Convolutional neural network guided prediction of saturation magnetic flux density of Fe-based metallic glasses

Abstract

Fe-based metallic glasses (FeMGs) have recently garnered considerable research attention due to their unique characteristics, such as low core loss and high saturation magnetic flux density (Bs). They also have promising applications in modern electrical machines, such as inductors, motors, and transformers. For FeMGs with high Bs, a trial-and-error approach using various metalloid elements as constituents has been intensively carried out to consider satisfactory thermal stability. However, this approach is time-consuming and expensive because it requires a huge compositional space and results in a lack of understanding of metalloid effects on magnetic properties and thermal stability. In this study, we established a deep learning model using a convolutional neural network (CNN) based only on compositional descriptors of FeMG constituent elements to predict Bs as the representative magnetic property. In addition, an artificial neural network was applied with the same compositional descriptors for comparison, resulting in a lower prediction performance than that of the CNN model. The CNN model demonstrated high-performance metrics that included a determination coefficient (R2) of 0.960 and a low root-mean-square error of 0.067 T, indicating that the material composition-to-property map is well-defined. We further applied the CNN model to unseen datasets for Bs prediction and compared it with results reported in the existing literature. The predicted Bs values are in excellent agreement with the reported experimental results.