  Chiral magnets have attracted a large amount of research interest in recent
years because they support a variety of topological defects, such as skyrmions
and bimerons, and allow for their observation and manipulation through several
techniques. They also have a wide range of applications in the field of
spintronics, particularly in developing new technologies for memory storage
devices. However, the vast amount of data generated in these experimental and
theoretical studies requires adequate tools, among which machine learning is
crucial. We use a Convolutional Neural Network (CNN) to identify the relevant
features in the thermodynamical phases of chiral magnets, including
(anti-)skyrmions, bimerons, and helical and ferromagnetic states. We use a
flexible multi-label classification framework that can correctly classify
states in which different features and phases are mixed. We then train the CNN
to predict the features of the final state from snapshots of intermediate
states of a lattice Monte Carlo simulation. The trained model allows
identifying the different phases reliably and early in the formation process.
Thus, the CNN can significantly speed up the large-scale simulations for 3D
materials that have been the bottleneck for quantitative studies so far.
Moreover, this approach can be applied to the identification of mixed states
and emerging features in real-world images of chiral magnets.
