Multi-objective optimization of multi-principal element alloys via high-throughput simulation and active learning

It is a great challenge to optimize the composition and improve the multi-properties of multi-principal element alloys (MPEAs) by traditional trial-and-error method. Here, an active learning strategy is proposed to design the multi-objective properties of MPEAs based on molecular dynamics (MD) simulation, machine learning (ML) models and multi-objective optimization algorithms. A database is established via a tensile test of 120 FeCrNiCoMn single-crystal samples by MD simulation. Six ML models are employed to predict the yield strength and Young's modulus based on domain knowledge, among which the support vector machine (SVM) model shows the better predictive performance, with low mean absolute error of 0.12 and 0.61 for cross validation, respectively. Then, by combining the SVM model and three multi-objective optimization algorithms, optimal alloy compositions are rapidly searched in a virtual composition space containing 885681 alloys. After four optimizationtesting iterations, Co35Cr30Ni35 is synthesized with simultaneous high yield strength and Young's modulus, which are 2.9 and 1.6 times as equiatomic FeCrNiCoMn alloy, respectively.