arXiv subjects

David J. Srolovitz

Publications and source records attributed to David J. Srolovitz.

At least 19 recordsLinked to original sources

arXiv2026

Harnessing agent memory to build lifelong AI partners for materials scientists

Materials research advances through accumulated experience - scripts that work, protocols that are trusted, warnings attached to failed calculations or experiments, and judgement that links a new question to an old result. This experience is essential for reproducibility and knowledge transfer, yet it is usually fragmented across notebooks, repositories, job logs and individual memory, and it is rarely portable across artificial-intelligence agents. Here we argue that a lifelong AI partner for materials science can be designed around persistent memory rather than around a particular agent implementation. We introduce a self-evolving memory framework that stores scientific experience as inspectable facts and executable skills, so that observations, failure boundaries, protocols and validation checks can be retrieved, revised and migrated across models. We evaluate the idea in three computational settings that expose different layers of materials-research competence. In 49 real-world materials-tool-use questions comprising 138 executable subtasks, memory nearly doubles GPT-5.2 task success without model-parameter updates. In elemental-solid equation-of-state calculations, memory converts a wavefunction-initialization failure into a pre-execution guardrail, improving outcomes from 22/1/4 to 25/2/0 Correct/Partial/Error and avoiding 92% of repeated errors. In 13 practical material simulation workflows, remembered skills and failure facts halve the aggregate trace burden (tokens) and reduce tool calls by over a factor of two by the third round, while preserving physically meaningful outputs in band-gap, phonon, vacancy and work-function analyses. These results show that agent memory can serve as a durable scientific asset; a portable, self-improving record of materials-research experience that outlives any single model or agent stack.

cs.AI↗

arXiv2026

A Generalized Frank-Bilby Equation for Interfaces in Crystalline Materials

The classical Frank-Bilby equation (FBE) is commonly used to predict the structure of interfaces in crystalline materials in terms of interfacial dislocation networks. However, in general, the line defects in interfaces are disconnections, possessing both dislocation and step character, which are not captured by the classical FBE. As a result, the FBE cannot fully describe the structure of most interfaces of practical interest. To address this issue, we derive a generalized Frank-Bilby equation (GFBE) that explicitly incorporates both dislocation and step components of interfacial defects. We demonstrate its application to several representative interface systems.

cond-mat.mtrl-sci↗

arXiv2026

Atomistic Machine Learning with Irreducible Cartesian Natural Tensors

Atomistic machine learning is a powerful tool for accurate and efficient investigation of material behavior at the atomic scale. While attempts have been made to construct models directly within Cartesian space, they face challenges in providing a systematic framework based on irreducible representations - a core feature of widely used spherical models. Here we propose Cartesian Natural Tensor Networks to overcome these limitations and thus offer a general, symmetry-preserving framework for atomistic machine learning. We present a theory of irreducible representations using Cartesian natural tensors, comprising their construction, their products, and a systematic scheme to decompose and reconstruct high-rank physical tensors. Leveraging this machinery, we develop equivariant machine learning interatomic potentials for materials and molecular systems with performance on par with leading spherical models. It further captures accurate structure-property relationships for tensorial quantities ranging from low-rank dipole moments to high-rank tensors with complex symmetries, such as the elastic constant tensor.

cond-mat.mtrl-sci↗

arXiv2026

Shear-Coupled Grain Growth Statistics

Grain growth (GG), driven by grain boundary (GB) migration, is a fundamental mechanism of microstructural evolution in polycrystalline materials. GB migration is frequently accompanied by a relative shear displacement of grains meeting at GBs, a phenomenon known as shear coupling. This coupling induces internal stresses within the microstructure, which recent studies have shown to play a decisive role in dictating the evolution of microstructure and GG pathways. This work provides a detailed characterization of the statistical features of two-dimensional GG in the presence of GB shear coupling through continuum modeling of GB migration that incorporates fundamental microscopic mechanisms and diffuse-interface simulations. We demonstrate that incorporating shear coupling produces a more heterogeneous, less equiaxed microstructure than conventional curvature-driven GG, while yielding topological and geometric properties consistent with experimental and atomistic observations. We further demonstrate that as grain grows, internal stress relaxes. Highly stressed grains shrink faster, and lightly stressed grains grow faster than other grains. These findings demonstrate that internal stress, an intrinsic feature of GG, profoundly changes essential features of GG microstructure and kinetics, consistent with experiments and atomic-scale simulations.

