Machine Learning for High-entropy Alloys: Progress, Challenges and Opportunities

TL;DR

This paper reviews how machine learning advances the understanding and design of high-entropy alloys by modeling atomic interactions, phase predictions, and macro-scale properties, addressing high-dimensional complexity and enabling accelerated discovery.

Contribution

It provides a comprehensive overview of ML applications in HEAs, highlighting recent progress, key challenges, and future research directions in modeling and materials design.

Findings

ML models effectively describe atomic interactions in HEAs

ML enables accurate phase prediction and defect simulations

Machine learning accelerates high-throughput alloy screening

Abstract

High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional mechanical properties and the vast compositional space for new HEAs. However, understanding their novel physical mechanisms and then using these mechanisms to design new HEAs are confronted with their high-dimensional chemical complexity, which presents unique challenges to (i) the theoretical modeling that needs accurate atomic interactions for atomistic simulations and (ii) constructing reliable macro-scale models for high-throughput screening of vast amounts of candidate alloys. Machine learning (ML) sheds light on these problems with its capability to represent extremely complex relations. This review highlights the success and promising future of utilizing ML to overcome these challenges. We first introduce the basics of ML algorithms and application scenarios. We then summarize the state-of-the-art ML models describing atomic interactions and atomistic simulations of thermodynamic and mechanical properties. Special attention is paid to phase predictions, planar-defect calculations, and plastic deformation simulations. Next, we review ML models for macro-scale properties, such as lattice structures, phase formations, and mechanical properties. Examples of machine-learned phase-formation rules and order parameters are used to illustrate the workflow. Finally, we discuss the remaining challenges and present an outlook of research directions, including uncertainty quantification and ML-guided inverse materials design.