Learning surface molecular structures via machine vision

Machine learning: Computers automatically decode complex molecular patterns Complex patterns formed by thousands of molecules on a surface can now be automatically recognized and classified by a computer. Sergei Kalinin, Maxim Ziatdinov and Artem Maksov at Oak Ridge National Lab have developed a ‘ma...

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Autores principales:	Maxim Ziatdinov, Artem Maksov, Sergei V. Kalinin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/9b69d65a9f054d11bfb90d00872589e6
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id	oai:doaj.org-article:9b69d65a9f054d11bfb90d00872589e6
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spelling	oai:doaj.org-article:9b69d65a9f054d11bfb90d00872589e62021-12-02T11:50:54ZLearning surface molecular structures via machine vision10.1038/s41524-017-0038-72057-3960https://doaj.org/article/9b69d65a9f054d11bfb90d00872589e62017-08-01T00:00:00Zhttps://doi.org/10.1038/s41524-017-0038-7https://doaj.org/toc/2057-3960Machine learning: Computers automatically decode complex molecular patterns Complex patterns formed by thousands of molecules on a surface can now be automatically recognized and classified by a computer. Sergei Kalinin, Maxim Ziatdinov and Artem Maksov at Oak Ridge National Lab have developed a ‘machine vision’ approach, adapting techniques already used in cancer detection and satellite imaging, to analyze up to 1000 s of individual molecular configurations from microscopic images of so-called ‘buckybowl’ molecules on gold surfaces. These bowl-shaped molecules may rest face up or down, and can adopt different rotational orientations, resulting in a rich tapestry of molecular patterns which are too complicated to sort manually or with existing computational methods. However, this machine vision approach revealed details of how buckybowls interact with their neighbors to build complex arrays, which could help in the design of molecular memory devices.Maxim ZiatdinovArtem MaksovSergei V. KalininNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 3, Iss 1, Pp 1-9 (2017)
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 Maxim Ziatdinov Artem Maksov Sergei V. Kalinin Learning surface molecular structures via machine vision
description	Machine learning: Computers automatically decode complex molecular patterns Complex patterns formed by thousands of molecules on a surface can now be automatically recognized and classified by a computer. Sergei Kalinin, Maxim Ziatdinov and Artem Maksov at Oak Ridge National Lab have developed a ‘machine vision’ approach, adapting techniques already used in cancer detection and satellite imaging, to analyze up to 1000 s of individual molecular configurations from microscopic images of so-called ‘buckybowl’ molecules on gold surfaces. These bowl-shaped molecules may rest face up or down, and can adopt different rotational orientations, resulting in a rich tapestry of molecular patterns which are too complicated to sort manually or with existing computational methods. However, this machine vision approach revealed details of how buckybowls interact with their neighbors to build complex arrays, which could help in the design of molecular memory devices.
format	article
author	Maxim Ziatdinov Artem Maksov Sergei V. Kalinin
author_facet	Maxim Ziatdinov Artem Maksov Sergei V. Kalinin
author_sort	Maxim Ziatdinov
title	Learning surface molecular structures via machine vision
title_short	Learning surface molecular structures via machine vision
title_full	Learning surface molecular structures via machine vision
title_fullStr	Learning surface molecular structures via machine vision
title_full_unstemmed	Learning surface molecular structures via machine vision
title_sort	learning surface molecular structures via machine vision
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/9b69d65a9f054d11bfb90d00872589e6
work_keys_str_mv	AT maximziatdinov learningsurfacemolecularstructuresviamachinevision AT artemmaksov learningsurfacemolecularstructuresviamachinevision AT sergeivkalinin learningsurfacemolecularstructuresviamachinevision
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Learning surface molecular structures via machine vision

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