  The robust approach for real-time analysis of the scanning transmission
electron microscopy (STEM) data streams, based on the ensemble learning and
iterative training (ELIT) of deep convolutional neural networks, is implemented
on an operational microscope, enabling the exploration of the dynamics of
specific atomic configurations under electron beam irradiation via an automated
experiment in STEM. Combined with beam control, this approach allows studying
beam effects on selected atomic groups and chemical bonds in a fully automated
mode. Here, we demonstrate atomically precise engineering of single vacancy
lines in transition metal dichalcogenides and the creation and identification
of topological defects graphene. The ELIT-based approach opens the pathway
toward the direct on-the-fly analysis of the STEM data and engendering
real-time feedback schemes for probing electron beam chemistry, atomic
manipulation, and atom by atom assembly.
