Magnetic performance oriented composition design of Sm-Co based alloys by machine learning and experimental studies

Abstract

Due to the complex crystal structures and interatomic interactions, the prediction of magnetic properties and effective composition design of rare earth permanent magnets are quite difficult. As the most promising permanent magnets for high-temperature applications, Sm-Co alloys have been developed for several decades by intuition, experience and trial-and-error methods. In this work, rapid and accurate prediction of saturation magnetization of Sm-Co alloys was realized by machine learning integrated with selection of characteristics of constituent elements, such as pseudopotential core radius, heat of fusion, boiling point, valence electron number and covalent radius. Based on the data-driven strategy and the proposed criteria for elements selection, new-type Sm-Co based alloys with excellent comprehensive magnetic performance were prepared. The methods of feature construction and optimal multistep feature selection in machine learning loops developed in this study are applicable for properties prediction and composition design of a series of multicomponent alloys.