  The nature of the atomic defects on the hydrogen passivated Si (100) surface
is analyzed using deep learning and scanning tunneling microscopy (STM). A
robust deep learning framework capable of identifying atomic species, defects,
in the presence of non-resolved contaminates, step edges, and noise is
developed. The automated workflow, based on the combination of several networks
for image assessment, atom-finding and defect finding, is developed to perform
the analysis at different levels of description and is deployed on an
operational STM platform. This is further extended to unsupervised
classification of the extracted defects using the mean-shift clustering
algorithm, which utilizes features automatically engineered from the combined
output of neural networks. This combined approach allows the identification of
localized and extended defects on the topographically non-uniform surfaces or
real materials. Our approach is universal in nature and can be applied to other
surfaces for building comprehensive libraries of atomic defects in quantum
materials.
