Visualization and Quantification of Geometric Diversity in Metal–Organic Frameworks

With
ever-growing numbers of metal–organic framework (MOF)
materials being reported, new computational approaches are required
for a quantitative understanding of structure–property correlations
in MOFs. Here, we show how structural coarse-graining and embedding
(“unsupervised learning”) schemes can together give
new insights into the geometric diversity of MOF structures. Based
on a curated data set of 1262 reported experimental structures, we
automatically generate coarse-grained and rescaled representations
which we couple to a kernel-based similarity metric and to widely
used embedding schemes. This approach allows us to visualize the breadth
of geometric diversity within individual topologies and to quantify
the distributions of local and global similarities across the structural
space of MOFs. The methodology is implemented in an openly available
Python package and is expected to be useful in future high-throughput
studies.