  Electron backscatter diffraction is a widely used technique for nano- to
micro-scale analysis of crystal structure and orientation. Backscatter patterns
produced by an alloy solid solution matrix and its ordered superlattice exhibit
only extremely subtle differences, due to the inelastic scattering that
precedes coherent diffraction. We show that unsupervised machine learning (with
PCA, NMF, and an autoencoder neural network) is well suited to fine feature
extraction and superlattice/matrix classification. Remapping cluster average
patterns onto the diffraction sphere lets us compare Kikuchi band profiles to
dynamical simulations, confirm the superlattice stoichiometry, and facilitate
virtual imaging with a spherical solid angle aperture. This pipeline now
enables unparalleled mapping of exquisite crystallographic detail from a wide
range of materials within the scanning electron microscope.
