  Scanning tunnelling microscopy (STM) with a functionalized tip apex reveals
the geometric and electronic structure of a sample within the same experiment.
However, the complex nature of the signal makes images difficult to interpret
and has so far limited most research to planar samples with a known chemical
composition. Here, we present automated structure discovery for STM (ASD-STM),
a machine learning tool for predicting the atomic structure directly from an
STM image, by building upon successful methods for structure discovery in
non-contact atomic force microscopy (nc-AFM). We apply the method on various
organic molecules and achieve good accuracy on structure predictions and
chemical identification on a qualitative level, while highlighting future
development requirements to ASD-STM. This method is directly applicable to
experimental STM images of organic molecules, making structure discovery
available for a wider SPM audience outside of nc-AFM. This work also opens
doors for more advanced machine learning methods to be developed for STM
discovery.
