(508c) Computational Investigation of Ionic Liquids Nanostructure Formation at a Mesoscale

Ionic Liquids (ILs) are a new class of materials with great potential to impact many areas in industry and science. In particular, ILs are good candidates for battery electrolytes due to low volatility, moderate reactivity, low flammability, and a wider liquid range than most organic solvents, thus, enabling safe and high-energy batteries. ILs are characterized with a number of unique properties, and one of the main feature for all ILs is a self-assembling of ions into nanostructures. Existence of such solvent structure is a cause for a strong interest to ILs. Computer modeling and simulation of the ionic liquid systems with the molecular dynamics (MD) approach yields an accurate prediction of the systems’ structure, dynamics, and thermodynamic properties. However, self-assembling of IL occurs at larger than atomic scale (at meso- to macroscale) and MD application to these systems is prohibited due to extremely high computational cost. In this work a new Probability Distribution Function Coarse Grain (PDF-CG) method has been used for accurate prediction of ionic liquids nanostructure formation together with accurate prediction of thermodynamic, dynamic, and transport properties. For the first time, formation of nanostructures in bulk ionic liquid is observed at mesoscale level using computational approach. Application of PDF-CG method to ILs allows to establish structure-property relationships critical for a complete ILs characterization.