Machine learning‐based research of new refractory high‐entropy alloys using guided multiobjectives search strategy

Abstract The development of novel refractory high‐entropy alloys (RHEAs) holds significant promise for advanced applications due to their exceptional properties. However, identifying optimal compositions of RHEAs within the vast alloy design space to meet specific property requirements remains a formidable challenge. In this study, we present an integrated machine learning (ML) framework to address this challenge, combining predictive models for material properties, a fingerprint map of composition distribution, a guided multiobjective search strategy, and a particle swarm optimizer to enable targeted exploration of promising RHEAs compositions. Using this approach, we successfully discovered several new RHEAs with outstanding mechanical performance, including Nb 0.189 Ti 0.203 V 0.203 Mo 0.206 Zr 0.197 , Nb 0.204 Ti 019 V 0.207 Mo 0.198 Zr 0.198 , Nb 0.174 Ti 0.19 V 0.251 Mo 0.201 Zr 0.181 , Nb 0.242 Ti 0.252 To 0.001 V 0.039 Mo 0.209 Zr 0.254 , and Nb 0.164 Ta 0.155 Ti 0.186 V 0.008 W 0.153 Mo 0.001 Hf 0.168 Zr 0.16 . These alloys exhibit remarkable yield strengths ranging from 1580 to 1740 MPa and fracture strains between 23% and 27%. The integrated ML models make it possible to rapidly optimize multiple properties during other materials designing, thus overcoming the common problems of limited data and a vast composition space in complex materials systems, paving the way for efficient design of advanced materials tailored to diverse application requirements.