Interpretable machine learning for simultaneous designing martensitic transformation temperature and thermal hysteresis of high-entropy shape memory alloys

Abstract

As an excellent functional material, high-entropy shape memory alloy (HESMAs) exhibits tremendous application potential in various fields such as actuators and energy exploration due to its advantages of high strength, high hardness, and corrosion resistance. The key factors determining the application potential of HESMAs are martensitic transformation temperature and thermal hysteresis. However, the design of HESMAs is a great challenge due to the large component search space. Here, we firstly propose a workflow aiming to achieve dual objectives: predicting the martensitic transformation peak temperature (Mp) and thermal hysteresis (Thy) to achieve a rapid design for HESMAs. The key features are obtained by correlation screening and Borutashap feature screening. The Mp and Thy models are training by eXtreme Gradient Boosting Regression algorithm(XGBR) combined with Bayesian Optimization algorith. Finally, XGBR model demonstrates superior performance in predicting both Mp (Rtrain2= 0.993, Rtest2= 0.902) and Thy (Rtrain2= 0.998, Rtest2= 0.928). By applying these models, we are able to identify potential candidates for targeted HESMAs in vast search spaces, specifically, low Mp and wide Thy HESMAs,room Mp and narrow Thy HESMAs, and high Mp HESMA. In addition, we utilize the Shapley additive interpretation method to explain the model's behavior, establishing a more transparent relationship between features and Mp and Thy individually. This proposed strategy is providing valuable guidance for the subsequent targeted design of HESMAs.