  Magnetic materials are crucial components of many technologies that could
drive the ecological transition, including electric motors, wind turbine
generators and magnetic refrigeration systems. Discovering materials with large
magnetic moments is therefore an increasing priority. Here, using
state-of-the-art machine learning methods, we scan the Inorganic Crystal
Structure Database (ICSD) of hundreds of thousands of existing materials to
find those that are ferromagnetic and have large magnetic moments. Crystal
graph convolutional neural networks (CGCNN), materials graph network (MEGNet)
and random forests are trained on the Materials Project database that contains
the results of high-throughput DFT predictions. For random forests, we use a
stochastic method to select nearly one hundred relevant descriptors based on
chemical composition and crystal structure. This gives results that are
comparable to those of neural networks. The comparison between these different
machine learning approaches gives an estimate of the errors for our predictions
on the ICSD database. Validating our final predictions by comparisons with
available experimental data, we found 15 materials that are likely to have
large magnetic moments and have not been yet studied experimentally.
