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

Abstract Machine learning is applied to a large number of modern devices that are essential in building an energy-efficient smart society. Audio and face recognition are among the most well-known technologies that make use of such artificial intelligence. In materials research, machine learning is a...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Masashi Kawaguchi, Kenji Tanabe, Keisuke Yamada, Takuya Sawa, Shun Hasegawa, Masamitsu Hayashi, Yoshinobu Nakatani
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/9596695d90ee43b88202f0a610ff66cb
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:9596695d90ee43b88202f0a610ff66cb
record_format	dspace
spelling	oai:doaj.org-article:9596695d90ee43b88202f0a610ff66cb2021-12-02T13:57:35ZDetermination of the Dzyaloshinskii-Moriya interaction using pattern recognition and machine learning10.1038/s41524-020-00485-22057-3960https://doaj.org/article/9596695d90ee43b88202f0a610ff66cb2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41524-020-00485-2https://doaj.org/toc/2057-3960Abstract Machine learning is applied to a large number of modern devices that are essential in building an energy-efficient smart society. Audio and face recognition are among the most well-known technologies that make use of such artificial intelligence. In materials research, machine learning is adapted to predict materials with certain functionalities, an approach often referred to as materials informatics. Here, we show that machine learning can be used to extract material parameters from a single image obtained in experiments. The Dzyaloshinskii–Moriya (DM) interaction and the magnetic anisotropy distribution of thin-film heterostructures, parameters that are critical in developing next-generation storage class magnetic memory technologies, are estimated from a magnetic domain image. Micromagnetic simulation is used to generate thousands of random images for training and model validation. A convolutional neural network system is employed as the learning tool. The DM exchange constant of typical Co-based thin-film heterostructures is studied using the trained system: the estimated values are in good agreement with experiments. Moreover, we show that the system can independently determine the magnetic anisotropy distribution, demonstrating the potential of pattern recognition. This approach can considerably simplify experimental processes and broaden the scope of materials research.Masashi KawaguchiKenji TanabeKeisuke YamadaTakuya SawaShun HasegawaMasamitsu HayashiYoshinobu NakataniNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Masashi Kawaguchi Kenji Tanabe Keisuke Yamada Takuya Sawa Shun Hasegawa Masamitsu Hayashi Yoshinobu Nakatani Determination of the Dzyaloshinskii-Moriya interaction using pattern recognition and machine learning
description	Abstract Machine learning is applied to a large number of modern devices that are essential in building an energy-efficient smart society. Audio and face recognition are among the most well-known technologies that make use of such artificial intelligence. In materials research, machine learning is adapted to predict materials with certain functionalities, an approach often referred to as materials informatics. Here, we show that machine learning can be used to extract material parameters from a single image obtained in experiments. The Dzyaloshinskii–Moriya (DM) interaction and the magnetic anisotropy distribution of thin-film heterostructures, parameters that are critical in developing next-generation storage class magnetic memory technologies, are estimated from a magnetic domain image. Micromagnetic simulation is used to generate thousands of random images for training and model validation. A convolutional neural network system is employed as the learning tool. The DM exchange constant of typical Co-based thin-film heterostructures is studied using the trained system: the estimated values are in good agreement with experiments. Moreover, we show that the system can independently determine the magnetic anisotropy distribution, demonstrating the potential of pattern recognition. This approach can considerably simplify experimental processes and broaden the scope of materials research.
format	article
author	Masashi Kawaguchi Kenji Tanabe Keisuke Yamada Takuya Sawa Shun Hasegawa Masamitsu Hayashi Yoshinobu Nakatani
author_facet	Masashi Kawaguchi Kenji Tanabe Keisuke Yamada Takuya Sawa Shun Hasegawa Masamitsu Hayashi Yoshinobu Nakatani
author_sort	Masashi Kawaguchi
title	Determination of the Dzyaloshinskii-Moriya interaction using pattern recognition and machine learning
title_short	Determination of the Dzyaloshinskii-Moriya interaction using pattern recognition and machine learning
title_full	Determination of the Dzyaloshinskii-Moriya interaction using pattern recognition and machine learning
title_fullStr	Determination of the Dzyaloshinskii-Moriya interaction using pattern recognition and machine learning
title_full_unstemmed	Determination of the Dzyaloshinskii-Moriya interaction using pattern recognition and machine learning
title_sort	determination of the dzyaloshinskii-moriya interaction using pattern recognition and machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9596695d90ee43b88202f0a610ff66cb
work_keys_str_mv	AT masashikawaguchi determinationofthedzyaloshinskiimoriyainteractionusingpatternrecognitionandmachinelearning AT kenjitanabe determinationofthedzyaloshinskiimoriyainteractionusingpatternrecognitionandmachinelearning AT keisukeyamada determinationofthedzyaloshinskiimoriyainteractionusingpatternrecognitionandmachinelearning AT takuyasawa determinationofthedzyaloshinskiimoriyainteractionusingpatternrecognitionandmachinelearning AT shunhasegawa determinationofthedzyaloshinskiimoriyainteractionusingpatternrecognitionandmachinelearning AT masamitsuhayashi determinationofthedzyaloshinskiimoriyainteractionusingpatternrecognitionandmachinelearning AT yoshinobunakatani determinationofthedzyaloshinskiimoriyainteractionusingpatternrecognitionandmachinelearning
_version_	1718392274174345216

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

Ejemplares similares