Machine learning prediction of magnetic properties of Fe-based metallic glasses considering glass forming ability  

作　　者：Xin Li Guangcun Shan C.H.Shek 

机构地区：[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

出　　处：《Journal of Materials Science & Technology》2022年第8期113-120,共8页材料科学技术（英文版）

基　　金：financially supported by National Natural Science Foundation of China(No.21771017);the Fundamental Research Funds for the Central Universities。

摘　　要：Fe-based metallic glasses(MGs)have shown great commercial values due to their excellent soft magnetic properties.Magnetism prediction with consideration of glass forming ability(GFA)is of great signifi-cance for developing novel functional Fe-based MGs.However,theories or models established based on condensed matter physics exhibit limited accuracy and some exceptions.In this work,based on 618 Fe-based MGs samples collected from published works,machine learning(ML)models were well trained to predict saturated magnetization(B_(s))of Fe-based MGs.GFA was treated as a feature using the experimental data of the supercooled liquid region(△T_(x)).Three ML algorithms,namely eXtreme gradient boosting(XGBoost),artificial neural networks(ANN)and random forest(RF),were studied.Through feature selection and hyperparameter tuning,XGBoost showed the best predictive performance on the randomly split test dataset with determination coefficient(R^(2))of 0.942,mean absolute percent error(MAPE)of 5.563%,and root mean squared error(RMSE)of 0.078 T.A variety of feature importance rankings derived by XGBoost models showed that T_(x) played an important role in the predictive performance of the models.This work showed the proposed ML method can simultaneously aggregate GFA and other features in ther-modynamics,kinetics and structures to predict the magnetic properties of Fe-based MGs with excellent accuracy.

关 键 词：Metallic glasses Soft magnetic properties Glass forming ability Machine learning Non-linear regression 

分 类 号：TG139.8[一般工业技术—材料科学与工程] TP181[金属学及工艺—合金]