  Recently, there has been an increased interest in the application of machine
learning (ML) techniques to a variety of problems in condensed matter physics.
In this regard, of particular significance is the characterization of simple
and complex phases of matter. Here, we use a ML approach to construct the full
phase diagram of a well known spin model combining ferromagnetic exchange and
Dzyaloshinskii-Moriya (DM) interactions where topological phases emerge. At low
temperatures, the system is tuned from a spiral phase to a skyrmion crystal by
a magnetic field. However, thermal fluctuations induce two types of
intermediate phases, bimerons and skyrmion gas, which are not as easily
determined as spirals or skyrmion crystals. We resort to large scale Monte
Carlo simulations to obtain low temperature spin configurations, and train a
convolutional neural network (CNN), taking only snapshots at specific values of
the DM couplings, to classify between the different phases, focusing on the
intermediate and intricate topological textures. We then apply the CNN to
higher temperature configurations and to other DM values, to construct a
detailed magnetic field-temperature phase diagram, achieving outstanding
results. We discuss the importance of including the disordered paramagnetic
phases in order to get the phase boundaries, and finally, we compare our
approach with other ML algorithms.
