Deep Bayesian local crystallography

Abstract The advent of high-resolution electron and scanning probe microscopy imaging has opened the floodgates for acquiring atomically resolved images of bulk materials, 2D materials, and surfaces. This plethora of data contains an immense volume of information on materials structures, structural...

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Autores principales:	Sergei V. Kalinin, Mark P. Oxley, Mani Valleti, Junjie Zhang, Raphael P. Hermann, Hong Zheng, Wenrui Zhang, Gyula Eres, Rama K. Vasudevan, Maxim Ziatdinov
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/13085c9b5ce442438a7c22e2d30d8e85
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id	oai:doaj.org-article:13085c9b5ce442438a7c22e2d30d8e85
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spelling	oai:doaj.org-article:13085c9b5ce442438a7c22e2d30d8e852021-11-14T12:15:28ZDeep Bayesian local crystallography10.1038/s41524-021-00621-62057-3960https://doaj.org/article/13085c9b5ce442438a7c22e2d30d8e852021-11-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00621-6https://doaj.org/toc/2057-3960Abstract The advent of high-resolution electron and scanning probe microscopy imaging has opened the floodgates for acquiring atomically resolved images of bulk materials, 2D materials, and surfaces. This plethora of data contains an immense volume of information on materials structures, structural distortions, and physical functionalities. Harnessing this knowledge regarding local physical phenomena necessitates the development of the mathematical frameworks for extraction of relevant information. However, the analysis of atomically resolved images is often based on the adaptation of concepts from macroscopic physics, notably translational and point group symmetries and symmetry lowering phenomena. Here, we explore the bottom-up definition of structural units and symmetry in atomically resolved data using a Bayesian framework. We demonstrate the need for a Bayesian definition of symmetry using a simple toy model and demonstrate how this definition can be extended to the experimental data using deep learning networks in a Bayesian setting, namely rotationally invariant variational autoencoders.Sergei V. KalininMark P. OxleyMani ValletiJunjie ZhangRaphael P. HermannHong ZhengWenrui ZhangGyula EresRama K. VasudevanMaxim ZiatdinovNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-12 (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 Sergei V. Kalinin Mark P. Oxley Mani Valleti Junjie Zhang Raphael P. Hermann Hong Zheng Wenrui Zhang Gyula Eres Rama K. Vasudevan Maxim Ziatdinov Deep Bayesian local crystallography
description	Abstract The advent of high-resolution electron and scanning probe microscopy imaging has opened the floodgates for acquiring atomically resolved images of bulk materials, 2D materials, and surfaces. This plethora of data contains an immense volume of information on materials structures, structural distortions, and physical functionalities. Harnessing this knowledge regarding local physical phenomena necessitates the development of the mathematical frameworks for extraction of relevant information. However, the analysis of atomically resolved images is often based on the adaptation of concepts from macroscopic physics, notably translational and point group symmetries and symmetry lowering phenomena. Here, we explore the bottom-up definition of structural units and symmetry in atomically resolved data using a Bayesian framework. We demonstrate the need for a Bayesian definition of symmetry using a simple toy model and demonstrate how this definition can be extended to the experimental data using deep learning networks in a Bayesian setting, namely rotationally invariant variational autoencoders.
format	article
author	Sergei V. Kalinin Mark P. Oxley Mani Valleti Junjie Zhang Raphael P. Hermann Hong Zheng Wenrui Zhang Gyula Eres Rama K. Vasudevan Maxim Ziatdinov
author_facet	Sergei V. Kalinin Mark P. Oxley Mani Valleti Junjie Zhang Raphael P. Hermann Hong Zheng Wenrui Zhang Gyula Eres Rama K. Vasudevan Maxim Ziatdinov
author_sort	Sergei V. Kalinin
title	Deep Bayesian local crystallography
title_short	Deep Bayesian local crystallography
title_full	Deep Bayesian local crystallography
title_fullStr	Deep Bayesian local crystallography
title_full_unstemmed	Deep Bayesian local crystallography
title_sort	deep bayesian local crystallography
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/13085c9b5ce442438a7c22e2d30d8e85
work_keys_str_mv	AT sergeivkalinin deepbayesianlocalcrystallography AT markpoxley deepbayesianlocalcrystallography AT manivalleti deepbayesianlocalcrystallography AT junjiezhang deepbayesianlocalcrystallography AT raphaelphermann deepbayesianlocalcrystallography AT hongzheng deepbayesianlocalcrystallography AT wenruizhang deepbayesianlocalcrystallography AT gyulaeres deepbayesianlocalcrystallography AT ramakvasudevan deepbayesianlocalcrystallography AT maximziatdinov deepbayesianlocalcrystallography
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Deep Bayesian local crystallography

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