cond-mat.mtrl-sci↗

arXiv2026

A Multi-agent Framework for Physical Laws Discovery

Discovering explicit physical laws has traditionally depended on human intuition and domain expertise. Recent advances in artificial intelligence, particularly large language models (LLMs), offer a new route to accelerate this process by automating key steps from hypothesis generation to interpretable model construction. Here we develop an LLM-based multi-agent framework for physical-law discovery that integrates literature-guided variable selection, hypothesis formulation, symbolic regression, formula derivation, and mechanistic explanation. We validate the framework on three representative materials problems: the glass-forming ability (GFA) of metallic glasses, the Vickers hardness of compounds, and the Young's modulus of multi-component alloys. Using physically and chemically meaningful descriptors as inputs, the discovered formulas achieve strong agreement with reference data, with correlation coefficients up to 0.94 (GFA), 0.86 (hardness), and 0.94 (Young's modulus), while remaining compact and interpretable. Beyond fitting, the Young's modulus formula generalizes to quaternary and quinary alloys, improving prediction accuracy by up to 78.8% relative to the classical rule of mixtures. By integrating cross-disciplinary knowledge, reflection mechanisms, and expert-like reasoning ability into symbolic regression, our AI-centric framework offers a robust and extensible platform for automated physical laws discovery, demonstrating that AI can increasingly serve as an essential role in modern scientific research by thinking and acting like field experts.

cond-mat.mtrl-sci↗

arXiv2025

MatTools: Benchmarking Large Language Models for Materials Science Tools

Large language models (LLMs) are increasingly applied to materials science questions, including literature comprehension, property prediction, materials discovery and alloy design. At the same time, a wide range of physics-based computational approaches have been developed in which materials properties can be calculated. Here, we propose a benchmark application to evaluate the proficiency of LLMs to answer materials science questions through the generation and safe execution of codes based on such physics-based computational materials science packages. MatTools is built on two complementary components: a materials simulation tool question-answer (QA) benchmark and a real-world tool-usage benchmark. We designed an automated methodology to efficiently collect real-world materials science tool-use examples. The QA benchmark, derived from the pymatgen (Python Materials Genomics) codebase and documentation, comprises 69,225 QA pairs that assess the ability of an LLM to understand materials science tools. The real-world benchmark contains 49 tasks (138 subtasks) requiring the generation of functional Python code for materials property calculations. Our evaluation of diverse LLMs yields three key insights: (1)Generalists outshine specialists;(2)AI knows AI; and (3)Simpler is better. MatTools provides a standardized framework for assessing and improving LLM capabilities for materials science tool applications, facilitating the development of more effective AI systems for materials science and general scientific research.

cond-mat.mtrl-sci↗

arXiv2025

Atomistic Structure of Transient Switching States in Ferroelectric AlScN

We resolve the microscopic mechanism of polarization switching in wurtzite ferroelectric AlScN by integrating advanced thin-film fabrication, ferroelectric switching dynamics characterizations, high-resolution scanning transmission electron microscopy (STEM), and large-scale molecular dynamics simulations enabled by a deep neural network-based interatomic potential. Contrary to earlier interpretations proposing a transient nonpolar intermediate phase, we demonstrate that the broad transitional regions previously observed in STEM images are projection artifacts resulting from the intrinsic three-dimensional zigzag morphology of 180$^\circ$ domain walls, which are a characteristic form of inversion domain boundary. This is further confirmed by STEM imaging of strategically prepared, partially switched Al$_{0.75}$Sc$_{0.25}$N thin films. Our simulations reveal that switching proceeds through collective, column-by-column atomic displacements, directly explaining the emergence of zigzag-shaped domain walls, and is consistent with the nucleation-limited switching behavior observed in experimental switching dynamic measurements. Furthermore, we show that increasing Sc content systematically lowers domain wall energy and associated nucleation barrier, thereby reducing the switching field in agreement with experimental trends. These findings establish a direct connection between local domain wall structure, switching kinetics, and macroscopic ferroelectric behavior.

