Discovering Molecular Coordination Environments for Selective Ion Binding Using Machine Learning

The design of ion-selective materials with improved separations efficacy and efficiency is paramount, as current technologies fail to meet real-world deployment challenges. In this study, we utilize a data-driven approach to investigate design features to enhance ion selectivity. We curate a dataset of 563 alkali metal coordinating molecular complexes from the Cambridge Structural Database and obtain ion binding energies from density functional theory calculations. Our analysis reveals that energetic preferences are related to ion size and 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 strength. These physical insights offer guidance toward the design of optimal membranes for ion selectivity.