  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 non-planar 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 apply
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.
