Prediction of glass forming ability of bulk metallic glasses based on convolutional neural network

Abstract

Bulk metallic glasses (BMGs) have been widely used in different fields owing to their unique and excellent properties. In order to accelerate the development of BMGs, different feasible parameters or criteria of their glass forming ability (GFA) have been proposed. With the advent of the era of big data, machine learning (ML) methods provide novel insights into the study of BMGs. In this paper, we trained a convolutional neural network (CNN) model to investigate GFA of BMGs. A hundred alloying elements and their possible combinations were taken into account by mapping an alloying composition into a 10 × 10 feature graph. Compared with the other prediction methods of GFA in BMGs, the alloy composition is the only variable input without the requirement for various physical and chemical properties obtained through pre-experiments. The predictive ability of our proposed model is quantified by a training R2 of 0.9745 and a test R2 of 0.8137. This work suggests that ML approaches has great potential in guiding the design of new BMG materials.