Applications of Machine Learning in High-Entropy Alloys: Phase Prediction, Performance Optimization, and Compositional Space Exploration

The rapid advancement of machine learning (ML) has ushered in a new era for materials science, particularly in the design and understanding of high-entropy alloys (HEAs). As a class of compositionally complex materials, HEAs have greatly benefited from the predictive power and computational efficiency of ML techniques. Recent years have witnessed remarkable expansion in the scope and sophistication of ML applications to HEAs, spanning from phase formation prediction to property and microstructure modeling. These developments have significantly accelerated the discovery and optimization of novel HEA systems. This review provides a comprehensive overview of the current progress and emerging trends in applying ML to HEA research. We first discuss phase prediction methodologies, encompassing both pure ML frameworks and hybrid physics-informed models. Subsequently, we summarize advances in ML-driven prediction of HEA properties and microstructural features. Further sections highlight the role of ML in exploring vast compositional spaces, guiding the design of high-performance HEAs, and optimizing existing alloys through data-driven algorithms. Finally, the challenges and limitations of current approaches are critically examined, and future directions are proposed toward interpretable models, mechanistic understanding, and efficient exploration of the HEA design space.