Glass forming ability prediction of bulk metallic glasses based on fused strategy

Abstract

In order to improve the prediction accuracy of random forest (RF), k-nearest neighbor (KNN), gradient boosted decision trees (GBDT) and extreme gradient boosting (XGBoost) models, a fused strategy was proposed for predicting the glass forming ability (GFA) of bulk metallic glasses (BMGs). Feature vectors were extracted using a trained convolutional neural network (CNN), and alloy composition information was the only variable input without requiring various physical and chemical properties acquired from experiments. Besides, the hyperparameters of RF, KNN, GBDT and XGBoost models were optimized by grid search method and k-fold cross validation. The obtained results show that the accuracy of CNN–RF, CNN–KNN, CNN–GBDT and CNN–XGBoost fused models proposed in this work in predicting GFA is higher than that of the four machine learning models mentioned above (i.e., RF, KNN, GBDT and XGBoost models), implying that the trained CNN could extract feature more effectively than manual feature construction. Furthermore, compared with previously reported machine learning models and GFA criteria, the proposed fused models could predict the GFA of BMG more accurately.