  Advancements in fast electron detectors have enabled the statistically
significant sampling of crystal structures on the nanometre scale by means of
Scanning Electron Nanobeam Diffraction (SEND). Characterisation of structural
similarity across this length scale is key to bridging the gap between local
atomic structure (using atomic resolution techniques such as High Resolution
Scanning Transmission Electron Microscopy (HR-STEM)) and the macro-scale (using
bulk techniques such as powder X-ray and neutron diffraction). The use of SEND
technique allows for structural investigation of a broad range of samples, due
to the techniques ability to operate with low electron dosage and its tolerance
for sample thickness, relative to HR-STEM. This, coupled with the capacity for
data collection over a wide areas and the automation of this collection, allows
for statistically representative sampling of the microstructure. Also due to
these factors, SEND generates large datasets and as a result automated/
semi-automated data processing workflows are required to aid in maximal
extraction of useful information. As such, this paper outlines a versatile,
data-driven approach for producing domain maps, as well as a statistical
approach for assessing their applicability. The production of such domain maps
for a dataset can help highlight nuance in the microstructure, as well as
improve the manageability of that dataset for further investigation. The
workflow outlined utilises a Variational AutoEncoder to identify and learn the
sources of variance in the diffraction signal and this, in combination with
clustering techniques, is used to produce domain maps for a set of varied
example cases. This approach: is agnostic to domain crystallinity; requires no
prior knowledge of crystal structure; and does not require the, potentially
prohibitive, simulation of a library of appropriate diffraction patterns.
