Machine learning-assisted high-entropy alloy discovery: a perspective

High-entropy alloys (HEAs) have attracted extensive attention due to their exceptional mechanical, physical, and chemical properties, making them promising candidates for extreme environments. Understanding the complex structure–property relationships in these multi-principal element systems is crucial for discovering and designing high-performance HEAs. However, their vast compositional space and high-dimensional chemical complexity pose major challenges to traditional trial-and-error design. Machine learning (ML) offers a transformative strategy to overcome these barriers by enabling data-driven exploration. This perspective first reviews the critical challenges currently limiting HEA development, then summarizes recent ML breakthroughs in phase formation prediction, multi-objective optimization, and accelerated atomistic simulations. Finally, we discuss ongoing challenges and propose future opportunities for integrating ML with experimental and computational methods to create more interpretable, data-efficient, and autonomous ML-driven HEA design frameworks.