Composition design of high yield strength points in single-phase Co–Cr–Fe–Ni–Mo multi-principal element alloys system based on electronegativity, thermodynamic calculations, and machine learning

A method which combines electronegativity difference, CALculation of PHAse Diagrams (CALPHAD) and machine learning has been proposed to efficiently screen the high yield strength regions in Co–Cr–Fe–Ni–Mo multi-component phase diagram. First, the single-phase region at a certain annealing temperature is obtained by combining CALPHAD method and machine learning, to avoid the formation of brittle phases. Then high yield strength points in the single-phase region are selected by electronegativity difference. The yield strength and plastic deformation behavior of the designed Co14Cr30Ni50Mo6 alloy are measured to evaluate the proposed method. The validation experiments indicate this method is effective to predict high yield strength points in the whole compositional space. Meanwhile, the interactions between the high density of shear bands and dislocations contribute to the high ductility and good work hardening ability of Co14Cr30Ni50Mo6 alloy. The method is helpful and instructive to property-oriented compositional design for multi-principal element alloys.