A New Phase Classifier with an Optimized Feature Set in ML-Based Phase Prediction of High-Entropy Alloys

Abstract

The phases of high-entropy alloys (HEAs) are closely related to their properties. However, phase prediction bears a significant challenge due to the extensive search space and complex formation mechanisms of HEAs. This study demonstrates a precise and timely methodology for predicting alloy phases. It first developed a machine learning classifier using 145 features and a dataset with 1009 samples to differentiate the four types of alloy phases. Feature selection was performed on the feature set using an Embedded algorithm and a genetic algorithm, resulting in the selection of nine features. The Light GBM algorithm was chosen to train the machine learning model. Finally, the implementation of oversampling and cost-sensitive methods enables LightGBM to tackle the problem of insufficient accuracy in BCC+FCC phase classification. The resulting accuracy of the alloy phase prediction model, evaluated through ten-fold cross-validation, stands at 0.9544.