Data-driven glass-forming ability criterion for bulk amorphous metals with data augmentation

A data augmentation technique is employed in the current work on a training dataset of 610 bulk metallic glasses (BMGs), which are randomly selected from 762 collected data. An ensemble machine learning (ML) model is developed on augmented training dataset and tested by the rest 152 data. The result shows that ML model has the ability to predict the maximal diameter D-max of BMGs more accurate than all reported ML models. In addition, the novel ML model gives the glass forming ability (GFA) rules: average atomic radius ranging from 140 pm to 165 pm, the value of TgTx/(T-l-T-g)(T-l-T-x) being higher than 2.5, the entropy of mixing being higher than 10 J/K/mol, and the enthalpy of mixing ranging from -32 kJ/mol to -26 kJ/mol. ML model is interpretative, thereby deepening the understanding of GFA. (C) 2022 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.

Automated summary

A data augmentation technique is used to process a training dataset and develop an ensemble machine learning model for predicting the maximal diameter of bulk metallic glasses (BMGs). The model outperforms all reported models and provides insights into the glass forming ability.