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

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

Automated summary

This study successfully classified and predicted different levels of GFA in BMG materials using feature selection and machine learning algorithms, providing guidance for novel material design.