Application of machine learning to two-dimensional Dzyaloshinskii-Moriya ferromagnets

Vinit Kumar Singh and Jung Hoon Han
Phys. Rev. B 99, 174426 – Published 23 May 2019

Abstract

Principles of machine learning are applied to spin configurations generated by Monte Carlo method on Dzyaloshinskii-Moriya ferromagnetic models hosting the skyrmion phase in two dimensions. Successful feature predictions regarding the average spin chirality, magnetization, as well as magnetic field and temperature, were possible with the machine-learning architecture consisting of convolutional and dense neural network layers. Algorithms trained solely on the xy or z component of the local magnetization were as effective as the one trained on the full xyz component of the input spin configuration in predicting various features. The predictive capacity of the algorithm extended beyond those configurations generated by the model used to make the training configurations, but also those generated by models plagued with disorder. A “scaling procedure” for working with data generated at various length scales is developed and proven to work in a manner analogous to the real-space renormalization process.

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  • Received 14 June 2018
  • Revised 27 March 2019

DOI:https://doi.org/10.1103/PhysRevB.99.174426

©2019 American Physical Society

Physics Subject Headings (PhySH)

Quantum Information, Science & TechnologyCondensed Matter, Materials & Applied Physics

Authors & Affiliations

Vinit Kumar Singh1,* and Jung Hoon Han2,†

  • 1Department of Physics, Indian Institute of Technology, Kharagpur 721302, India
  • 2Department of Physics, Sungkyunkwan University, Suwon 16419, Korea

  • *vinitsingh911@gmail.com
  • hanjemme@gmail.com

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Issue

Vol. 99, Iss. 17 — 1 May 2019

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