2023 AIChE Annual Meeting
(451b) Designing Molecular Coordination Environments for Selective Ion Binding Using Machine Learning
Authors
Shuwen Yue - Presenter, Princeton University
Aditya Nandy, Massachusetts Institute of Technology
The design of ion selective materials with improved separations efficacy and efficiency is critical for the development of novel membrane technologies in water purification and desalination. In material classes such as polymeric membranes, MOFs, 2D nanosheets, and carbon nanotubes, pore geometry and chemistry are highly tunable, for example through chemical functionalization. In this study, we utilize a data-driven approach to investigate the design knobs of material structure and chemistry which can enhance ion selectivity. We curate a dataset of 1022 alkali metal coordinating molecular complexes from the Cambridge Structural Database (CSD) and obtain ion binding energies from density functional theory (DFT) calculations. Our analysis reveals that energetic preferences correlate strongly with ion size and are largely due to enthalpic interactions rather than structural rearrangement. We then use machine learning models to identify significant features in ion coordination, such as coordination distances, electrostatic potential, and charge, that can discriminate between alkali ion species. These physical insights offer guidance toward the design of optimal membranes for ion selectivity.