Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering

Abstract The 4D scanning transmission electron microscopy (STEM) method maps the structure and functionality of solids on the atomic scale, yielding information-rich data sets describing the interatomic electric and magnetic fields, structural and electronic order parameters, and other symmetry brea...

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Autores principales:	Mark P. Oxley, Maxim Ziatdinov, Ondrej Dyck, Andrew R. Lupini, Rama Vasudevan, Sergei V. Kalinin
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/ecf1dba1c93343adbad24d4051791a72
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spelling	oai:doaj.org-article:ecf1dba1c93343adbad24d4051791a722021-12-02T16:57:57ZProbing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering10.1038/s41524-021-00527-32057-3960https://doaj.org/article/ecf1dba1c93343adbad24d4051791a722021-05-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00527-3https://doaj.org/toc/2057-3960Abstract The 4D scanning transmission electron microscopy (STEM) method maps the structure and functionality of solids on the atomic scale, yielding information-rich data sets describing the interatomic electric and magnetic fields, structural and electronic order parameters, and other symmetry breaking distortions. A critical bottleneck is the dearth of analytical tools that can reduce complex 4D-STEM data to physically relevant descriptors. We propose an approach for the systematic exploration of 4D-STEM data using rotationally invariant variational autoencoders (rrVAE), which disentangle the general rotation of the object from other latent representations. The implementation of purely rotational rrVAE is discussed as are applications to simulated data for graphene and zincblende structures. The rrVAE analysis of experimental 4D-STEM data of defects in graphene is illustrated and compared to the classical center-of-mass analysis. This approach is universal for probing symmetry-breaking phenomena in complex systems and can be implemented for a broad range of diffraction methods.Mark P. OxleyMaxim ZiatdinovOndrej DyckAndrew R. LupiniRama VasudevanSergei V. KalininNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-6 (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 Mark P. Oxley Maxim Ziatdinov Ondrej Dyck Andrew R. Lupini Rama Vasudevan Sergei V. Kalinin Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering
description	Abstract The 4D scanning transmission electron microscopy (STEM) method maps the structure and functionality of solids on the atomic scale, yielding information-rich data sets describing the interatomic electric and magnetic fields, structural and electronic order parameters, and other symmetry breaking distortions. A critical bottleneck is the dearth of analytical tools that can reduce complex 4D-STEM data to physically relevant descriptors. We propose an approach for the systematic exploration of 4D-STEM data using rotationally invariant variational autoencoders (rrVAE), which disentangle the general rotation of the object from other latent representations. The implementation of purely rotational rrVAE is discussed as are applications to simulated data for graphene and zincblende structures. The rrVAE analysis of experimental 4D-STEM data of defects in graphene is illustrated and compared to the classical center-of-mass analysis. This approach is universal for probing symmetry-breaking phenomena in complex systems and can be implemented for a broad range of diffraction methods.
format	article
author	Mark P. Oxley Maxim Ziatdinov Ondrej Dyck Andrew R. Lupini Rama Vasudevan Sergei V. Kalinin
author_facet	Mark P. Oxley Maxim Ziatdinov Ondrej Dyck Andrew R. Lupini Rama Vasudevan Sergei V. Kalinin
author_sort	Mark P. Oxley
title	Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering
title_short	Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering
title_full	Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering
title_fullStr	Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering
title_full_unstemmed	Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering
title_sort	probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ecf1dba1c93343adbad24d4051791a72
work_keys_str_mv	AT markpoxley probingatomicscalesymmetrybreakingbyrotationallyinvariantmachinelearningofmultidimensionalelectronscattering AT maximziatdinov probingatomicscalesymmetrybreakingbyrotationallyinvariantmachinelearningofmultidimensionalelectronscattering AT ondrejdyck probingatomicscalesymmetrybreakingbyrotationallyinvariantmachinelearningofmultidimensionalelectronscattering AT andrewrlupini probingatomicscalesymmetrybreakingbyrotationallyinvariantmachinelearningofmultidimensionalelectronscattering AT ramavasudevan probingatomicscalesymmetrybreakingbyrotationallyinvariantmachinelearningofmultidimensionalelectronscattering AT sergeivkalinin probingatomicscalesymmetrybreakingbyrotationallyinvariantmachinelearningofmultidimensionalelectronscattering
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Probing atomic-scale symmetry breaking by rotationally invariant machine learning of multidimensional electron scattering

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