Exploration of V–Cr–Fe–Co–Ni high-entropy alloys with high yield strength: A combination of machine learning and molecular dynamics simulation

Abstract

Improving the strength of Cr–Fe–Co–Ni high-entropy alloys is a key issue in expanding their applicability. Herein, a framework combining machine learning and molecular dynamics is employed to improve the yield strength of Cr–Fe–Co–Ni high-entropy alloys through vanadium addition. The results indicate that by specifying the valence electron concentration, vacancy formation energy, and mismatch in cohesive energy as input features, a support vector regression model with a radial bias function kernel displays the highest performance among numerous combinations of machine learning models and material features. According to the Shapley additive explanation, the vacancy formation energy and valence electron concentration show a negative correlation with the yield strength above 1.66 eV and 7.60, respectively, and a positive correlation otherwise. The mismatch in the cohesive energy always shows a negative correlation. By utilizing a Bayesian adaptive alloy design, V5Cr16Fe9Co35Ni35 has been identified to have the highest yield strength. The simulated tensile deformation of polycrystalline high-entropy alloys confirms the predicted trend in yield strength, and the events observed during plastic deformation are consistent with previous experimental observations. The proposed framework provides a promising prospect for accelerating the design of high-entropy alloys with reduced dependence on costly trial-and-error experiments.