuracy of PET-MAD-DOS in this context, we evaluate the ensemble-averaged DOS and the electronic heat capacity of three technologically relevant systems: lithium thiophosphate (LPS), gallium arsenide (GaAs), and a high entropy alloy (HEA). By comparing with bespoke models, trained exclusively on system-specific datasets, we show that our universal model achieves semi-quantitative agreement for all these tasks. Furthermore, we demonstrate that fine-tuning can be performed using a small fraction of the bespoke data, yielding models that are comparable to, and sometimes better than, fully-trained bespoke models.

[RSC - Digital Discovery latest articles] MOFReasoner: Think Like a Scientist-A Reasoning Large Language Model via Knowledge Distillation

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

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

Open Access

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

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

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

Publication date: 28 February 2026

Source: Journal of Energy Storage, Volume 148

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

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

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

Publication date: 15 January 2026

Source: Science Bulletin, Volume 71, Issue 1

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

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

Publication date: 1 March 2026

Source: Solid State Ionics, Volume 436

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

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

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

Publication date: 1 March 2026

Source: Solid State Ionics, Volume 436

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

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

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

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

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

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

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

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

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

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

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

Small Methods, EarlyView.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Open Access

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

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

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

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

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

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

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

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

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

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

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

Publication date: 28 February 2026

Source: Journal of Energy Storage, Volume 148

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

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

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

Publication date: Available online 6 January 2026

Source: Progress in Materials Science

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

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

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

Advanced Functional Materials, EarlyView.

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

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

Advanced Science, EarlyView.

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

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

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03557

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

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

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202506736

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

[Wiley: Angewandte Chemie International Edition: Table of Contents] Outside Back Cover: Rhodopsin‐Mimicking Reversible Photo‐Switchable Chloride Channels Based on Azobenzene‐Appended Semiaza‐Bambusurils for Light‐Controlled Ion Transport and Cancer Cell Apoptosis

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

Angewandte Chemie International Edition, EarlyView.

[Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents] Thermodynamic Mechanisms of Co‐S Bond Anchoring in Few‐Layered 1T‐MoS2 for Enhanced Capacitive Performance via Spin State Regulation and Ion Diffusion Kinetics

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

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.

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

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

Advanced Materials, EarlyView.

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

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

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

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

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

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

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

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

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

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

Open Access

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

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

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

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

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

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

[Applied Physics Letters Current Issue] Pressure-induced evolution of the electronic structure and bandgap expansion in MgPbN 2

Applied Physics Letters Current Issue — Thu, 08 Jan 2026 00:00:00 GMT

The structural, electronic, and optical properties of MgPbN₂ under pressure have been systematically studied using first-principles calculations combined with the CALYPSO crystal structure prediction method. Two ambient pressure phases (Pna2₁ and I4¯2d) and two high-pressure phases ( R3¯m and Fd3¯m) were identified, all of which are dynamically, mechanically, and thermally stable, and exhibit semiconductor characteristics. Notably, their bandgaps increase with increasing pressure. This phenomenon is primarily attributed to two factors. First, the strengthened orbital coupling under pressure enhances electron cloud overlap, raising the energy of antibonding states (conduction band) and lowering the energy of bonding states (valence band). Second, high pressure alters the distribution of electron clouds, causing electrons to become more localized around the atoms. This localized electron distribution reduces electron transitions between energy bands, thereby increasing the bandgap. This analysis provides a unified view of the electronic structure evolution under compression, linking microscopic orbital interactions to macroscopic observable properties. The calculations and analysis of the optical absorption and reflectivity coefficient suggest that the two high-pressure phases of MgPbN₂ have potential applications in transparent optics and ultraviolet detection. This study provides insights into the role of pressure in tuning optical properties.

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

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

Publication date: 28 February 2026

Source: Journal of Energy Storage, Volume 148

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

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

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

Publication date: Available online 6 January 2026

Source: Progress in Materials Science

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

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

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

Advanced Functional Materials, EarlyView.

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

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

Advanced Science, EarlyView.

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

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

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03557

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

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

CCS Chemistry, Ahead of Print.

