Machine-Learning-Assisted Phase Prediction in High-Entropy Alloys Using Two-Step Feature Selection Strategy

Abstract

The complex compositions of high-entropy alloys (HEAs) enable a variety of phase structures like FCC single phase, BCC single phase, or duplex FCC + BCC phase. Accurate and efficient prediction of phase structure is crucial for accelerating the discovery of new components and designing HEAs with desired phase structure. In this work, five machine learning strategies were utilized to predict the phase structures of HEAs with a dataset of 296. Specifically, a two-step feature selection strategy was proposed, enabling pronounced improvement in the computational efficiency from 2047 to 12 iterations for each model while ensuring fewer input features and higher prediction accuracy. Compared with traditional valence electron concentration criterion, the prediction accuracy of collected dataset was highly improved from 0.79 to 0.98 for random forest. Furthermore, HEAs with compositions of Al_xCoCu₆Ni₆Fe₆ (x = 1, 3, 6) were developed to validate the prediction results of machine learning models, and the mechanical properties as well as corrosion resistance were investigated. It is found that the higher Al content enhances the yield strength but deteriorates corrosion resistance. The present two-step feature selection strategy provides an alternative method that is feasible for predicting the phase structure of HEAs with high efficiency and accuracy.