Machine learning-based Curie temperature prediction for magnetic 14:2:1 phases

Abstract

The TM14RE2B-based phases (TM = transition metal, RE = rare earth metal; hereafter called 14:2:1) enable permanent magnets with outstanding magnetic properties. Novel chemical compositions that represent new 14:2:1 phases necessitate that they do not demagnetize at application-specific operating temperatures. Therefore, an accurate knowledge of the Curie temperature (Tc) is important. For magnetic 14:2:1 phases, we present a machine learning model that predicts Tc by using merely chemical compositional features. Hyperparameter tuning on bagging and boosting models, as well as averaging predictions from individual models using the voting regressor, enables a low mean-absolute-error of 16 K on an unseen test set. The training set and a test set have been constructed by randomly splitting, in an 80:20 ratio, of a database that contains 449 phases (270 compositionally unique) mapped with their Tc, taken from distinct publications. The model correctly identifies the relative importance of key substitutional elements that influence Tc, especially in an Fe base such as Co, Mn, and Al. This paper is expected to serve as a basis for accurate Curie temperature predictions in the sought-after 14:2:1 permanent magnet family, particularly for transition metal substitution of within 20% in an Fe or Co base.