  Using electron beam manipulation, we enable deterministic motion of
individual Si atoms in graphene along predefined trajectories. Structural
evolution during the dopant motion was explored, providing information on
changes of the Si atom neighborhood during atomic motion and providing
statistical information of possible defect configurations. The combination of a
Gaussian mixture model and principal component analysis applied to the deep
learning-processed experimental data allowed disentangling of the atomic
distortions for two different graphene sublattices. This approach demonstrates
the potential of e-beam manipulation to create defect libraries of multiple
realizations of the same defect and explore the potential of symmetry breaking
physics. The rapid image analytics enabled via a deep learning network further
empowers instrumentation for e-beam controlled atom-by-atom fabrication. The
analysis described in the paper can be reproduced via an interactive Jupyter
notebook at https://git.io/JJ3Bx
