  Despite being the main tool to visualize molecules at the atomic scale, AFM
with CO-functionalized metal tips is unable to chemically identify the observed
molecules. Here we present a strategy to address this challenging task using
deep learning techniques. Instead of identifying a finite number of molecules
following a traditional classification approach, we define the molecular
identification as an image captioning problem. We design an architecture,
composed of two multimodal recurrent neural networks, capable of identifying
the structure and composition of an unknown molecule using a 3D-AFM image stack
as input. The neural network is trained to provide the name of each molecule
according to the IUPAC nomenclature rules. To train and test this algorithm we
use the novel QUAM-AFM dataset, which contains almost 700,000 molecules and 165
million AFM images. The accuracy of the predictions is remarkable, achieving a
high score quantified by the cumulative BLEU 4-gram, a common metric in
language recognition studies.
