Towards Computational Microscope of Chemical Order-Disorder via ML-Accelerated Monte Carlo Simulation

TL;DR

This paper develops and benchmarks machine learning models to enhance Monte Carlo simulations for understanding chemical order-disorder phenomena in complex materials, aiming to bridge the gap between theory and experiment.

Contribution

It systematically evaluates ML architectures for chemical simulations, decouples interaction effects, and assesses the impact of lattice relaxation, advancing ML-accelerated Monte Carlo methods.

Findings

Benchmarking identifies optimal ML models for chemical simulations.

Decoupling interactions reveals their roles in chemical ordering.

Lattice relaxation influences model performance and accuracy.

Abstract

Tailoring the performance of next-generation high entropy materials requires a deep understanding of the competition between entropy-driven random solid solution and enthalpy-driven chemical ordering. Investigating such order and disorder complexity demands atomistic simulations that achieve high accuracy, efficiency, and generalizability across vast spatial, temporal, and especially chemical scales. While machine learning (ML) interatomic potentials have transformed molecular dynamics, they remain limited in capturing diffusion-driven chemical evolution over long timescales. The recently introduced SMC-X method brings exciting opportunities. Realizing its full potential requires a comprehensive study, which is the focus of this work. To assess model performance, we systematically benchmark invariant and equivariant architectures using a density functional theory dataset of more than 10,000 configurations spanning seven elements: Fe, Co, Ni, Al, Ti, Ta, and V. To understand the roles of pairwise and higher-order interactions, we decouple their contributions across chemical space using an explainable machine learning approach. We also examine the impact of lattice relaxation by comparing models trained on datasets with and without structural relaxation. Our results clarify how to choose ML surrogate models for Monte Carlo simulations, bridge the gap between theory and experiment, and lay a foundation for establishing ML-accelerated Monte Carlo as a computational microscope for chemical complexity.