cond-mat.mtrl-sci↗

arXiv2025

Electron hopping induced phonon pumping in opto-mechanical molecular nanocavities

Plasmonic molecular nanojunctions exhibit opto-mechanical coupling at the nanoscale, enabling intertwined optical, vibrational and electronic phenomena. Here, we demonstrate plasmon-mediated phonon pumping, driven by inelastic electron hopping in conductive molecules, which results in strong Raman nonlinearity at the light intensities almost three orders of magnitude lower than in the conventional opto-mechanical systems and up to four-fold enhancement of the effective Raman polarizability due to vibrational electron-phonon coupling, as confirmed by the significant increase in anti-Stokes Raman scattering intensity, indicating enhanced vibrational occupancy. We also developed a microscopic framework of opto-mechanical electron-phonon coupling in molecular nanojunctions based on the Marcus electron hopping. Systematically varying electrical conductance of the molecules in the junction and laser intensity, we observed the transition between a photo-assisted tunneling regime and an electron hopping process. Our findings provide a microscopic description for vibrational, optical, and electronic phenomena in plasmonic nanocavities important for efficient phonon lasing, representing the first attempt to exploit conductive molecules as quantum-mechanical oscillators.

physics.optics↗

arXiv2025

Why Grain Growth is Not Curvature Flow

Grain growth in polycrystals is traditionally considered a capillarity-driven process, where grain boundaries (GBs) migrate toward their centers of curvature (i.e., mean curvature flow) with a velocity proportional to the local curvature (including extensions to account for anisotropic GB energy and mobility). Experimental and simulation evidence shows that this simplistic view is untrue. We demonstrate that the failure of the classical mean curvature flow description of grain growth mainly originates from the shear deformation naturally coupled with GB motion (i.e., shear coupling). Our findings are built on large-scale microstructure evolution simulations incorporating the fundamental (crystallography-respecting) microscopic mechanism of GB migration. The nature of the deviations from curvature flow revealed in our simulations is consistent with observations in recent experimental studies on different materials. This work also demonstrates how to incorporate the mechanical effects that are essential to the accurate prediction of microstructure evolution.

cond-mat.mtrl-sci↗

arXiv2025

Hot-electron-injection-induced symmetry breaking in bilayer MoS$_2$ probed by second-harmonic generation

Symmetry governs the selection rules of light-matter interactions in crystalline materials, making symmetry manipulation a powerful tool for tuning their optical properties. Here, we demonstrate that the hot-electron injection from a plasmonic resonator breaks the centrosymmtry of an adjacent transition metal dichalcogenide bilayer, probed via second-harmonic generation (SHG) in a Au-nanoparticle@bilayer-MoS$_2$@Au-film hybrid system. Power-dependent SHG measurements exhibit saturation behavior, consistent with a capacitor model where interfacial charge accumulation creates a dynamic barrier limiting further electron injection. Polarization-resolved SHG measurements reveal anisotropic second-order susceptibility response under hot-electron injection, where the contrast between different susceptibility components provides a quantitative measure of symmetry-breaking anisotropy. First-principles calculations elucidate the nonlinear optical responses evolution in bilayer MoS$_2$ and comfirm the anisotropic modification of susceptibility components under hot-electron injection, modeled by a perpendicular electric field. Our work establishes SHG as an effective probe of hot-electron-induced symmetry breaking in 2D materials, demonstrating a promising approach for ultrafast manipulation of material properties through controlled charge injection at the nanoscale.

physics.optics↗

arXiv2025

Active Learning for Conditional Inverse Design with Crystal Generation and Foundation Atomic Models

Artificial intelligence (AI) is transforming materials science, enabling both theoretical advancements and accelerated materials discovery. Recent progress in crystal generation models, which design crystal structures for targeted properties, and foundation atomic models (FAMs), which capture interatomic interactions across the periodic table, has significantly improved inverse materials design. However, an efficient integration of these two approaches remains an open challenge. Here, we present an active learning framework that combines crystal generation models and foundation atomic models to enhance the accuracy and efficiency of inverse design. As a case study, we employ Con-CDVAE to generate candidate crystal structures and MACE-MP-0 FAM as one of the high-throughput screeners for bulk modulus evaluation. Through iterative active learning, we demonstrate that Con-CDVAE progressively improves its accuracy in generating crystals with target properties, highlighting the effectiveness of a property-driven fine-tuning process. Our framework is general to accommodate different crystal generation and foundation atomic models, and establishes a scalable approach for AI-driven materials discovery. By bridging generative modeling with atomic-scale simulations, this work paves the way for more accurate and efficient inverse materials design.

