Dynamical properties across different coarse-grained models for ionic liquids.

Area of Science:

• Computational Chemistry
• Materials Science
• Soft Matter Physics

Background:

• Room-temperature ionic liquids (RTILs) exhibit complex structural and dynamic heterogeneity due to strong electrostatic interactions.
• Developing accurate coarse-grained (CG) simulation models is crucial for understanding RTIL behavior over longer timescales.
• Existing CG models vary in their parameterization strategies, impacting their predictive power.

Purpose of the Study:

• To investigate the relationship between CG parameterization methods and the resulting dynamical properties and transferability of RTIL models.
• To compare the performance of thermodynamic-based versus structure-based CG models.
• To identify a unified approach for developing dynamically consistent CG models of RTILs.

Main Methods:

• Systematic comparison of five distinct CG models for RTILs.
• Parameterization strategies included fitting to experimental thermodynamic data and reproducing structural distribution functions.
• Analysis of structural transferability across temperatures and dynamical speedup factors relative to atomistic simulations.

Main Results:

• All five CG models demonstrated limited structural transferability with changes in temperature.
• Structure-based CG models showed more consistent cation-anion relative diffusion compared to thermodynamic-based models at a single state point.
• A unified relationship was found between short- and long-timescale dynamics across different CG models.

Conclusions:

• The choice of CG parameterization significantly influences the dynamical properties and transferability of RTIL models.
• Structure-based parameterization offers advantages for capturing cation-anion relative diffusion.
• A general route exists for constructing dynamically consistent CG models of RTILs by linking short- and long-timescale dynamics.