  High-throughput grain mapping with sub-nanometer spatial resolution is
demonstrated using scanning nanobeam electron diffraction (also known as 4D
scanning transmission electron microscopy, or 4D-STEM) combined with high-speed
direct electron detection. An electron probe size down to 0.5 nm in diameter is
implemented and the sample investigated is a gold-palladium nanoparticle
catalyst. Computational analysis of the 4D-STEM data sets is performed using a
disk registration algorithm to identify the diffraction peaks followed by
feature learning to map the individual grains. Two unsupervised feature
learning techniques are compared: Principal component analysis (PCA) and
non-negative matrix factorization (NNMF). The characteristics of the PCA versus
NNMF output are compared and the potential of the 4D-STEM approach for
statistical analysis of grain orientations at high spatial resolution is
discussed.