cond-mat.mtrl-sci↗

arXiv2024

Large Language Models for Material Property Predictions: elastic constant tensor prediction and materials design

Efficient and accurate prediction of material properties is critical for advancing materials design and applications. The rapid-evolution of large language models (LLMs) presents a new opportunity for material property predictions, complementing experimental measurements and multi-scale computational methods. We focus on predicting the elastic constant tensor, as a case study, and develop domain-specific LLMs for predicting elastic constants and for materials discovery. The proposed ElaTBot LLM enables simultaneous prediction of elastic constant tensors, bulk modulus at finite temperatures, and the generation of new materials with targeted properties. Moreover, the capabilities of ElaTBot are further enhanced by integrating with general LLMs (GPT-4o) and Retrieval-Augmented Generation (RAG) for prediction. A specialized variant, ElaTBot-DFT, designed for 0 K elastic constant tensor prediction, reduces the prediction errors by 33.1% compared with domain-specific, material science LLMs (Darwin) trained on the same dataset. This natural language-based approach lowers the barriers to computational materials science and highlights the broader potential of LLMs for material property predictions and inverse design.

cond-mat.mtrl-sci↗

arXiv2024

Strain-Enabled Giant Second-Order Susceptibility in Monolayer WSe$_2$

Monolayer WSe$_2$ (ML WSe$_2$) exhibits a high second-harmonic generation (SHG) efficiency under single 1-photon (1-p) or 2-photon (2-p) resonant excitation conditions due to enhanced second-order susceptibility compared with off-resonance excitation states \cite{lin2021narrow,wang2015giant}. Here, we propose a novel strain engineering approach to dramatically boost the in-plane second-order nonlinear susceptibility ($χ_{yyy}$ ) of ML WSe$_2$ by tuning the biaxial strain to shift two K-valley excitons (the A-exciton and a high-lying exciton (HX)) into double resonance. We first identify the A-exciton and HX from the 2D Mott-Wannier model for pristine ML WSe$_2$ and calculate the $χ_{yyy}$ under either 1-p or 2-p resonance excitations, and observe a $\sim$ 39-fold $χ_{yyy}$ enhancement arising from the 2-p HX resonance state compared with the A-exciton case. By applying a small uniform biaxial strain (0.16\%), we observe an exciton double resonance state ($E_{\rm{HX}}$ = 2$E_{\rm{A}}$, $E_{\rm{HX}}$ and $E_{\rm{A}}$ are the exciton absorption energies), which yields up to an additional 52-fold enhancement in $χ_{yyy}$ compared to the 2-p HX resonance state, indicating an overall $\sim$ 2000-fold enhancement compared to the single 2-p A-exciton resonance state reported in Ref \cite{wang2015giant}. Further exploration of the strain-engineered exciton states (with biaxial strain around 0.16\%) reveals that double resonance also occurs at other wavevectors near the K valley, leading to other enhancement states in $χ_{yyy}$, confirming that strain engineering is an effective approach for enhancing $χ_{yyy}$. Our findings suggest new avenues for strain engineering the optical properties of 2D materials for novel nonlinear optoelectronic applications.

physics.optics↗

arXiv2024

Dynamics of Small Solid Particles on Substrates of Arbitrary Topography

We study the dynamics of a small solid particle arising from the dewetting of a thin film on a curved substrate driven by capillarity, where mass transport is controlled by surface diffusion. We consider the case when the size of the deposited particle is much smaller than the local radius of curvature of the substrate surface. The application of the Onsager variational principle leads to a reduced-order model for the dynamic behaviour of particles on arbitrarily curved substrates. We demonstrate that particles move toward region of the substrate surface with lower mean curvature with a determined velocity. In particular, the velocity is proportional to the substrate curvature gradient and inversely proportional to the size of the particle, with a coefficient that depends on material properties that include the surface energy, surface diffusivity, density, and Young's (wetting) angle. The reduced model is validated by comparing with numerical results for the full, sharp-interface model in both two and three dimensions.

