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Determination of the Dzyaloshinskii-Moriya interaction using pattern recognition and machine learning

Physics

Determination of the Dzyaloshinskii-Moriya interaction using pattern recognition and machine learning

M. Kawaguchi, K. Tanabe, et al.

This groundbreaking research by Masashi Kawaguchi and team showcases the innovative use of machine learning to extract crucial material parameters from a single magnetic domain image. Their convolutional neural network effectively estimates the Dzyaloshinskii-Moriya interaction and magnetic anisotropy, paving the way for advancements in magnetic memory technologies.

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Playback language: English
Abstract
This paper demonstrates the use of machine learning to extract material parameters from a single magnetic domain image. Specifically, it focuses on estimating the Dzyaloshinskii-Moriya (DM) interaction and magnetic anisotropy distribution in thin-film heterostructures, crucial for next-generation magnetic memory technologies. A convolutional neural network trained on micromagnetic simulations accurately estimates the DM exchange constant and anisotropy distribution, showing good agreement with experimental results. This approach simplifies experimental processes and broadens the scope of materials research.
Publisher
npj Computational Materials
Published On
Jan 29, 2021
Authors
Masashi Kawaguchi, Kenji Tanabe, Keisuke Yamada, Takuya Sawa, Shun Hasegawa, Masamitsu Hayashi, Yoshinobu Nakatani
Tags
machine learning
magnetic domain
Dzyaloshinskii-Moriya interaction
magnetic anisotropy
thin-film heterostructures
magnetic memory

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