My Customized Papers (Auto-Filtered)

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

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

Publication date: February 2026

Source: Journal of Catalysis, Volume 454

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

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

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

Small, EarlyView.

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

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

Small, EarlyView.

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

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

Advanced Materials, EarlyView.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

arXiv:2512.23010v1 Announce Type: cross +Abstract: Density functional theory (DFT) and machine learning potentials (MLPs) are essential for predicting and understanding materials properties, yet preparing, executing, and analyzing these simulations typically requires extensive scripting, multi-step procedures, and significant high-performance computing (HPC) expertise. These challenges hinder reproducibility and slow down discovery. Here, we introduce Masgent, an AI-assisted materials simulation agent that unifies structure manipulation, automated VASP input generation, DFT workflow construction and analysis, fast MLP-based simulations, and lightweight machine learning (ML) utilities within a single platform. Powered by large language models (LLMs), Masgent enables researchers to perform complex simulation tasks through natural-language interaction, eliminating most manual scripting and reducing setup time from hours to seconds. By standardizing protocols and integrating advanced simulation and data-driven tools, Masgent democratizes access to state-of-the-art computational methodologies, accelerating hypothesis testing, pre-screening, and exploratory research for both new and experienced practitioners.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[ChemRxiv] Probabilistic Forecasting for Coarse-Grained Molecular Dynamics

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

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

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

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

Publication date: February 2026

Source: Journal of Catalysis, Volume 454

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

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

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

Small, EarlyView.

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

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

Small, EarlyView.

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

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

Advanced Materials, EarlyView.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[iScience] River plastic hotspot detection from space

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

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

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

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

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

[ChemRxiv] Machine learning the quantum topology of chemical bonds

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

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

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

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

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

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

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

We present a GPU-accelerated software package for the evaluation of analytic two-electron energy and gradient contributions in Hartree-Fock (HF) and Density Functional theory (DFT) calculations. The implementation is provided as a Python library with a C++ backend, enabling straightforward integration into modern computational chemistry and drug-discovery workflows. The code supports single-point energy and nuclear gradient evaluations on both single- and multi-GPU systems, and employs MPI-based parallelization with dynamic load balancing in multi-node environments. +Abstract: Imaging and hyperspectral data analysis is central to progress across biology, medicine, chemistry, and physics. The core challenge lies in converting high-resolution or high-dimensional datasets into interpretable representations that enable insight into the underlying physical or chemical properties of a system. Traditional analysis relies on expert-designed, multistep workflows, such as denoising, feature extraction, clustering, dimensionality reduction, and physics-based deconvolution, or on machine learning (ML) methods that accelerate individual steps. Both approaches, however, typically demand significant human intervention, including hyperparameter tuning and data labeling. Achieving the next level of autonomy in scientific imaging requires designing effective reward-based workflows that guide algorithms toward best data representation for human or automated decision-making. Here, we discuss recent advances in reward-based workflows for image analysis, which capture key elements of human reasoning and exhibit strong transferability across various tasks. We highlight how reward-driven approaches enable a shift from supervised black-box models toward explainable, unsupervised optimization on the examples of Scanning Probe and Electron Microscopies. Such reward-based frameworks are promising for a broad range of applications, including classification, regression, structure-property mapping, and general hyperspectral data processing.

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

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

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

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

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

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

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

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

[iScience] River plastic hotspot detection from space

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

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

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

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

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

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

[ChemRxiv] Machine learning the quantum topology of chemical bonds

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

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

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

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

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

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

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

ACS Nano

DOI: 10.1021/acsnano.5c15161

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

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

Publication date: February 2026

Source: Computational Materials Science, Volume 263

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

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

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

Publication date: Available online 26 December 2025

Source: Materials Today Physics

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

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

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

Publication date: Available online 26 December 2025

Source: Materials Today Physics

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

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

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

Journal of Chemical Theory and Computation

DOI: 10.1021/acs.jctc.5c01610

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

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

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03196

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

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

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c03397

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

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

The Journal of Physical Chemistry Letters

DOI: 10.1021/acs.jpclett.5c02968

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

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

The Journal of Physical Chemistry C

DOI: 10.1021/acs.jpcc.5c05232

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

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

Advanced Materials, EarlyView.

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

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

Advanced Functional Materials, EarlyView.

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

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

Advanced Functional Materials, EarlyView.

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

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

ACS Nano

DOI: 10.1021/acsnano.5c16117

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

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

Advanced Science, EarlyView.

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

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

Carbon Energy, EarlyView.

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

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

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

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

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

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

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

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

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

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

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

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

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

Publication date: 15 February 2026

Source: Solid State Ionics, Volume 435

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

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

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

Publication date: 30 December 2025

Source: Science Bulletin, Volume 70, Issue 24

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

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

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

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

My Customized Papers (Auto-Filtered)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[ChemRxiv] Probabilistic Forecasting for Coarse-Grained Molecular Dynamics

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[iScience] River plastic hotspot detection from space

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

[ChemRxiv] Machine learning the quantum topology of chemical bonds

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

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

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

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

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

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

[iScience] River plastic hotspot detection from space

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

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

[ChemRxiv] Machine learning the quantum topology of chemical bonds

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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