  Much of our understanding of complex structures is based on simplification:
for example, metal-organic frameworks are often discussed in the context of
"nodes" and "linkers", allowing for a qualitative comparison with simpler
inorganic structures. Here we show how such an understanding can be obtained in
a systematic and quantitative framework, by combining atom-density based
similarity (kernel) functions and unsupervised machine learning with the
long-standing idea of "coarse-graining" atomic structure. We demonstrate how
the latter enables a comparison of vastly different chemical systems, and use
it to create a unified, two-dimensional structure map of experimentally known
tetrahedral AB2 networks - including clathrate hydrates, zeolitic imidazolate
frameworks (ZIFs), and diverse inorganic phases. The structural relationships
that emerge can then be linked to microscopic properties of interest, which we
exemplify for structural heterogeneity and tetrahedral density.
