  Over the last two decades, Electron Energy Loss Spectroscopy (EELS) imaging
with a scanning transmission electron microscope (STEM) has emerged as a
technique of choice for visualizing complex chemical, electronic, plasmonic,
and phononic phenomena in complex materials and structures. The availability of
the EELS data necessitates the development of methods to analyze
multidimensional datasets with complex spatial and energy structures.
Traditionally, the analysis of these data sets has been based on analysis of
individual spectra, one at a time, whereas the spatial structure and
correlations between individual spatial pixels containing the relevant
information of the physics of underpinning processes have generally been
ignored and analyzed only via the visualization as 2D maps. Here we develop a
machine learning-based approach and workflows for the analysis of spatial
structures in 3D EELS data sets using a combination of dimensionality reduction
and multichannel rotationally-invariant variational autoencoders. This approach
is illustrated for the analysis of both the plasmonic phenomena in a system of
nanowires and in the core excitations in functional oxides using low loss and
core loss EELS, respectively. The code developed in this manuscript is open
sourced and freely available and provided as a Jupyter notebook for the
interested reader here.
