arXiv subjects

Guoxiang Hu

Publications and source records attributed to Guoxiang Hu.

8 recordsLinked to original sources

arXiv2026

Symbolic Predicate-Guided Language Agents for Inverse Design of Perovskite Oxides

Efficient discovery of high-performance materials has been pursued through a variety of data- and AI-driven strategies, among which inverse design, generating materials from desired target properties, has emerged as an important paradigm. Large language models (LLMs) offer a complementary route for inverse materials design because their reasoning and in-context learning capability can be used not only to propose candidates but also to demonstrate interpretable design principles. In this work, we introduce a domain specific language (DSL)-guided strategy to improve the reasoning and design capability of LLM agents by translating natural language design rules into symbolic predicates encoded in a predefined chemistry DSL. These predicates allow the LLM agent to obtain statistical evidence from the accumulated materials data, enabling the agent to evaluate and refine its own reasoning during the design loop. Based on this strategy, we developed a multi-agent materials design framework, called Operational Rule-grounded CHEmical Search Through Reasoning Agents (ORCHESTRA), and applied it to the inverse design of double perovskite oxides under multiple target-property objectives. The results show that symbolic predicates help the LLM identify unsupported rules, validate newly proposed rules and improve the rule store over iterative design cycles. Compared with a strategy relying only on natural language rules, the DSL-guided framework showed the potential to improve materials design performance, particularly for challenging target properties. These findings suggest that mathematical and statistical grounding can enhance the reasoning capability of LLM agents in materials science and that LLM-based inverse design can be performed effectively without large task-specific datasets or additional model training.

cond-mat.mtrl-sci↗

arXiv2026

Ligand-Controlled Phonon Dynamics in CsPbBr3 Nanocrystals Revealed by Machine-Learned Interatomic Potentials

Halide perovskite nanocrystals are leading candidates for next-generation optoelectronics, yet the role of surface ligands in controlling their phonon dynamics remains poorly understood. These dynamics critically govern nonradiative relaxation, energy up-conversion, and phonon-assisted anti-Stokes emission. Conventional ab initio methods, while accurate, are computationally infeasible for experimentally relevant nanocrystal sizes that require thousands of atoms to capture realistic ligand shells and dynamic disorder at finite temperatures. Here, we introduce a machine- learned interatomic potential fine-tuned on small CsPbBr3 nanocrystals with diverse ligands, enabling accurate prediction of ligand-induced phonon properties far beyond the spatial and temporal scales of ab initio methods. We find that both cationic and anionic ligands systematically redshift Pb-Br-Pb stretching modes while blueshifting the PbBr64- octahedral rotation mode, with stronger overall effects for anionic passivation. Notably, anionic ligands stiffen the rotation mode non-monotonically with respect to the ligand binding energy. Our findings reveal important roles of cationic and anionic ligands in modulating key dynamic modes of halide perovskite nanocrystals associated with detrimental nonradiative losses, offering mechanistic insights and design principles for high-performance perovskite nanocrystal optoelectronics.

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arXiv2026

Reasoning-Driven Design of Single Atom Catalysts via a Multi-Agent Large Language Model Framework

Large language models (LLMs) are becoming increasingly applied beyond natural language processing, demonstrating strong capabilities in complex scientific tasks that traditionally require human expertise. This progress has extended into materials discovery, where LLMs introduce a new paradigm by leveraging reasoning and in-context learning, capabilities absent from conventional machine learning approaches. Here, we present a Multi-Agent-based Electrocatalyst Search Through Reasoning and Optimization (MAESTRO) framework in which multiple LLMs with specialized roles collaboratively discover high-performance single atom catalysts for the oxygen reduction reaction. Within an autonomous design loop, agents iteratively reason, propose modifications, reflect on results and accumulate design history. Through in-context learning enabled by this iterative process, MAESTRO identified design principles not explicitly encoded in the LLMs' background knowledge and successfully discovered catalysts that break conventional scaling relations between reaction intermediates. These results highlight the potential of multi-agent LLM frameworks as a powerful strategy to generate chemical insight and discover promising catalysts.

cond-mat.mtrl-sci↗

arXiv2025

Scalable Foundation Interatomic Potentials via Message-Passing Pruning and Graph Partitioning

Atomistic foundation models (AFMs) have great promise as accurate interatomic potentials, and have enabled data-efficient molecular dynamics simulations with near quantum mechanical accuracy. However, AFMs remain markedly slower at inference and are far more memory-intensive than conventional interatomic potentials, due to the need to capture a wide range of chemical and structural motifs in pre-training datasets requiring deep, parameter-rich model architectures. These deficiencies currently limit the practical use of AFMs in molecular dynamics (MD) simulations at extended temporal and spatial scales. To address this problem, we propose a general workflow for accelerating and scaling AFMs containing message-passing architectures. We find that removing low-contribution message-passing layers from AFM backbones serves as an effective pruning method, significantly reducing the parameter count while preserving the accuracy and data-efficiency of AFMs. Once pruned, these models become more accessible for large scale simulations via a graph-partitioned, GPU-distributed strategy, which we implement and demonstrate within the AFM fine-tuning platform MatterTune. We show that this approach supports million-atom simulations on both single and multiple GPUs, and enables task-specific large-scale simulations at nanosecond timescales with AFM-level accuracy.

