Machine learning guided phase and hardness controlled AlCoCrCuFeNi high-entropy alloy design

Abstract

Machine learning(ML) assisted high-entropy alloys(HEA) design is dedicated to solving the problem of long period and high cost of designing by traditional trial and error experimental methods. The classic AlCoCrCuFeNi HEA was taken as the research object. The phase structure prediction model and hardness prediction model were established respectively. The support vector machine(SVM) models have the best training performance in both tasks. The best phase classification accuracy is 0.944, and the root mean square error(RMSE) of the hardness regression model is 56.065HV. The two ML models are further connected in series. Based on the upper and lower limits of the data set, the high-throughput prediction and selection of phases and hardness of AlCoCrCuFeNi HEA are carried out simultaneously, thus realizing the efficient composition design of the new alloy. The experimental results show that the five new alloys are in accord with the predicted results, and the RMSE is 12.58HV. It shows that the ML models can predict the phase and hardness of HEA efficiently and accurately.

Keywords

• machine learning
• high entropy alloy
• phase prediction
• hardness prediction
• composition design