Bayesian Optimization of Grain-Boundary Segregation in High-Entropy Alloys

The concurrent segregation of multiple solute elements at grain boundaries (GBs), also known as co-segregation, is a pervasive interfacial behavior that governs microstructural evolution and influences many properties of high-entropy alloys (HEAs). However, accurately predicting co-segregation behavior in HEAs is a challenging task due to the vast compositional space and complex interactions among multiple solute elements. In this paper, we developed a scalarization-based Bayesian optimization (SBO) framework integrated with high-throughput atomistic simulations to efficiently explore and optimize the large compositional space of CrMnFeCoNi HEAs for targeted co-segregation behavior and other desirable interfacial properties. Specifically, Thompson sampling is adopted to explore the input compositional space and identify HEA candidates representing two extremes: the strongest and weakest co-segregation of Cr and Mn at CrMnFeCoNi GBs. These SBO-predicted segregation extremes are subsequently validated by hybrid molecular dynamics/Monte Carlo simulations and first-principles calculations. Furthermore, electronic structure calculations demonstrate that the co-segregation of Cr and Mn can be ascribed to the hybridization of their d valence electrons promoted by the presence of Fe. While this SBO framework focuses on segregation behavior, it can be easily extended to optimize a wide range of interfacial properties in multicomponent systems. This study establishes a new paradigm for designing advanced HEAs through interfacial property optimization.