Thermodynamically-guided machine learning modelling for predicting the glass-forming ability of bulk metallic glasses.

ABSTRACT: Glass-forming ability (GFA) of bulk metallic glasses (BMGs) is a determinant parameter which has been significantly studied. GFA improvements could be achieved through trial-and-error experiments, as a tedious work, or by using developed predicting tools. Machine-Learning (ML) has been used as a promising method to predict the properties of BMGs by removing the barriers in the way of its alloy design. This article aims to develop a ML-based method for predicting the maximum critical diameter (D_max) of BMGs as a factor of their glass-forming ability. The main result is that the random forest method can be used as a sustainable model (R² = 92%) for predicting glass-forming ability. Also, adding characteristic temperatures to the model will increase the accuracy and efficiency of the developed model. Comparing the measured and predicted values of D_max for a set of newly developed BMGs indicated that the model is reliable and can be truly used for predicting the GFA of BMGs.

SUBMITTER: Ghorbani A 

PROVIDER: S-EPMC9273633 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature