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...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.展开更多
基金support by the DOE Award DE-SC0025431The research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award BES-ERCAP0031213This work was also supported by a user project at the Center for Nanophase Materials Sciences (CNMS), a US DOE Office of Science User Facility, operated at Oak Ridge National Laboratory. Computations used resources of the National Energy Research Scientific Computing Center (NERSC), a US DOE Office of Science User Facility using NERSC award BES-ERCAP0027465 and ERCAP0031261.
文摘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.
