  Frequency modulation (FM) Atomic Force Microscopy (AFM) with metal tips
functionalized with a CO molecule at the tip apex has provided access to the
internal structure of molecules with totally unprecedented resolution. We
propose a model to extract the chemical information from those AFM images in
order to achieve a complete identification of the imaged molecule. Our
Conditional Generative Adversarial Network (CGAN) converts a stack of AFM
images at various tip-sample distances into a ball-and-stick depiction, where
balls of different color and size represent the chemical species and sticks
represent the bonds, providing complete information on the structure and
chemical composition. The CGAN has been trained and tested with the QUAM-AFM
data set, that contains simulated AFM images for a collection of 686,000
molecules that include all the chemical species relevant in organic chemistry.
Tests with a large set of theoretical images and few experimental examples
demonstrate the accuracy and potential of our approach for molecular
identification.
