  Implementation of a fast, robust, and fully-automated pipeline for crystal
structure determination and underlying strain mapping for crystalline materials
is important for many technological applications. Scanning electron
nanodiffraction offers a procedure for identifying and collecting strain maps
with good accuracy and high spatial resolutions. However, the application of
this technique is limited, particularly in thick samples where the electron
beam can undergo multiple scattering, which introduces signal nonlinearities.
Deep learning methods have the potential to invert these complex signals, but
previous implementations are often trained only on specific crystal systems or
a small subset of the crystal structure and microscope parameter phase space.
In this study, we implement a Fourier space, complex-valued deep neural network
called FCU-Net, to invert highly nonlinear electron diffraction patterns into
the corresponding quantitative structure factor images. We trained the FCU-Net
using over 200,000 unique simulated dynamical diffraction patterns which
include many different combinations of crystal structures, orientations,
thicknesses, microscope parameters, and common experimental artifacts. We
evaluated the trained FCU-Net model against simulated and experimental 4D-STEM
diffraction datasets, where it substantially out-performs conventional analysis
methods. Our simulated diffraction pattern library, implementation of FCU-Net,
and trained model weights are freely available in open source repositories, and
can be adapted to many different diffraction measurement problems.
