Improving the performance of machine learning model predicting phase and crystal structure of high entropy alloys by the synthetic minority oversampling technique

Abstract

It is critical to select the suitable integration of elements that has an influence on phase formation when selecting the proper High Entropy Alloys (HEAs) with desired qualities. Machine Learning (ML) models were used to predict the phase and crystal structure of HEAs in this study. Trained on extensive experimental datasets, the set of input characteristics includes Atomic size difference, Mixing Enthalpy, Mixing Entropy, Valence Electron concentration, Electronegativity difference, Ω and Average Melting Temperature. The phase is divided into three categories: Solid solution (S), Dual phase (D), and Intermetallic (Im). Crystal structure classifications include BCC, FCC, and FCC+BCC. For phase and crystal structure prediction, five ML algorithms were used: Decision Tree (DT), K Nearest Neighbour (KNN), Random Forest (RF), Gradient Boosting (GB) and eXtreme Gradient Boosting (XG Boosting), The classification of the HEAs phase has been predicted with great accuracy using an XG Boosting (80 %) and Random Forest exhibits the highest accuracy (94 %) for crystal structure prediction when model is trained on an experimental dataset. Even though the ML model predicts the classes with a better degree of accuracy (>80 %), the F1 value of each class varies more dramatically. This issue has arisen as a result of an imbalance in the dataset's class distribution. To boost performance, this work uses an ML model in conjunction with the Synthetic Minority Oversampling Technique (SMOTE). According to this approach, XG boosting identified the crystal structure with 93 % accuracy and predicted the phase of HEAs with 84 % accuracy. Additionally, it has been noted that each class's F1 score improved and the difference between them was reduced after using the SMOTE technique. It is evident that the SMOTE approach has improved the ML model's ability to differentiate between the various classes.