Machine learning guided prediction of the yield strength and hardness of multi-principal element alloys

Abstract

Background: Multi-Principal Element Alloys (MPEAs) have better properties, such as yield strength, hardness, and corrosion resistance compared to conventional alloys. Compositional optimization is a challenging task to obtain desired properties of MPEAs and machine learning is a potential tool to rapidly accelerate the search and design of new materials. Methods: We have implemented different machine learning models to predict the yield strength and Vickers hardness of MPEAs at room temperature and quantify the uncertainty of the predictions. Results: Our results suggest that valence electron concentration (VEC) is the key feature dominating the yield strength and hardness of MPEAs. Our predicted yield strength and hardness values for the experimental validation set show < 15 % error for most cases with respect to the experimental values. Conclusions: Our machine learning model can serve as a useful tool to screen half a trillion MPEAs and down select promising compositions for useful applications.