How Temperature, Pressure, and Salt Concentration Affect Correlations in LiTFSI/EMIM-TFSI Electrolytes: A Molecular Dynamics Study

Does [Tf2N]- slither? Equivalence of cation and anion self-diffusion activation volumes in 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)amide

New high-pressure self-diffusion data are reported for the ionic liquid 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)amide ([EMIM][Tf₂N]) at pressures up to 363 MPa in the temperature range 288-348 K. The cation and anion activation volumes derived from these are found to be equal at a fixed temperature, within experimental error, in contradiction to a report in the literature that they differ significantly. Self-diffusion activation volumes derived from our earlier high-pressure diffusion studies also show equality for the respective cations and anions of bis(trifluoromethylsulfonyl)amide, tetrafluoroborate and hexafluorophosphate salts with various cations. Stokes-Einstein-Sutherland analysis and density scaling are applied to the [EMIM][Tf₂N] self-diffusion measurements and support the conclusion that pressure effects both cation and anion mass (and hence charge) transport in the same way. The density scaling parameters are consistent with the theoretical predictions of Knudsen et al. and agree with that for the viscosity, as for other ionic liquids.

Applying Classical, Ab Initio, and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries

Rechargeable batteries have become indispensable implements in our daily life and are considered a promising technology to construct sustainable energy systems in the future. The liquid electrolyte is one of the most important parts of a battery and is extremely critical in stabilizing the electrode-electrolyte interfaces and constructing safe and long-life-span batteries. Tremendous efforts have been devoted to developing new electrolyte solvents, salts, additives, and recipes, where molecular dynamics (MD) simulations play an increasingly important role in exploring electrolyte structures, physicochemical properties such as ionic conductivity, and interfacial reaction mechanisms. This review affords an overview of applying MD simulations in the study of liquid electrolytes for rechargeable batteries. First, the fundamentals and recent theoretical progress in three-class MD simulations are summarized, including classical, ab initio, and machine-learning MD simulations (section 2). Next, the application of MD simulations to the exploration of liquid electrolytes, including probing bulk and interfacial structures (section 3), deriving macroscopic properties such as ionic conductivity and dielectric constant of electrolytes (section 4), and revealing the electrode-electrolyte interfacial reaction mechanisms (section 5), are sequentially presented. Finally, a general conclusion and an insightful perspective on current challenges and future directions in applying MD simulations to liquid electrolytes are provided. Machine-learning technologies are highlighted to figure out these challenging issues facing MD simulations and electrolyte research and promote the rational design of advanced electrolytes for next-generation rechargeable batteries.