(585a) Ion Pairing in Nanoconfined Electrolytes from Machine Learning-Based Molecular Simulations

Numerous technologies in energy storage and water treatment, from supercapacitors to filtration membranes, rely on electrolyte solutions confined to nanoscale pores or channels. While it has been established that the behavior of nanoconfined electrolytes can differ substantially from their bulk counterparts, the molecular-level structure and dynamics of these systems remain poorly understood. This is an issue exacerbated by both challenges in experimentally characterizing such systems as well as the limitations of existing simulation methodologies, namely the high cost of ab initio-level molecular simulations and the lack of classical force fields parameterized for confined systems.

Herein, we develop a machine learning-based interatomic potential to describe a prototypical nanoconfined electrolyte: aqueous NaCl in graphene slit pores. This model enables access to the long time and length scales relevant for studying confined electrolytes at density functional theory-level accuracy. We apply our machine learning model to characterize the free energy of ion pairing as a function of the degree of electrolyte confinement, finding significant changes in the electrolyte structure in highly confined systems. We demonstrate that these changes arise from (i) differences in ion solvation resulting from the steric constraints of confinement, which promote long-range structure within the electrolyte, and (ii) effects from the electronic structure of the graphene, in which polarization of the carbon weakens short-range ion-ion interactions. Importantly, the classical force fields conventionally used to simulate these systems produce qualitatively opposing trends in ion pairing, i.e., more contact ion pairing and less solvent-separated ion pairing under confinement. By illuminating the interplay of ion solvation, pore electronic structure effects, and ion-pore chemical interactions, this work offers insights into predicting and optimizing nanoconfined ion pairing for sustainable energy storage and filtration applications.

KD Fong, B Sumic, N O’Neill, C Schran, CP Grey, A Michaelides. “The interplay of solvation and polarization effects on ion pairing in nanoconfined electrolytes.” ChemRxiv, 2024; doi:10.26434/chemrxiv-2024-r67mx.