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

Abstract

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.