Comparison of Force Fields on the Basis of Various Model Approaches—How To Design the Best Model for the [C_nMIM][NTf₂] Family of Ionic Liquids

Abstract

In this contribution, we present two new united-atom force fields (UA-FFs) for 1-alkyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide [C(n)MIM][NTf(2)] (n=1, 2, 4, 6, 8) ionic liquids (ILs). One is parametrized manually, and the other is developed with the gradient-based optimization workflow (GROW). By doing so, we wanted to perform a hard test to determine how researchers could benefit from semiautomated optimization procedures. As with our already published all-atom force field (AA-FF) for [C(n)MIM][NTf(2)] (T. Köddermann, D. Paschek, R. Ludwig, ChemPhysChem- 2007, 8, 2464), the new force fields were derived to fit experimental densities, self-diffusion coefficients, and NMR rotational correlation times for the IL cation and for water molecules dissolved in [C(2)MIM][NTf(2)]. In the manual force field, the alkyl chains of the cation and the CF3 groups of the anion were treated as united atoms. In the GROW force field, only the alkyl chains of the cation were united. All other parts of the structures of the ions remained unchanged to prevent any loss of physical information. Structural, dynamic, and thermodynamic properties such as viscosity, cation rotational correlation times, and heats of vaporization calculated with the new force fields were compared with values simulated with the previous AA-FF and the experimental data. All simulated properties were in excellent agreement with the experimental values. Altogether, the UA-FFs are slightly superior for speed-up reasons. The UA-FF speeds up the simulation by about 100 % and reduces the demanded disk space by about 78 %. More importantly, real time and efforts to generate force fields could be significantly reduced by utilizing GROW. The real time for the GROW parametrization in this work was 2 months. Manual parametrization, in contrast, may take up to 12 months, and this is, therefore, a significant increase in speed, though it is difficult to estimate the duration of manual parametrization.

Local Structure in Terms of Nearest-Neighbor Approach in 1-Butyl-3-methylimidazolium-Based Ionic Liquids: MD Simulations

“…3,[27][28][29][30] Regarding the use of molecular simulations in explaining various spectroscopic observations one has to keep in mind that the vast majority of such experimental information reports the local molecular environment around specific sites of IL cations, e.g., 1 H NMR signals, CH vibrational bands, etc. [22][23][31][32][33][34] The importance of studying the influence of the anions on the microscopic structure around a reference cation is evident in this context.…”

Dispersion and Hydrogen Bonding Rule: Why the Vaporization Enthalpies of Aprotic Ionic Liquids Are Significantly Larger than those of Protic Ionic liquids

“…[5][6][7] However,itiswell-known that the interaction in ionic liquids is governed by as ubtle energy balance between Coulomb forces,h ydrogen bonds,a nd dispersion interactions. [10][11][12] In ionic liquids the situation is more complicated. [10] It is easy to separate the two contributions to the vaporization enthalpies of alcohols through ac omparison with alkanes,p rovided that the gas phase is dominated by monomeric species.Ifweincrease the alkyl chain length in nalcohols and n-alkanes we can attribute the differences to the H-bonds present in the n-alcohols and increasing dispersion forces with increasing alkyl chain length.…”

Automated parameterization of intermolecular pair potentials using global optimization techniques

“…[15] has extended this approach by various descent methods like steepest descent, conjugate gradients, and trust region, allowing the initial parameters to be situated farther away from the optimum. GROW has proven to be a useful and reliable tool in diverse applications: test functions [16], small molecules [17], and ionic liquids [18]. Nevertheless, suitable starting parameters are necessary for convergence.…”