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arXiv · 1803.06682

Supervised learning magnetic skyrmion phases

I. A. Iakovlev·O. M. Sotnikov·V. V. Mazurenko
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Abstract

We propose and apply simple machine learning approaches for recognition and classification of complex non-collinear magnetic structures in two-dimensional materials. The first approach is based on the implementation of the single-hidden-layer neural network that only relies on the z projections of the spins. In this setup one needs a limited set of magnetic configurations to distinguish ferromag- netic, skyrmion and spin spiral phases, as well as their different combinations in transitional areas of the phase diagram. The network trained on the configurations for square-lattice Heisenberg model with Dzyaloshinskii-Moriya interaction can classify the magnetic structures obtained from Monte Carlo calculations for triangular lattice and vice versa. The second approach we apply, a minimum distance method performs a fast and cheap classification in cases when a particular configuration is to be assigned to only one magnetic phase. The methods we propose are also easy to use for analysis of the numerous experimental data collected with spin-polarized scanning tunneling microscopy and Lorentz transmission electron microscopy experiments.

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BibTeXRIS

I. A. Iakovlev, O. M. Sotnikov, V. V. Mazurenko. 2018-03-18. Supervised learning magnetic skyrmion phases. https://doi.org/10.1103/physrevb.98.174411

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