Machine learning assisted design of novel refractory high entropy alloys with enhanced mechanical properties

This paper details an alloy design effort by machine learning (ML) attempting to design novel refractory high entropy alloys (RHEAs) with exceptional mechanical properties at elevated temperatures and good room temperature ductility. For this purpose, four datasets were generated by mining the data available in literature, containing room temperature strength, high temperature strength, room temperature ductility and hardness, which were trained by three different ML models, namely the support vector regression, random forest, and artificial neural network. As a result, three novel RHEA compositions were predicted, and their performances were experimentally validated. Specifically, the Ti8Nb21Zr27Ta13Mo19V12, Ti10Nb19Zr15Ta43Mo7V6, and Ti10Nb20Zr37Mo21V12 RHEAs were produced and subjected to room-temperature and high-temperature compression, and room-temperature hardness tests, which have demonstrated that especially the Ti8Nb21Zr27Ta13Mo19V12 and the Ti10Nb20Zr37Mo21V12 RHEAs exhibit both high strength at elevated temperatures and good room-temperature ductility. The current study not only contributes to the literature by presenting three novel RHEAs, but also constitutes an encouraging example of efficient alloy design by ML for demanding applications.