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arXiv2025

MatterTune: An Integrated, User-Friendly Platform for Fine-Tuning Atomistic Foundation Models to Accelerate Materials Simulation and Discovery

Geometric machine learning models such as graph neural networks have achieved remarkable success in recent years in chemical and materials science research for applications such as high-throughput virtual screening and atomistic simulations. The success of these models can be attributed to their ability to effectively learn latent representations of atomic structures directly from the training data. Conversely, this also results in high data requirements for these models, hindering their application to problems which are data sparse which are common in this domain. To address this limitation, there is a growing development in the area of pre-trained machine learning models which have learned general, fundamental, geometric relationships in atomistic data, and which can then be fine-tuned to much smaller application-specific datasets. In particular, models which are pre-trained on diverse, large-scale atomistic datasets have shown impressive generalizability and flexibility to downstream applications, and are increasingly referred to as atomistic foundation models. To leverage the untapped potential of these foundation models, we introduce MatterTune, a modular and extensible framework that provides advanced fine-tuning capabilities and seamless integration of atomistic foundation models into downstream materials informatics and simulation workflows, thereby lowering the barriers to adoption and facilitating diverse applications in materials science. In its current state, MatterTune supports a number of state-of-the-art foundation models such as ORB, MatterSim, JMP, and EquformerV2, and hosts a wide range of features including a modular and flexible design, distributed and customizable fine-tuning, broad support for downstream informatics tasks, and more.

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arXiv2022

Origin of Metal-Insulator Transitions in Correlated Perovskite Metals

The mechanisms that drive metal-to-insulator transitions (MIT) in correlated solids are not fully understood. For example, the perovskite (PV) SrCoO3 is a FM metal while the oxygen-deficient (n-doped) brownmillerite (BM) SrCoO2.5 is an anti-ferromagnetic (AFM) insulator. Given the magnetic and structural transitions that accompany the MIT, the driver for such a MIT transition is unclear. We also observe that the perovskite metals LaNiO3, SrFeO3, and SrCoO3 also undergo MIT when n-doped via high-to-low valence compositional changes. Also, pressurizing the insulating BM SrCoO2.5 phase, drives a gap closing. Using DFT and correlated diffusion Monte Carlo approaches we demonstrate that the ABO3 perovskites most prone to MIT are self hole-doped materials, reminiscent of a negative charge-transfer system. Upon n-doping away from the negative-charge transfer metallic phase, an underlying charge-lattice (or e-phonon) coupling drives the system to a bond-disproportionated gapped state, thereby achieving ligand hole passivation at certain sites only, leading to charge-disproportionated states. The size of the gap opened is correlated with the size of the hole-filling at these ligand sites. This suggests that the interactions driving the gap opening to realize a MIT even in correlated metals is the charge-transfer energy, but it couples with the underlying phonons to enable the transition to the insulating phase. Other orderings (magnetic, charge, etc.) driven by weaker interactions are secondary and may assist gap openings at small dopings, but its the charge-transfer energy that predominantly determines the bandgap, with a negative energy preferring the metallic phase. This n-doping can be achieved by modulations in stoichiometry or composition or pressure. Hence, controlling the amount of the ligand-hole is key in controlling MIT. We compare our predictions to experiments where possible.

cond-mat.mtrl-sci↗

arXiv2021

A Combined First Principles Study of the Structural, Magnetic, and Phonon Properties of Monolayer CrI$_{3}$

The first magnetic 2D material discovered, monolayer (ML) CrI$_3$, is particularly fascinating due to its ground state ferromagnetism. Yet, because monolayer materials are difficult to probe experimentally, much remains unresolved about ML CrI$_{3}$'s structural, electronic, and magnetic properties. Here, we leverage Density Functional Theory (DFT) and high-accuracy Diffusion Monte Carlo (DMC) simulations to predict lattice parameters, magnetic moments, and spin-phonon and spin-lattice coupling of ML CrI$_{3}$. We exploit a recently developed surrogate Hessian DMC line search technique to determine CrI$_{3}$'s monolayer geometry with DMC accuracy, yielding lattice parameters in good agreement with recently-published STM measurements - an accomplishment given the $\sim 10$% variability in previous DFT-derived estimates depending upon the functional. Strikingly, we find previous DFT predictions of ML CrI$_3$'s magnetic spin moments are correct on average across a unit cell, but miss critical local spatial fluctuations in the spin density revealed by more accurate DMC. DMC predicts magnetic moments in ML CrI$_3$ are 3.62 $μ_B$ per chromium and -0.145 $μ_B$ per iodine; both larger than previous DFT predictions. The large disparate moments together with the large spin-orbit coupling of CrI$_3$'s I-$\textit{p}$ orbital suggests a ligand superexchange-dominated magnetic anisotropy in ML CrI$_3$, corroborating recent observations of magnons in its 2D limit. We also find ML CrI$_3$ exhibits a substantial spin-phonon coupling of $\sim$3.32 cm$^{-1}$. Our work thus establishes many of ML CrI$_{3}$'s key properties, while also continuing to demonstrate the pivotal role DMC can assume in the study of magnetic and other 2D materials.

cond-mat.mtrl-sci↗

arXiv2021

Inverse design of two-dimensional materials with invertible neural networks

The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery. However, thoroughly and efficiently sampling the entire design space in a computationally tractable manner remains a highly challenging task. To tackle this problem, we propose an inverse design framework (MatDesINNe) utilizing invertible neural networks which can map both forward and reverse processes between the design space and target property. This approach can be used to generate materials candidates for a designated property, thereby satisfying the highly sought-after goal of inverse design. We then apply this framework to the task of band gap engineering in two-dimensional materials, starting with MoS2. Within the design space encompassing six degrees of freedom in applied tensile, compressive and shear strain plus an external electric field, we show the framework can generate novel, high fidelity, and diverse candidates with near-chemical accuracy. We extend this generative capability further to provide insights regarding metal-insulator transition, important for memristive neuromorphic applications among others, in MoS2 which is not otherwise possible with brute force screening. This approach is general and can be directly extended to other materials and their corresponding design spaces and target properties.

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