Machine learning of phases and mechanical properties in complex concentrated alloys

Abstract

The mechanical properties of complex concentrated alloys (CCAs) depend on their formed phases and corresponding microstructures. The data-driven prediction of the phase formation and associated mechanical properties is essential to discovering novel CCAs. The present work collects 557 samples of various chemical compositions, comprising 61 amorphous, 167 single-phase crystalline, and 329 multi-phases crystalline CCAs. Three classification models are developed with high accuracies to category and understand the formed phases of CCAs. Also, two regression models are constructed to predict the hardness and ultimate tensile strength of CCAs, and the correlation coefficient of the random forest regression model is greater than 0.9 for both of two targeted properties. Furthermore, the Shapley additive explanation (SHAP) values are calculated, and accordingly four most important features are identified. A significant finding in the SHAP values is that there exists a critical value in each of the top four features, which provides an easy and fast assessment in the design of improved mechanical properties of CCAs. The present work demonstrates the great potential of machine learning in the design of advanced CCAs.