A COSMO-RS based guide to analyze/quantify the polarity of ionic liquids and their mixtures with organic cosolvents

A COSMO-RS descriptor (S sigma-profile) has been used in quantitative structure-property relationship (QSPR) studies by a neural network (NN) for the prediction of empirical solvent polarity E-T(N) scale of neat ionic liquids (ILs) and their mixtures with organic solvents. S sigma-profile is a two-dimensional quantum chemical parameter which quantifies the polar electronic charge of chemical structures on the polarity (sigma) scale. Firstly, a radial basis neural network exact fit (RBNN) is successfully optimized for the prediction of E-T(N), the solvatochromic parameter of a wide variety of neat organic solvents and ILs, including imidazolium, pyridinium, ammonium, phosphonium and pyrrolidinium families, solely using the S sigma-profile of individual molecules and ions. Subsequently, a quantitative structure-activity map (QSAM), a new concept recently developed, is proposed as a valuable tool for the molecular understanding of IL polarity, by relating the E-T(N) polarity parameter to the electronic structure of cations and anions given by quantum-chemical COSMO-RS calculations. Finally, based on the additive character of the S sigma-profile descriptor, we propose to simulate the mixture of IL-organic solvents by the estimation of the S-sigma-profile(Mixture) descriptor, defined as the weighted mean of the S sigma-profile values of the components. Then, the E-T(N) parameters for binary solvent mixtures, including ILs, are accurately predicted using the S-sigma-profile(Mixture) values from the RBNN model previously developed for pure solvents. As result, we obtain a unique neural network tool to simulate, with similar reliability, the E-T(N) polarity of a wide variety of pure ILs as well as their mixtures with organic solvents, which exhibit significant positive and negative deviations from ideality.