Supervised learning magnetic skyrmion phases

TL;DR

This paper introduces simple machine learning methods, including neural networks and minimum distance classification, to identify and distinguish complex magnetic phases like skyrmions in two-dimensional materials, aiding experimental and theoretical analysis.

Contribution

It presents novel, easy-to-implement machine learning approaches for classifying magnetic phases, including transferability across different lattice models.

Findings

Neural network accurately classifies magnetic phases from limited data.

Minimum distance method provides fast, reliable phase assignment.

Methods are applicable to experimental data analysis.

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.