A Machine Learning Framework for Modeling Ensemble Properties of Atomically Disordered Materials.

ABSTRACT: Atomic disorder can strongly influence material properties such as charge transport, optical response, and catalytic activity. However, efficiently modeling these disorder effects remains challenging for first-principles methods due to the cost of sampling large configurational spaces and computing complex physical quantities. Recent advances of machine learning techniques, particularly graph neural networks (GNNs), has enabled the efficient and accurate predictions of complex material properties, offering promising tools for studying disordered systems. In this work, we present a general machine-learning-assisted computational framework that integrates equivariant GNNs with Monte Carlo simulations to compute the thermodynamic and ensemble-averaged functional properties of disordered mater

SUBMITTER: Fang Z 

PROVIDER: S-EPMC12574221 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature