Magnetocaloric properties predicted by deep learning with compositional features for bulk metallic glasses

A convolutional neural network (CNN) is a widely recognized deep-learning model that performs regression or classification from domain knowledge using a kernel function that extracts specific features of the input data. This study demonstrates the prediction performance of the magnetic entropy changes (- Delta Smag) and transition temperatures (Tr) of bulk metallic glasses (BMGs) with magnetocaloric effects using a CNN regression model. Compositional descriptors were generated using the Magpie method and were used as input data. The prediction performance was evaluated using the determination coefficient (R2 score) and root mean square error (RMSE), resulting in R2 score of 0.920 and 0.917 and RMSE of 1.285 J/kgK and 45.619 K for - Delta Smag and Tr, respectively. In the case study, we further applied the CNN model to unseen datasets for extrapolated - Delta Smag and Tr prediction and compared the results with the experimental values, resulting in excellent agreement with the reported experimental results.

Automated summary

This study demonstrates the prediction performance of a CNN regression model for the magnetic entropy changes and transition temperatures of bulk metallic glasses with magnetocaloric effects. The model achieved high prediction performance, as measured by the determination coefficient and root mean square error. The results showed good agreement with experimental values and reported results.