(592c) Multiscale Coarse-Graining of Ionic Liquid Electrolytes to Deliver Accurate Dynamics and Transport Properties at the Mesoscale

The air transportation system is a major part of the United States and global economies. Electric aircraft that is characterized with high energy efficiency, low emissions, and reduced noise is proposed to make the nationâ??s air transportation system more efficient, safe, and sustainable. The application of new electrode materials together with alternative electrolytes based on ionic liquids (IL) have the potential to enable safe and high energy batteries. Ionic liquids are very attractive candidates for battery electrolytes because they have low volatility, moderate reactivity, low flammability, and a wider liquid range than most organic solvents. Computer modeling and simulation of the ionic liquid systems with the molecular dynamics (MD) approach yields an accurate prediction of the systemsâ?? structure, dynamics, and thermodynamic properties. However, application of the MD to the IL systems at mesoscale is limited and in most cases is prohibited due to extremely high computational cost. As an alternative, the coarse-grained molecular dynamics (CGMD) approaches may be used. In CGMD the number of degrees of freedom in the systems is significantly reduced by combining multiple atoms into a single coarse-grain (CG) particle. These approaches are successfully used for an accurate prediction of structure and thermodynamic properties. However, CGMD methods are not widely applied to the IL systems due to lack of accurate dynamic properties prediction without additional treatments. In this work a new Probability Distribution Function Coarse Grain (PDF-CG) method is applied to the system of ionic liquids to recover its dynamics properties. It is shown that application of the PDF-CG method accurately captures dynamics of the IL system in addition to the accurate prediction of the structure and thermodynamic properties. PDF-CG method may be successfully applied to the systems where accurate representation of dynamics is essential to advance computational capability up to the mesoscale.