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Recently, there has been an increased interest in the application of machinelearning (ML) techniques to a variety of problems in condensed matter physics.In this regard, of particular significance is the characterization of simpleand complex phases of matter. Here, we use a ML approach to construct the fullphase diagram of a well known spin model combining ferromagnetic exchange andDzyaloshinskii-Moriya (DM) interactions where topological phases emerge. At lowtemperatures, the system is tuned from a spiral phase to a skyrmion crystal bya magnetic field. However, thermal fluctuations induce two types ofintermediate phases, bimerons and skyrmion gas, which are not as easilydetermined as spirals or skyrmion crystals. We resort to large scale MonteCarlo simulations to obtain low temperature spin configurations, and train aconvolutional neural network (CNN), taking only snapshots at specific values ofthe DM couplings, to classify between the different phases, focusing on theintermediate and intricate topological textures. We then apply the CNN tohigher temperature configurations and to other DM values, to construct adetailed magnetic field-temperature phase diagram, achieving outstandingresults. We discuss the importance of including the disordered paramagneticphases in order to get the phase boundaries, and finally, we compare ourapproach with other ML algorithms.

Machine learning techniques to construct detailed phase diagrams for skyrmion systems