Machine learning for the search for topological spin textures

G.V. Paradezhenko^†^a, A.A. Pervishko^‡^a, b, D.I. Yudin^§^a, b
^aSkolkovo Institute of Science and Technology, Bolshoy Boulevard 30, bld. 1, Moscow, 121205, Russian Federation
^bFar Eastern Federal University, Sukhanova str. 8, Vladivostok, 690950, Russian Federation

We present an alternative method for numerical modeling of topological magnetic textures using a neural network algorithm. We discuss a model of localized spins where topological magnetic textures are stabilized due to a delicate interplay between the symmetric exchange interaction, and the antisymmetric interaction caused by exchange—relativistic effects, as well as a model of an itinerant magnet where noncoplanar spin configurations emerge when taking multispin interactions into account. The viability of the proposed method is illustrated with the formation of lattices of skyrmions and antiskyrmions, magnetic hedgehogs, and skyrmion tubes in two-dimensional and three-dimensional magnetic systems.

Correspondence should be addressed to
Keywords: magnets, machine learning, spin—orbit coupling, Dzyaloshinskii—Moriya interaction, multispin interaction, skyrmions, antiskyrmions, magnetic hedgehogs
PACS: 07.05.Mh, 75.10.−b, 75.30.−m, 75.40.Cx (all)
DOI: 10.3367/UFNe.2022.12.039303
URL: https://ufn.ru/en/articles/2023/11/g/
Web of Science

001131650500006
Scopus

2-s2.0-85182587213
ADS NASA

2023PhyU...66.1164P
Citation: Paradezhenko G V, Pervishko A A, Yudin D I "Machine learning for the search for topological spin textures" Phys. Usp. 66 1164–1173 (2023)

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Received: 19th, October 2022, revised: 24th, November 2022, accepted: 21st, December 2022

Оригинал: Парадеженко Г В, Первишко А А, Юдин Д И «Машинное обучение для поиска топологических спиновых структур» УФН 193 1237–1247 (2023); DOI: 10.3367/UFNr.2022.12.039303

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