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Automated structure discovery in atomic force microscopy

Atomic force microscopy (AFM) with molecule-functionalized tips has emerged as the primary experimental technique for probing the atomic structure of organic molecules on surfaces. Most experiments have been limited to nearly planar aromatic molecules due to difficulties with interpretation of highl...

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Autores principales: Alldritt, Benjamin, Hapala, Prokop, Oinonen, Niko, Urtev, Fedor, Krejci, Ondrej, Federici Canova, Filippo, Kannala, Juho, Schulz, Fabian, Liljeroth, Peter, Foster, Adam S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7043916/
https://www.ncbi.nlm.nih.gov/pubmed/32133405
http://dx.doi.org/10.1126/sciadv.aay6913
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author Alldritt, Benjamin
Hapala, Prokop
Oinonen, Niko
Urtev, Fedor
Krejci, Ondrej
Federici Canova, Filippo
Kannala, Juho
Schulz, Fabian
Liljeroth, Peter
Foster, Adam S.
author_facet Alldritt, Benjamin
Hapala, Prokop
Oinonen, Niko
Urtev, Fedor
Krejci, Ondrej
Federici Canova, Filippo
Kannala, Juho
Schulz, Fabian
Liljeroth, Peter
Foster, Adam S.
author_sort Alldritt, Benjamin
collection PubMed
description Atomic force microscopy (AFM) with molecule-functionalized tips has emerged as the primary experimental technique for probing the atomic structure of organic molecules on surfaces. Most experiments have been limited to nearly planar aromatic molecules due to difficulties with interpretation of highly distorted AFM images originating from nonplanar molecules. Here, we develop a deep learning infrastructure that matches a set of AFM images with a unique descriptor characterizing the molecular configuration, allowing us to predict the molecular structure directly. We apply this methodology to resolve several distinct adsorption configurations of 1S-camphor on Cu(111) based on low-temperature AFM measurements. This approach will open the door to applying high-resolution AFM to a large variety of systems, for which routine atomic and chemical structural resolution on the level of individual objects/molecules would be a major breakthrough.
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spelling pubmed-70439162020-03-04 Automated structure discovery in atomic force microscopy Alldritt, Benjamin Hapala, Prokop Oinonen, Niko Urtev, Fedor Krejci, Ondrej Federici Canova, Filippo Kannala, Juho Schulz, Fabian Liljeroth, Peter Foster, Adam S. Sci Adv Research Articles Atomic force microscopy (AFM) with molecule-functionalized tips has emerged as the primary experimental technique for probing the atomic structure of organic molecules on surfaces. Most experiments have been limited to nearly planar aromatic molecules due to difficulties with interpretation of highly distorted AFM images originating from nonplanar molecules. Here, we develop a deep learning infrastructure that matches a set of AFM images with a unique descriptor characterizing the molecular configuration, allowing us to predict the molecular structure directly. We apply this methodology to resolve several distinct adsorption configurations of 1S-camphor on Cu(111) based on low-temperature AFM measurements. This approach will open the door to applying high-resolution AFM to a large variety of systems, for which routine atomic and chemical structural resolution on the level of individual objects/molecules would be a major breakthrough. American Association for the Advancement of Science 2020-02-26 /pmc/articles/PMC7043916/ /pubmed/32133405 http://dx.doi.org/10.1126/sciadv.aay6913 Text en Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). http://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (http://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
spellingShingle Research Articles
Alldritt, Benjamin
Hapala, Prokop
Oinonen, Niko
Urtev, Fedor
Krejci, Ondrej
Federici Canova, Filippo
Kannala, Juho
Schulz, Fabian
Liljeroth, Peter
Foster, Adam S.
Automated structure discovery in atomic force microscopy
title Automated structure discovery in atomic force microscopy
title_full Automated structure discovery in atomic force microscopy
title_fullStr Automated structure discovery in atomic force microscopy
title_full_unstemmed Automated structure discovery in atomic force microscopy
title_short Automated structure discovery in atomic force microscopy
title_sort automated structure discovery in atomic force microscopy
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7043916/
https://www.ncbi.nlm.nih.gov/pubmed/32133405
http://dx.doi.org/10.1126/sciadv.aay6913
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