DOI:10.31635/ccschem.025.202506736

[Wiley: Angewandte Chemie International Edition: Table of Contents] Outside Back Cover: Rhodopsin‐Mimicking Reversible Photo‐Switchable Chloride Channels Based on Azobenzene‐Appended Semiaza‐Bambusurils for Light‐Controlled Ion Transport and Cancer Cell Apoptosis

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

Angewandte Chemie International Edition, EarlyView.

[Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents] Thermodynamic Mechanisms of Co‐S Bond Anchoring in Few‐Layered 1T‐MoS2 for Enhanced Capacitive Performance via Spin State Regulation and Ion Diffusion Kinetics

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

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.

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

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

Advanced Materials, EarlyView.

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

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

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

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

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

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

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

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

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

My Customized Papers

[RSC - Digital Discovery latest articles] MOFReasoner: Think Like a Scientist-A Reasoning Large Language Model via Knowledge Distillation

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

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

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

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

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

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

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

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

[cond-mat updates on arXiv.org] Towards understanding the defect properties in the multivalent A-site Na$_{0.5}$Bi$_{0.5}$TiO$_3$-based perovskite ceramics

[cond-mat updates on arXiv.org] Scalable Dielectric Tensor Predictions for Inorganic Materials using Equivariant Graph Neural Networks

[cond-mat updates on arXiv.org] Lateral Graphene-Metallene Interfaces at the Nanoscale

[cond-mat updates on arXiv.org] Stable Machine Learning Potentials for Liquid Metals via Dataset Engineering

[cond-mat updates on arXiv.org] Hierarchical Crystal Structure Prediction of Zeolitic Imidazolate Frameworks Using DFT and Machine-Learned Interatomic Potentials

[cond-mat updates on arXiv.org] Beyond the imbalance: site-resolved dynamics probing resonances in many-body localization

[cond-mat updates on arXiv.org] Chiral Graviton Modes in Fermionic Fractional Chern Insulators

[cond-mat updates on arXiv.org] Exact Multimode Quantization of Superconducting Circuits via Boundary Admittance

[cond-mat updates on arXiv.org] Higher-Order Knowledge Representations for Agentic Scientific Reasoning

[cond-mat updates on arXiv.org] A joint voxel flow - phase field framework for ultra-long microstructure evolution prediction with physical regularization

[cond-mat updates on arXiv.org] Robust Reasoning as a Symmetry-Protected Topological Phase

[cond-mat updates on arXiv.org] Machine learning short-ranged many-body interactions in colloidal systems using descriptors based on Voronoi cells

[cond-mat updates on arXiv.org] Characterizing the cage state of glassy systems and its sensitivity to frozen boundaries

[cond-mat updates on arXiv.org] Li+/H+ exchange in solid-state oxide Li-ion conductors

[cond-mat updates on arXiv.org] A universal machine learning model for the electronic density of states

[RSC - Digital Discovery latest articles] MOFReasoner: Think Like a Scientist-A Reasoning Large Language Model via Knowledge Distillation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[Wiley: Angewandte Chemie International Edition: Table of Contents] Outside Back Cover: Rhodopsin‐Mimicking Reversible Photo‐Switchable Chloride Channels Based on Azobenzene‐Appended Semiaza‐Bambusurils for Light‐Controlled Ion Transport and Cancer Cell Apoptosis

[Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents] Thermodynamic Mechanisms of Co‐S Bond Anchoring in Few‐Layered 1T‐MoS2 for Enhanced Capacitive Performance via Spin State Regulation and Ion Diffusion Kinetics

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

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

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

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

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

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

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

[Applied Physics Letters Current Issue] Pressure-induced evolution of the electronic structure and bandgap expansion in MgPbN 2

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

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

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

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

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

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

[Wiley: Angewandte Chemie International Edition: Table of Contents] Outside Back Cover: Rhodopsin‐Mimicking Reversible Photo‐Switchable Chloride Channels Based on Azobenzene‐Appended Semiaza‐Bambusurils for Light‐Controlled Ion Transport and Cancer Cell Apoptosis

[Wiley: ENERGY & ENVIRONMENTAL MATERIALS: Table of Contents] Thermodynamic Mechanisms of Co‐S Bond Anchoring in Few‐Layered 1T‐MoS2 for Enhanced Capacitive Performance via Spin State Regulation and Ion Diffusion Kinetics

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

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

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

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

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

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