Improved phase prediction of high-entropy alloys assisted by imbalance learning

Predicting phase formation is crucial in novel high-entropy alloys (HEAs) design. Herein, machine learning and imbalance learning algorithms were combined together to improve the phase prediction of HEAs. In this work, an extensive database by collecting experimental data from published literature was constructed, and the key features affecting the phase formation of HEAs were filtered out by performing a three-step feature selection process. Then, extreme gradient boosting (XGB) models were constructed to categorize phase structures of HEAs with high accuracies. Moreover, the Synthetic Minority Oversampling TEchnique (SMOTE) algorithm was employed for data oversampling to address the data imbalance issue. It was found that imbalanced learning significantly improves the phase prediction, particularly for the minority class, without costing the overall prediction accuracy. Finally, a machine learning-base protocol was proposed to integrate established models to classify the phase formation of HEAs into seven phase labels, and its generalization ability was verified. The present work provides a practical approach in predicting phase structures of HEAs and enhances the efficiency in developing advanced HEAs.