Machine learning modeling of the Curie temperature for ferromagnetic intermetallics

Magnetic materials play an essential role in green energy and informatics applications, such as efficient energy harvesting and conversion and low energy cost spintronic devices.Currently the key challenge is how to optimize the performance of existing systems and to design novel materials for broader applications. In this talk, a random forest model is trained to classify ferromagnetic and antiferromagnetic orderings and to predict the transition temperature (T_C) of the ferromagnets, using 2805 known intermetallic compounds. The resulting accuracy is 86% for classification and 92% for regression (with a mean absolute error of 58K), comparing favourably with first-principles methods. We apply these models to 5183 intermetallic compounds found in the Materials Project database, predicting their magnetic ordering and T_C. This enables us to make reliable predictions, particularly by combing high throughput and machine learning methods, paving the way to accelerate the discovery of novel magnetic compounds for technological applications.