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We develop a Stockmayer fluid model for molecular dynamics simulations of ionic liquids that captures molecular polarization, ionic conductivity, viscosity, and glass transition temperature, using ethylammonium nitrate (EAN) as an example. The ions in EAN are treated as spheres interacting via the Lennard-Jones potential with an embedded point charge and a permanent dipole moment. We show that our simulation results for EAN are consistent with experimental data and then explore the effects of the molecular parameters on the viscosity of ionic liquids. Our results indicate that viscosity monotonically increases with ionic charge and dipole moment but non-monotonically changes with ionic diameter (or molar volume). This non-monotonic trend arises from the competition among the electrostatic interactions, molecular packing, and size asymmetry between the cation and anion. Our model also shows that long-lived ion pairs result in higher viscosities. 

Abstract Ionic liquid (IL)‐containing polymers garner attention for electrochemical applications. This article overviews recent experimental and theoretical studies of polymer electrolytes that would be likely to cultivate new theoretical and computational frameworks for IL‐containing polymers. The first two sections outline the uniqueness of ILs that differentiates them from inorganic salts in polymers and explore deviation from the concept of the metaphor “room‐temperature molten salt.” Such distinct properties include (1) large intrinsic dipole moment and electronic polarizability, (2) hydrogen bonding, (3) π‐interactions, (4) a broad distribution of charges over the entire ion, and (5) the anisotropy of the ions. Moreover, the complexity of these properties substantially increases when the ions are polymerized. Indeed, their exceptional features would overcome the hurdle due to a trade‐off between ionic conductivity and mechanical robustness in inorganic salt‐doped polymers. Given these facts, the rest of the article focuses on emerging trends in the study of the dielectric response, phase separation, ion conductivity, and mechanical robustness of the polymer electrolytes, highlighting outstanding observations in experiments that may inspire existing theory and simulation. Our discussion also includes improving computational complexity for IL‐containing polymers. To this end, recent machine learning studies that consider ILs and polymer liquids are presented.