  Material properties strongly depend on the nature and concentration of
defects. Characterizing these features may require nano- to atomic-scale
resolution to establish structure-property relationships. 4D-STEM, a technique
where diffraction patterns are acquired at a grid of points on the sample,
provides a versatile method for highlighting defects. Computational analysis of
the diffraction patterns with virtual detectors produces images that can map
material properties. Here, using multislice simulations, we explore different
virtual detectors that can be applied to the diffraction patterns that go
beyond the binary response functions that are possible using ordinary STEM
detectors. Using graphene and lead titanate as model systems, we investigate
the application of virtual detectors to study local order and in particular
defects. We find that using a small convergence angle with a rotationally
varying detector most efficiently highlights defect signals. With experimental
graphene data, we demonstrate the effectiveness of these detectors in
characterizing atomic features, including vacancies, as suggested in
simulations. Phase and amplitude modification of the electron beam provides
another process handle to change image contrast in a 4D-STEM experiment. We
demonstrate how tailored electron beams can enhance signals from short-range
order and how a vortex beam can be used to characterize local symmetry.
