Predicting the Curie temperature of ferromagnets using machine learning

The magnetic properties of a material are determined by a subtle balance between the various interactions at play, a fact that makes the design of new magnets a daunting task. High-throughput electronic structure theory may help to explore the vast chemical space available and offers a design tool to the experimental synthesis. This method efficiently predicts the elementary magnetic properties of a compound and its thermodynamical stability, but it is blind to information concerning the magnetic critical temperature. Here we introduce a range of machine-learning models to predict the Curie temperature, $T_\mathrm{C}$, of ferromagnets. The models are constructed by using experimental data for about 2,500 known magnets and consider the chemical composition of a compound as the only feature determining $T_\mathrm{C}$. Thus, we are able to establish a one-to-one relation between the chemical composition and the critical temperature. We show that the best model can predict $T_\mathrm{C}$'s with an accuracy of about 50K. Most importantly our model is able to extrapolate the predictions to regions of the chemical space, where only a little fraction of the data was considered for training. This is demonstrated by tracing the $T_\mathrm{C}$ of binary intermetallic alloys along their composition space and for the Al-Co-Fe ternary system.