Determinants of saturation magnetic flux density in Fe‐based metallic glasses: insights from machine‐learning models
Jie Xiong, Bowen Bai, Haoran Jiang, A. Faus‐Golfe
Abstract
Abstract Fe‐based metallic glasses have garnered significant attention due to their low coercivity force and core loss. Enhancing the saturation magnetic flux density ( B s ) of Fe‐based metallic glasses is crucial for their industry applications. This work constructed a dataset comprising 330 training data and 157 test data. The support vector regression model surpassed the tree‐based ensemble models in the test set and demonstrated comparable accuracy to the tree‐based ensemble models in the training set. Additionally, we proposed an indicator for B s based on symbolic regression. This newly proposed indicator exhibits a Pearson correlation coefficient exceeding 0.92 with B s . The present work provides a simple and accurate formula for predicting the B s of Fe‐based amorphous alloys, demonstrating the effectiveness of machine learning approaches in discovering novel soft magnetic materials.