MLR Data-Driven for the Prediction of Infinite Dilution Activity Coefficient of Water in Ionic Liquids (ILs) Using QSPR-Based COSMO Descriptors.

Abstract

To predict the partial molar excess enthalpy, entropy at infinite dilution, and phase equilibria, the availability of an infinite dilution activity coefficient is vital. The "quantitative structure-activity/property relationship" (QSAR/QSPR) approach has been used for the prediction of infinite dilution activity coefficient of water in ionic liquids using an extensive data set. The data set comprised 380 data points including 68 unique ILs at a wide range of temperatures, which is more extensive than previously published data sets. Moreover, new predictive QSAR/QSPR models including novel molecular descriptors, called "COSMO-RS descriptors", have been developed. Using two different techniques of external validation, the data set was divided to the training set for the development of models and to the validation set for external validation. Unlike former available models, internal validation using leave one/multi out-cross validations (LOO-CV/LMO-CV) and Y-scrambling methods were performed on the models using statistical parameters for further assessment. According to the obtained results of statistical parameters (R² = 0.99 and Q²_LOO-CV = 0.99), the predictive capability of the developed QSPR model was excellent for training set. Regarding the external validation, other statistical parameters such as AAD = 0.283 and AARD % = 30 were also satisfactory for the validation set. While the values of γ_H2O^∞ increase or decrease with increasing temperature, the QSAR/QSPR models based on the van't Hoff equation takes into account the negative and positive effects of temperature on the γ_H2O^∞ in ILs well, depending on the nature of ILs. It was also shown that γ_H2O^∞ in some new ILs which had not been experimentally studied before can be predicted using the QSPR model.