Prediction of the Properties of the Rare-Earth Magnets Ce2Fe17−xCoxCN : A Combined Machine-Learning and Ab Initio Study

Abstract

We employ a combination of machine learning and first-principles calculations to predict magnetic properties of rare-earth lean magnets. For this purpose, based on training set constructed out of experimental data, the machine is trained to make predictions on magnetic transition temperature (Tc), largeness of saturation magnetization ({\mu}0Ms), and nature of the magnetocrystalline anisotropy (Ku). Subsequently, the quantitative values of {\mu}0Ms and Ku of the yet-to-be synthesized compounds, screened by machine learning, are calculated by first-principles density functional theory. The applicability of the proposed technique of combined machine learning and first-principles calculations is demonstrated on 2-17-X magnets, Ce2Fe17-xCoxCN. Further to this study, we explore stability of the proposed compounds by calculating vacancy formation energy of small atom interstitials (N/C). Our study indicates a number of compounds in the proposed family, offers the possibility to become solution of cheap, and efficient permanent magnet.