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_0.189Ti_0.203V_0.203Mo_0.206Zr_0.197, Nb_0.204Ti₀₁₉V_0.207Mo_0.198Zr_0.198, Nb_0.174Ti_0.19V_0.251Mo_0.201Zr_0.181, Nb_0.242Ti_0.252To_0.001V_0.039Mo_0.209Zr_0.254, and Nb_0.164Ta_0.155Ti_0.186V_0.008W_0.153Mo_0.001Hf_0.168Zr_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.

Keywords

digital twin models / generative model / guided multi-objectives search / machine learning / refractory high-entropy alloys

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2025 The Author(s). Materials Genome Engineering Advances published by Wiley-VCH GmbH on behalf of University of Science and Technology Beijing.