Machine learning assisted prediction of the Young's modulus of compositionally complex alloys.

ABSTRACT: We identify compositionally complex alloys (CCAs) that offer exceptional mechanical properties for elevated temperature applications by employing machine learning (ML) in conjunction with rapid synthesis and testing of alloys for validation to accelerate alloy design. The advantages of this approach are scalability, rapidity, and reasonably accurate predictions. ML tools were implemented to predict Young's modulus of refractory-based CCAs by employing different ML models. Our results, in conjunction with experimental validation, suggest that average valence electron concentration, the difference in atomic radius, a geometrical parameter λ and melting temperature of the alloys are the key features that determine the Young's modulus of CCAs and refractory-based CCAs. The Gradient Boosting mo

SUBMITTER: Khakurel H 

PROVIDER: S-EPMC8387451 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature