Automated structure discovery in atomic force microscopy

Benjamin Alldritt, Hapala Hapala, Niko Oinonen, Fedor Urtev, Ondrej Krejci, Filippo Federici Canova, Juho Kannala, Fabian Schulz, Peter Liljeroth, Adam Foster

Research output: Contribution to journalArticleScientificpeer-review

72 Citations (Scopus)
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Atomic force microscopy (AFM) with molecule-functionalized tips has emerged as the primary experimental tech- nique 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 originat- ing 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 struc- ture 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.
Original languageEnglish
Article numbereaay6913
Number of pages10
JournalScience Advances
Issue number9
Publication statusPublished - 26 Feb 2020
MoE publication typeA1 Journal article-refereed


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