  Atomic Force Microscopy (AFM) operating in the frequency modulation mode with
a metal tip functionalized with a CO molecule images the internal structure of
molecules with an unprecedented resolution. The interpretation of these images
is often difficult, making the support of theoretical simulations important.
Current simulation methods, particularly the most accurate ones, require
expertise and resources to perform ab initio calculations for the necessary
inputs (i.e charge density and electrostatic potential of the molecule). Here,
we propose an efficient and simple alternative to simulate these AFM images
based on a Conditional Generative Adversarial Network (CGAN), that avoids all
force calculations, and uses as the only input a 2D ball--and--stick depiction
of the molecule. We discuss the performance of the model when optimized using
different training subsets. Our CGAN reproduces accurately the intramolecular
contrast observed in the simulated images for quasi--planar molecules, but has
significant limitations for molecules with a significant internal torsion, due
to the strictly 2D character of the input.

Materials Science;Disordered Systems and Neural Networks