RT Journal Article
SR Electronic
T1 Automated structure discovery in atomic force microscopy
JF Science Advances
JO Sci Adv
FD American Association for the Advancement of Science
SP eaay6913
DO 10.1126/sciadv.aay6913
VO 6
IS 9
A1 Alldritt, Benjamin
A1 Hapala, Prokop
A1 Oinonen, Niko
A1 Urtev, Fedor
A1 Krejci, Ondrej
A1 Federici Canova, Filippo
A1 Kannala, Juho
A1 Schulz, Fabian
A1 Liljeroth, Peter
A1 Foster, Adam S.
YR 2020
UL http://advances.sciencemag.org/content/6/9/eaay6913.abstract
AB 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.