  Techniques for training artificial neural networks (ANNs) and convolutional
neural networks (CNNs) using simulated dynamical electron diffraction patterns
are described. The premise is based on the following facts. First, given a
suitable crystal structure model and scattering potential, electron diffraction
patterns can be simulated accurately using dynamical diffraction theory.
Secondly, using simulated diffraction patterns as input, ANNs can be trained
for the determination of crystal structural properties, such as crystal
orientation and local strain. Further, by applying the trained ANNs to
four-dimensional diffraction datasets (4D-DD) collected using the scanning
electron nanodiffraction (SEND) or 4D scanning transmission electron microscopy
(4D-STEM) techniques, the crystal structural properties can be mapped at high
spatial resolution. Here, we demonstrate the ANN-enabled possibilities for the
analysis of crystal orientation and strain at high precision and benchmark the
performance of ANNs and CNNs by comparing with previous methods. A factor of
thirty improvement in angular resolution at 0.009 degrees (0.16 mrad) for
orientation mapping, sensitivity at 0.04% or less for strain mapping, and
improvements in computational performance are demonstrated.
