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Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations
journal contribution
posted on 2017-12-07, 00:00 authored by Maxim Ziatdinov, Ondrej Dyck, Artem Maksov, Xufan Li, Xiahan Sang, Kai Xiao, Raymond R. Unocic, Rama Vasudevan, Stephen Jesse, Sergei V. KalininRecent
advances in scanning transmission electron and scanning
probe microscopies have opened exciting opportunities in probing the
materials structural parameters and various functional properties
in real space with angstrom-level precision. This progress has been
accompanied by an exponential increase in the size and quality of
data sets produced by microscopic and spectroscopic experimental techniques.
These developments necessitate adequate methods for extracting relevant
physical and chemical information from the large data sets, for which a priori information on the structures of various atomic
configurations and lattice defects is limited or absent. Here we demonstrate
an application of deep neural networks to extract information from
atomically resolved images including location of the atomic species
and type of defects. We develop a “weakly supervised”
approach that uses information on the coordinates of all atomic species
in the image, extracted via a deep neural network,
to identify a rich variety of defects that are not part of an initial
training set. We further apply our approach to interpret complex atomic
and defect transformation, including switching between different coordination
of silicon dopants in graphene as a function of time, formation of
peculiar silicon dimer with mixed 3-fold and 4-fold coordination,
and the motion of molecular “rotor”. This deep learning-based
approach resembles logic of a human operator, but can be scaled leading
to significant shift in the way of extracting and analyzing information
from raw experimental data.