Improving phase prediction accuracy for high entropy alloys with Machine learning

Abstract High entropy alloys (HEAs) can possess improved physicochemical properties due to the formation of solid solution (SS), intermetallic (IM) and/or mixed (SS + MM) phases with unique microstructures. For this reason, phase prediction in HEAs (SS, IM, or SS + IM) is the preliminary step in alloy design. In this research, main-stream data vetting techniques and Machine Learning (ML) algorithms have been used to classify and predict the phases in HEAs with the goal of significantly improving phase prediction accuracy. The data used for this work collected from literature are first refined manually by removing redundant and repeated entries. Six features, VEC, δ, Δχ, ΔS, ΔH, and Tmelt, are calculated for each data entry. In quantifying the feature significance, different selection techniques were used. With Feature Importance and Correlation based feature selection method (CFS), entropy change is found to be the least important feature. Results from Principal Component Analysis (PCA) and Correlation Analysis suggest that there is no need to drop any feature from the dataset. Nine different datasets were then created with data sampling and fed to four different ML classifiers, where all these classifiers were optimized by tuning the hyperparameters. K-Nearest Neighbor and Random Forest Classifier on the “Oversampled-PCA-6” dataset, with test accuracy of 92.31% and 91.21% respectively, stood as the efficient classifiers for classification of HEAs phases. With above 90% test accuracies, the results from SVM and MLP classifiers were also satisfactory.