cond-mat.mtrl-sci↗

arXiv2024

An Extendable Cloud-Native Alloy Property Explorer

The ability to rapidly evaluate materials properties through atomistic simulation approaches is the foundation of many new artificial intelligence-based approaches to materials identification and design. This depends on the availability of accurate descriptions of atomic bonding through various forms of interatomic potentials. We present an efficient, robust platform for calculating materials properties, i.e., APEX, the Alloy Property Explorer. APEX enables the rapid evolution of interatomic potential development and optimization, which is of particular importance in fine-tuning new classes of general AI-based foundation models to forms that are readily applicable to impacting materials development. APEX is an open-source, extendable, and cloud-native platform for material property calculations using a range of atomistic simulation methodologies that effectively manages diverse computational resources and is built upon user-friendly features including automatic results visualization, web-based platforms and NoSQL database client. It is designed for expert and non-specialist users, lowers the barrier to entry for interdisciplinary research within the "AI for Materials" framework. We describe the foundation and use of APEX, as well as provide an example of its application to properties of titanium for a wide-range of bonding descriptions.

cond-mat.mtrl-sci↗

arXiv2024

Grain boundary metastability controls irradiation resistance in nanocrystalline metals

Grain boundaries (GBs) in polycrystalline materials are powerful sinks for irradiation defects. While standard theories assume that the sink efficiency of a grain boundary is defined solely by its character before irradiation, recent evidence conclusively shows that the irradiation sink efficiency is a highly dynamic property controlled by the intrinsic metastability of GBs under far-from-equilibrium irradiation conditions. In this paper, we reveal that the denuded (i.e., defect-free) zone, typically the signature of a strong sink, can collapse as irradiation damage accumulates. We propose a radiation damage evolution model that captures this behavior based on the emergence of a series of irradiation defect-enabled metastable GB microstate changes that dynamically alter the ability of the GB to absorb further damage. We show that these microstate changes control further defect absorption and give rise to the formation of a defect network that manifests itself as a net Nye-tensor signal detectable via lattice curvature experiments.

cond-mat.mtrl-sci↗

arXiv2024

A "Magnetic" Machine Learning Interatomic Potential for Nickel

Nickel (Ni) is a magnetic transition metal with two allotropic phases, stable face-centered cubic (FCC) and metastable hexagonal close-packed (HCP), widely used in structural applications. Magnetism affects many mechanical and defect properties, but spin-polarized density functional theory (DFT) calculations are computationally inefficient for studying material behavior requiring large system sizes and/or long simulation times. Here we develop a "magnetism-hidden" machine-learning Deep Potential (DP) model for Ni without a descriptor for magnetic moments, using training datasets derived from spin-polarized DFT calculations. The "magnetism-hidden" DP-Ni model exhibits high transferability and representability for a wide-range of FCC and HCP properties, including (finite-temperature) lattice parameters, elastic constants, phonon spectra, and many defects. As an example of its applicability, we investigate the Ni FCC-HCP allotropic phase transition under (high-stress) uniaxial tensile loading. The DP model for magnetic Ni facilitates accurate large-scale atomistic simulations for complex mechanical behavior and serves as a foundation for developing interatomic potentials for Ni-based superalloys and other multi-principal component alloys.

cond-mat.mtrl-sci↗

arXiv2024

A Prompt-Engineered Large Language Model, Deep Learning Workflow for Materials Classification

Large language models (LLMs) have demonstrated rapid progress across a wide array of domains. Owing to the very large number of parameters and training data in LLMs, these models inherently encompass an expansive and comprehensive materials knowledge database, far exceeding the capabilities of individual researcher. Nonetheless, devising methods to harness the knowledge embedded within LLMs for the design and discovery of novel materials remains a formidable challenge. We introduce a general approach for addressing materials classification problems, which incorporates LLMs, prompt engineering, and deep learning. Utilizing a dataset of metallic glasses as a case study, our methodology achieved an improvement of up to 463% in prediction accuracy compared to conventional classification models. These findings underscore the potential of leveraging textual knowledge generated by LLMs for materials especially in the common situation where datasets are sparse, thereby promoting innovation in materials discovery and design.

cond-mat.mtrl-sci↗