Domain knowledge aided machine learning method for properties prediction of soft magnetic metallic glasses
(1. School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, China;
2. Department of Materials Science and Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China;
3. State Key Laboratory of Advanced Materials for Smart Sensing, GRINM Group Co., Ltd., Beijing 100088, China)
2. Department of Materials Science and Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, China;
3. State Key Laboratory of Advanced Materials for Smart Sensing, GRINM Group Co., Ltd., Beijing 100088, China)
Abstract: A machine learning (ML) method aided by domain knowledge was proposed to predict saturated magnetization (Bs) and critical diameter (Dmax) of soft magnetic metallic glasses (MGs). Two datasets were established based on published experimental works about soft magnetic MGs. A general feature space was proposed and proven to be adaptive for ML model training for different prediction tasks. It was demonstrated that the predictive performance of ML models was better than that of traditional knowledge-based estimation methods. In addition, domain knowledge aided feature design can greatly reduce the number of features without significantly reducing the prediction accuracy. Finally, the binary classification of Dmax of soft magnetic MGs was studied.
Key words: metallic glass; soft magnetism; glass forming ability; machine learning; material descriptor