A systematic review of machine learning approaches for phase stability and mechanical property prediction in high-entropy alloys

Abstract

: High entropy alloys (HEAs) have attracted significant research interest owing to their vast compositional space and diverse achievable properties. However, exploring this extensive design space and experimentally determining phase constituents and mechanical properties are time-consuming processes. Machine learning (ML) provides a data-driven approach to identifying relationships among alloy composition, phase, microstructure