  Over the last decade, scanning transmission electron microscopy (STEM) has
emerged as a powerful tool for probing atomic structures of complex materials
with picometer precision, opening the pathway toward exploring ferroelectric,
ferroelastic, and chemical phenomena on the atomic-scale. Analyses to date
extracting a polarization signal from lattice coupled distortions in STEM
imaging rely on discovery of atomic positions from intensity maxima/minima and
subsequent calculation of polarization and other order parameter fields from
the atomic displacements. Here, we explore the feasibility of polarization
mapping directly from the analysis of STEM images using deep convolutional
neural networks (DCNNs). In this approach, the DCNN is trained on the labeled
part of the image (i.e., for human labelling), and the trained network is
subsequently applied to other images. We explore the effects of the choice of
the descriptors (centered on atomic columns and grid-based), the effects of
observational bias, and whether the network trained on one composition can be
applied to a different one. This analysis demonstrates the tremendous potential
of the DCNN for the analysis of high-resolution STEM imaging and spectral data
and highlights the associated limitations.
