Towards high entropy alloy with enhanced strength and ductility using domain knowledge constrained active learning

Abstract

The simultaneous optimization of competing properties is a challenge of machine learning based materials design. We proposed a domain knowledge constrained active learning loop for the design of high entropy alloys with optimized strength and ductility, by narrowing down the unexplored space using the valence electron concentration criterion. The active learning loop iterated six times and one alloy with an ultimate strength of 1258 MPa and an elongation of 17.3 % was synthesized. To uncover the underlying mechanism for synergetic optimization, we characterized the phase structure and eutectic microstructure, and discussed the possible origins from the view of strain hardening and crack initiation. The proposed framework that combines domain knowledge with machine learning can facilitate the design of target materials with coordinating optimization of competing properties.

Keywords

• Active learning
• Domain knowledge
• Bayesian optimization
• High-entropy alloys
• Mechanical properties