Coarse-grained models for ionic liquids and applications to biological and electrochemical systems

Abstract

Ionic liquids (ILs) are a class of molten salts with a collection of exciting properties and have been employed for wide-ranging applications across chemistry, biology, and materials science. However, the high viscosity of ionic liquids challenges atomistic molecular dynamics (MD) simulations in studying their structure–property relationships on large spatiotemporal scales. Coarse-grained (CG) models provide insight into the microscopic structure and intermolecular interactions underlying various properties by eliminating unnecessary atomic details. The general protocol for proposing a new CG model is reviewed, including determination of CG representation and force field (FF) parameterization. Recent advances in polarizable CG models were discussed with the emphasis on Drude oscillators and QM-based polarizable models. An overview was given on some recent applications of machine learning (ML) techniques on development of CG potentials, including the utilization of an ML surrogate model for FF parameterization and the development of ML potentials. Applications and challenges of IL CG models in treating complex systems, including pure solvents, mixtures, biological systems, and electrochemically confined environments, were presented. Finally, prospects for the development of transferable IL CG models are highlighted to extend the applicability to more mesoscopic systems.

Keywords: Ionic liquids; Coarse-grained models; Polarization effect; Machine learning; Molecular dynamics simulation.