Atomic force microscopy simulations for CO-functionalized tips with deep learning

Abstract

Atomic force microscopy (AFM) operating in the frequency modulation mode with a metal tip functionalized with a CO molecule is able to image 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 a computationally inexpensive and fast alternative to the physical simulation of 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 trained with different subsets extracted from the previously published QUAM-AFM database. Our CGAN reproduces accurately the intramolecular contrast observed in the simulated images for quasi–planar molecules, but has limitations for molecules with a substantial internal corrugation, due to the strictly 2D character of the input.