Deep learning-based prediction of saturation magnetic flux density in Fe-based metallic glasses via transfer learning

Abstract

Transfer learning (TL) is a widely recognized machine learning model that leverages domain knowledge from one or more predictive tasks to enhance performance in a related task. This study demonstrated the improved prediction performance of saturation magnetic flux density (Bs) of bulk metallic glasses (BMGs) with deep learning through the utilization of TL, which enabled the extension of compositional knowledge to overcome the limitations posed by scarce data. Specifically, TL was demonstrated by training a convolutional autoencoder network on full BMG datasets and transferring the acquired compositional knowledge to scarce iron (Fe)-based metallic glass datasets. Through this transfer, the broad compositional knowledge derived from the full BMG dataset is embedded in data representations, allowing its domain knowledge to be shared with smaller datasets. Here, a convolutional neural network was used as a predictor, with the prediction performance of Bs evaluated by the determination coefficient (R2 score) and root-mean-squared error (RMSE). The predictor, having received knowledge about the broad composition through TL, exhibited enhanced predictive performance of Bs on unseen BMG datasets while maintaining its prediction capability for Fe-based metallic glass alloys with R2 = 0.939 and RMSE = 0.084 T. This performance surpassed the performance of directly transferred knowledge on scarce data. These findings underscore the benefits of TL in predictive tasks with limited datasets and highlight the potential for applying this method to the development of new materials, where well-trained models can be repurposed for unlabeled material datasets with desired properties.