Machine Learning Application to Two-Dimensional Dzyaloshinskii-Moriya Ferromagnets

Abstract

Principles of machine learning are applied to models that support skyrmion phases in two dimensions. Successful feature predictions on various phases of the skyrmion model were possible with several layers of convolutional neural network inserted together with several neural network layers. A new training scheme based on features of the input configuration such as magnetization and spin chirality is introduced. It proved possible to further train external parameters such as the magnetic field and temperature and make reliable predictions on them. Algorithms trained on only the z-component or the xy-components of the spin gave equally reliable predictions. The predictive capacity of the algorithm extended to configurations not generated by the original model, but related ones. A procedure for integrating the machine learning algorithm into the interpretation of experimental data is given.

arXiv commentpublished version

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• Machine learning for structure-property relationships: Scalability and limitations
• Robust cycloid crossover driven by anisotropy in the skyrmion host GaV${}_4$S${}_8$
• Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks
• Quantifying the magnetic interactions governing chiral spin textures using deep neural networks
• Energy Transfer in Random-Matrix ensembles of Floquet Hamiltonians
• Deep learning methods for Hamiltonian parameter estimation and magnetic domain image generation in twisted van der Waals magnets
• Machine-Learning Recognition of Dzyaloshinskii-Moriya Interaction from Magnetometry
• Identifying magnetic antiskyrmions while they form with convolutional neural networks