  Modern TEM instrumentation can probe a wide range of structural, optical, and
chemical properties with unprecedented resolution. However, each of these
properties must be recorded in independent datasets using different detector
modes with no unifying framework currently available for quantitatively mapping
their relationships onto chemically distinct features, particularly in complex
morphologies. Here, we tackle this challenge by proposing a data acquisition
and analysis workflow called "hypermodal data fusion," describing how to
directly couple an arbitrary number of highly disparate detector modes
including spectroscopy and scanning diffraction and jointly analyze them for
correlations. We demonstrate this concept on a random collection of anatase and
rutile nanoparticles, first detailing how to use core-loss EELS to unmix the
different polymorphs despite three-dimensional overlap along the beam direction
and then showing how this can be used to extract polymorph-specific
composition, bandgaps, and crystal structure. We conclude with a discussion on
the applicability of this workflow for a broad range of materials systems.
