Spectroscopic methods—like
nuclear magnetic resonance,
mass
spectrometry, X-ray diffraction, and UV/visible spectroscopies—applied
to molecular ensembles have so far been the workhorse for molecular
identification. Here, we propose a radically different chemical characterization
approach, based on the ability of noncontact atomic force microscopy
with metal tips functionalized with a CO molecule at the tip apex
(referred as HR-AFM) to resolve the internal structure of individual
molecules. Our work demonstrates that a stack of constant-height HR-AFM
images carries enough chemical information for a complete identification
(structure and composition) of quasiplanar organic molecules, and
that this information can be retrieved using machine learning techniques
that are able to disentangle the contribution of chemical composition,
bond topology, and internal torsion of the molecule to the HR-AFM
contrast. In particular, we exploit multimodal recurrent neural networks
(M-RNN) that combine convolutional neural networks for image analysis
and recurrent neural networks to deal with language processing, to
formulate the molecular identification as an imaging captioning problem.
The algorithm is trained using a data set—which contains almost
700,000 molecules and 165 million theoretical AFM images—to
produce as final output the IUPAC name of the imaged molecule. Our
extensive test with theoretical images and a few experimental ones
shows the potential of deep learning algorithms in the automatic identification
of molecular compounds by AFM. This achievement supports the development
of on-surface synthesis and overcomes some limitations of spectroscopic
methods in traditional solution-based synthesis.