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.CO-AFM now offers an unprecedented window into molecular structure on surfaces -aside from the detailed resolution of the results of molecular assembly 11,12 , it is possible to study bond order 13 , charge distributions 14,15 and the individual steps of on-surface chemical reactions 16,17,18,19 .As yet, most CO-AFM studies have been focused on planar molecular systems, where the experimental image requires almost no interpretation 10,5,20 . Even where understanding is not immediately obvious, such as due to controversies over the nature of observed bonds 21 , efficient models have been developed 22,12,23,24,25 that explain the contrast mechanism in terms of the tip-surface interaction and CO lateral flexibility. However, the further the systems studied are from two-dimensional molecules containing only hydrogen and carbon, the more complex and time consuming (if not impossible) the interpretation process becomes 17,26,27,28,29 . While recent measurements using rigid O-terminated copper tips makes interpreting images of flat systems even easier 30,31 , the rigidity also means even less atoms can be characterized when moving to 3D systems. In recent years, CO-AFM has moved towards measuring truly unknown structures 29,32,33,34 , where it has overcome many of the limitations of techniques such as nuclear magnetic resonance and mass spectrometry. It is clear that this trend is going to continue, and potentially even accelerate, in particular for innovative studies, e.g. in life sciences or biochemistry 6,7 , demonstrated manifestly in the first CO-AFM images of DNA 35 . Reliable interpretation of such data becomes a vast exploration through all possible molecules, configurations and imaging parameters to find agreement. This is impractical in anything beyond very simple systems, severely limiting the ultimate power of the technique.In this work, we couple a systematic software approach with detailed experimental CO-AFM imaging to understand and predict AFM images for molecules of any size, configuration or orientation without prior knowledge of the system being studied. We use the late...
