High Entropy Alloy Composition Design for Mechanical Properties

Abstract

Multi-component high-entropy alloys (HEAs) are a novel class of materials exhibiting outstanding material properties that often surpassing their traditional counterparts. Despite their ubiquity, the underlying microstructure-property relationships in HEAs remain elusive. This chapter addresses this gap by exploring the application of cutting-edge machine learning tools to establish robust connections between HEAs’ chemical composition, microstructure, and mechanical response. The survey begins by discussing the current state of micro-structural characterization techniques in HEAs, giving insights into their complex underlying microstructure. The development of ML force fields for HEAs based on ab initio datasets is then highlighted, addressing challenges posed by the expansive composition space associated with HEAs. The chapter further outlines machine learning-assisted composition search strategies for HEAs with specific functional properties, offering a systematic and efficient approach to explore material properties. Overall, the present overview demonstrates the potential of machine learning in unraveling the intricate nature of HEAs and accelerating their tailored design for diverse applications.