  Understanding transformations under electron beam irradiation requires
mapping the structural phases and their evolution in real time. To date, this
has mostly been a manual endeavor comprising of difficult frame-by-frame
analysis that is simultaneously tedious and prone to error. Here, we turn
towards the use of deep convolutional neural networks (DCNN) to automatically
determine the Bravais lattice symmetry present in atomically-resolved images. A
DCNN is trained to identify the Bravais lattice class given a 2D fast Fourier
transform of the input image. Monte-Carlo dropout is used for determining the
prediction probability, and results are shown for both simulated and real
atomically-resolved images from scanning tunneling microscopy and scanning
transmission electron microscopy. A reduced representation of the final layer
output allows to visualize the separation of classes in the DCNN and agrees
with physical intuition. We then apply the trained network to electron
beam-induced transformations in WS2, which allows tracking and determination of
growth rate of voids. These results are novel in two ways: (1) It shows that
DCNNs can be trained to recognize diffraction patterns, which is markedly
different from the typical "real image" cases, and (2) it provides a method
with in-built uncertainty quantification, allowing the real-time analysis of
phases present in atomically resolved images.
