Discovering Molecular Coordination Environment Trends for Selective Ion Binding to Molecular Complexes Using Machine Learning

The
design of ion-selective materials with improved separation
efficacy and efficiency is paramount, as current technologies fail
to meet real-world deployment challenges. Selectivity in these materials
can be informed by local ion binding in confined membrane ion channels.
In this study, we utilize a data-driven approach to investigate design
features in small molecular complexes coordinating ions as simplified
models of ion channels. We curate a data set of 563 alkali metal coordinating
molecular complexes (i.e., with Li+, Na+, or
K+) from the Cambridge Structural Database and calculate
differential ion binding energies using density functional theory.
Using this information, we probe when and why structures favor exchange
with alternate ions. Our analysis reveals that energetic preferences
are related to ion size but are largely due to chemical interactions
rather than structural reorganization. We identify unique trends in
the selectivity for Li+ over other alkali ions, including
the presence of N coordination atoms, planar coordination geometry,
and small coordinating ring sizes. We use machine learning models
to identify the key contributions of both geometric and electronic
features in predicting selective ion binding. These physical insights
offer preliminary guidance into the design of optimal membranes for
ion selectivity.