Interpretable prediction of remanence in sintered NdFeB through machine learning strategy

Abstract

The effects of composition on magnetic properties of sintered NdFeB are generally revealed by trial-and-error method, which is actually time-consuming and resource-intensive. It has been demonstrated that data-driven machine learning (ML) strategy has a great potential in accelerating processes of material research. In this study, accurate prediction of remanence in sintered NdFeB magnets has been achieved by a high-quality and few-shot dataset. We used a set of physical and chemical properties of element as inputs to build ML models based on element classification knowledge. Specifically, according to the position of elements, we divided them into three categories and then calculated the average properties of three positions separately. The strong generalization ability of best-performing ML model was further verified in nineteen unknown samples with only 0.724 % error. In addition, the symbolic regressor (SR) and SHapley Additive exPlanation (SHAP) algorithm were employed to explain the model results from physical perspective. In short, a high-efficiency and feasible ML strategy was developed for predicting remanence of sintered NdFeB magnets. We believe that this work will show great prospects in the composition design and performance screening of sintered NdFeB magnets.