Atomistic Modeling of Chemical Disorder in Materials: Bridging Classical Methods and AI-Assisted Approaches

2026-05-18

AI Summary

This work addresses the gap between experimental characterization—which provides ensemble-averaged occupancy information—and atomistic simulations or AI-driven workflows that require explicit atomic configurations to model chemically disordered materials. To bridge this divide, we propose a disorder-native AI paradigm that integrates mean-field theory, cluster expansion, Monte Carlo sampling, quasi-random approximations, universal interatomic potentials, and generative models into a unified framework. This framework efficiently translates averaged disorder descriptions into representative ensembles of atomic configurations. By enabling controllable modeling of chemical disorder—transforming it from a computational obstacle into a tunable variable—the approach endows AI workflows with configuration generation, ranking, and dynamics-aware capabilities. Consequently, it significantly enhances the accuracy and efficiency of simulations for complex disordered materials in tasks such as stability assessment, novelty evaluation, and experimental guidance.

Abstract

Chemical disorder, originating from the mixed occupation of crystallographic sites by multiple elements, is widespread in alloys, ceramics, and compositionally complex materials, where short- and long-range orderings can strongly influence properties. A central obstacle is the representation gap between experiments and simulations: experiments often report disorder as partial occupancies and ensemble-averaged behaviors, whereas atomistic simulations and AI workflows usually require fully specified configurations. Tackling this gap requires computational methods that convert averaged disorder descriptions into representative configurational ensembles while balancing cost, bias, and fidelity. This challenge has become more urgent in AI-driven computational discovery, where ignoring disorder may cause AI workflows to misrank stability, misjudge novelty, and misdirect experiments with too-idealized representations. This Review highlights how classical and AI-driven methods can bridge this representation gap. We assess the strengths and limitations of approaches spanning mean-field theories, cluster expansion, quasi-random approximations, Monte Carlo, and emerging schemes powered by universal interatomic potentials and generative models. We further highlight how AI can accelerate classical computational schemes by lowering the cost of microstate evaluation, configurational exploration, and atomistic-to-thermodynamic closure. We also emphasize how AI can enable disorder-native capabilities, including workflow triage, ordering-sensitive and alchemical representations, generative models of disordered structures and distributions, and kinetics-aware disorder prediction. Together, this framework outlines a practical roadmap toward disorder-native AI, which can transform chemical disorder from a representational obstacle into a controllable variable for realistic AI-accelerated materials discovery.

Problem

• chemical disorder
• representation gap
• atomistic modeling
• AI-driven materials discovery
• configurational ensemble

Innovation

• chemical disorder
• AI-assisted materials discovery
• generative models
• configurational ensembles
• disorder-native AI