  One of the largest obstacles facing scanning probe microscopy is the constant
need to correct flaws in the scanning probe in situ. This is currently a
manual, time-consuming process that would benefit greatly from automation. Here
we introduce a convolutional neural network protocol that enables automated
recognition of a variety of desirable and undesirable scanning probe tip states
on both metal and non-metal surfaces. By combining the best performing models
into majority voting ensembles, we find that the desirable states of H:Si(100)
can be distinguished with a mean precision of 0.89 and an average
receiver-operator-characteristic curve area of 0.95. More generally, high and
low-quality tips can be distinguished with a mean precision of 0.96 and near
perfect area-under-curve of 0.98. With trivial modifications, we also
successfully automatically identify undesirable, non-surface-specific states on
surfaces of Au(111) and Cu(111). In these cases we find mean precisions of 0.95
and 0.75 and area-under-curves of 0.98 and 0.94, respectively.
