A data-driven approach to predict the saturation magnetization for magnetic 14:2:1 phases from chemical composition

Abstract

14:2:1 phases enable permanent magnets with excellent magnetic properties. From an application viewpoint, saturation polarization, Curie temperature, and anisotropy constant are important parameters for the magnetic 14:2:1 phases. Novel chemical compositions that represent new 14:2:1 phases require especially maximum saturation magnetization values at application-specific operating temperatures to provide maximum values for the remanence and the maximum energy density in permanent magnets. Therefore, accurate knowledge of the saturation magnetization Ms is important. Ms gets affected by chemical composition in a twofold way, with chemical composition significantly influencing both magnetic moments and crystal structure parameters. Therefore, for magnetic 14:2:1 phases, we have developed a regression model with the aim to predict the saturation magnetization in [µB/f.u.] at room temperature directly from the chemical composition as input features. The dataset for the training and testing of the model is very diverse, with literature data of 143 unique phases and 55 entries of repeated phases belonging to the ternary, quaternary, quinary, and senary alloy systems. Substitutionally dissolved elements are heavy and light rare earth elements, transition metals, and additional elements. The trained model is a voting regressor model with different weights assigned to four base regressors and has generalized well, resulting in a low mean absolute error of 0.8 [µB/f.u.] on the unseen test set of 52 phases. This paper could serve as the basis for developing novel magnetic 14:2:1 phases from chemical composition.