Ionic liquids (ILs) are increasingly being investigated as an alternative to flammable organic electrolytes used in current batteries due to their high stability and tunable chemical structures [1]. However, the strong intermolecular interactions which stabilize IL molecules produce highly correlated ionic environments that complicate electrolyte design. Transport models commonly used to model ion transport in liquid electrolytes have led to a framework for maximizing conductivity by minimizing viscosity in ILs [2]. However, recent studies show that many ILs deviate from hydrodynamic behavior and instead suggest the presence of ion-hopping transport mechanisms [3, 4]. To overcome the limitations of theory in understanding IL design, this work builds upon classical theory frameworks by using machine learning to identify structural and energetic motifs important for describing ion transport.
We use the SMARTS framework to define a specialized molecular fragment representation for IL conductivity. We explicitly capture the contributions of electrostatic motifs that dictate conductivity for over 200 ILs with reported experimental conductivities. We find expertise-defined molecular representations to significantly simplify modeling structure-conductivity relationships, especially in low-data regimes. Notably, we report contributions of molecular motifs to ion transport in ILs and analyze the variation in contributions under varied molecular environments. We report motifs with delocalized charges to possess the highest variability in contribution towards ion transport. This suggests that ILs with delocalized charges are the most promising for molecular tuning due to the increased relative strength of polar and apolar interactions. Overall, this work provides a survey of structure-property relationships for currently studied ionic liquid structures. This new framework provides a promising avenue for tuning IL properties and learning insights into the physical origin of transport behavior.
Baskin, I., A. Epshtein, and Y. Ein-Eli, Benchmarking machine learning methods for modeling physical properties of ionic liquids. Journal of Molecular Liquids, 2022. 351.
Nordness, O. and J.F. Brennecke, Ion Dissociation in Ionic Liquids and Ionic Liquid Solutions. Chemical Reviews, 2020. 120(23): p. 12873-12902.
Cashen, R.K., et al., Bridging Database and Experimental Analysis to Reveal Super-hydrodynamic Conductivity Scaling Regimes in Ionic Liquids. J Phys Chem B, 2022. 126(32): p. 6039-6051.
Nurnberg, P., et al., Superionicity in Ionic-Liquid-Based Electrolytes Induced by Positive Ion-Ion Correlations. Journal of the American Chemical Society, 2022. 144(10): p. 4657-4666.
