Combining machine learning with first principles to model the Curie temperature of magnetic Heusler compounds

Accurate theoretical prediction of Curie temperature (Tc) of magnetic compositions prior to their synthesis in the laboratory is a challenging but vital task, especially for their permanent magnet application. In this work, we combine first-principles density functional theory (DFT) calculations with machine learning (ML) to predict the Tc of magnetic Heusler alloys. For this purpose, we gather the experimental Tc of 105 stable magnetic full Heusler alloys (DFT calculated net magnetic moment of 2 μB per formula unit). We employ a robust descriptor set (comprising of the elemental, compound structural, and compound magnetic ones), whereby compound descriptors are calculated from our DFT calculations. We build a regression model for the Tc using a systematic ML approach, whereby an unprecedented accuracy is attained using random forest. Furthermore, we use one of the compress sensing methods (SISSO) to perform dimensionality reduction and analyze the complex interplay of the dimensions, which curiously reveal the connection between ionization potential, radius, and melting points of atoms with the Tc.