Machine learning-assisted design of high-entropy alloys for optimal strength and ductility

Abstract

High Entropy Alloys (HEAs) are a novel class of multi-component alloys with compositional flexibility, presenting a promising alternative to traditional alloys. This research aims to enhance the strength of HEAs while achieving adequate ductility, a challenging objective due to their conflicting nature. We applied evolutionary data-driven models and Bi-Objective Genetic Programming (BioGP) with genetic algorithms (GA) to accurately predict and optimize yield strength and ductility. The predictions were validated through experimental methods, including casting by vacuum arc melting and comprehensive mechanical and microstructural characterization. Our integrated approach successfully developed an HEA exhibiting a strength of 1795 ± 21 MPa and a ductility of 31.45 %. This study highlights the effectiveness of combining data-driven models with experimental validation to advance the development of high-performance materials.