Fe-based metallic glasses have garnered significant attention due to their low coercivity force and core loss.Enhancing the saturation magnetic flux density(Bs)of Fe-based metallic glasses is crucial for their industr...Fe-based metallic glasses have garnered significant attention due to their low coercivity force and core loss.Enhancing the saturation magnetic flux density(Bs)of Fe-based metallic glasses is crucial for their industry applications.This work constructed a dataset comprising330 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 Bsbased on symbolic regression.This newly proposed indicator exhibits a Pearson correlation coefficient exceeding 0.92 with Bs.The present work provides a simple and accurate formula for predicting the Bsof Fe-based amorphous alloys,demonstrating the effectiveness of machine learning approaches in discovering novel soft magnetic materials.展开更多
基金financially supported by Shanghai Pujiang Program(No.23PJ1403500)GuangDong Basic and Applied Basic Research Foundation(No.2023A1515110901)+2 种基金Shenzhen Pengcheng Peacock Project(No.NA11409004)the National Natural Science Foundation of China(Nos.U22B2064 and 51105102)and Shanghai Rising-Star Program Yangfan Project(No.23YF1411900)。
文摘Fe-based metallic glasses have garnered significant attention due to their low coercivity force and core loss.Enhancing the saturation magnetic flux density(Bs)of Fe-based metallic glasses is crucial for their industry applications.This work constructed a dataset comprising330 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 Bsbased on symbolic regression.This newly proposed indicator exhibits a Pearson correlation coefficient exceeding 0.92 with Bs.The present work provides a simple and accurate formula for predicting the Bsof Fe-based amorphous alloys,demonstrating the effectiveness of machine learning approaches in discovering novel soft magnetic materials.
