Relationships between Changes in Guest Ion Properties and in the Host Framework Topology in Ionic Coordination Polymers

In
order to identify possible general relationships between changes
in the framework topology and changes in the extra framework ion properties,
a set consisting of 2202 crystal structures of ionic coordination
polymers was extracted from the Cambridge Structural Database. Changes
in ion properties served as independent variables for several machine
learning models trained to predict the changes in framework dimensionality,
topological density, and average ring size of the framework’s
tiling. The trained classifiers showed acceptable predictive performance
with F1 score in the range 0.4 ÷ 0.6 and were subjected
to the validation tests, which confirmed that they fit the data significantly
better than by chance. Subsequent feature importance analysis of the
classifiers revealed a set of the ion properties being important for
prediction of the changes in corresponding framework characteristics
in the extracted set of crystal structures. It is shown that in general
changes in molecular surface area and molecular flexibility of the
guest ions are essential for predicting changes in selected topological
characteristics of a framework. Case studies were conducted for several
sets of crystal structures with frameworks that are observed to host
significant variety of counter ions. The decision tree classifiers
allowed us to discover the ion properties determining topological
characteristics in particular frameworks.