Machine learning for high-entropy alloys: Progress, challenges and opportunities

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 theo-retical modeling that needs accurate atomic interactions for atomistic simulations and (ii) con-structing 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 re-view 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 ma-terials design.

Automated summary

This review discusses the use of machine learning in overcoming challenges in the field of high-entropy alloys (HEAs). It introduces the basics of machine learning algorithms and application scenarios, summarizes the latest machine learning models for describing atomic interactions, thermodynamic and mechanical properties, and provides examples of machine-learned phase-formation rules and order parameters. The article also discusses the remaining challenges and future research directions, including uncertainty quantification and machine learning-guided inverse materials design.