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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2020
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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. |
format | Online Article Text |
id | pubmed-7043916 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
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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