Efficient property-oriented optimization of magnetic high-entropy metallic glasses via a multi-stage design strategy

High-entropy metallic glasses (HE-MGs) have drawn much attention as promising multifunctional materials combining the excellent soft magnetic properties of traditional metallic glasses and impressive mechanical properties, thermal stability, corrosion resistance, etc., of solid solution high entropy alloys. Property optimization of HE-MGs is of great significance for promoting their engineering applications. However, various constituent elements and the high chemical complexity make the possible alloying composition space extremely massive, which is very challenging for the rational design of HE-MGs. In this work, we proposed a multi-stage optimization strategy based on machine learning (ML) to accelerate the rational design of magnetic HE-MGs with desired properties. The huge composition search space was significantly narrowed by the ML-based phase prediction model and constraints from user preferences. Utility functions based on the exploitation and exploration strategy were designed to find the global optimization solutions, i.e., alloying compositions. Experiments were conducted as concept validation, and new Fe-Co-Ni-Si-B HE-MGs with balanced saturation magnetic flux density and mixing entropy were developed.