Machine learning prediction of glass-forming ability in bulk metallic glasses

Abstract

The critical casting diameter (Dmax) quantitatively represents glass-forming ability (GFA) of bulk metallic glasses (BMGs). The present work constructed a dataset of two subsets, L-GFA subset of 376 BMGs with 1 mm ≤Dmax < 5 mm and G-GFA subset of 319 BMGs with Dmax ≥ 5 mm. The sequential backward selector and exhaustive feature selector are introduced to select key features. The trained XGBoost classifier with four selected features is able to successfully classify the L-GFA and G-GFA BMGs. Furthermore, the trained XGBoost regression model with another four selected features predicts the Dmax of G-GFA samples with a cross-validated correlation coefficient of 0.8012. The correlation between features and Dmax will provide the guidance in the design and discovery of novel BMGs.