Extensive Databases and Group Contribution QSPRs of Ionic Liquid Properties. 3: Surface Tension

Quantitative structure–property relationships (QSPR) for calculating temperature dependence of surface tension (σ) of ionic liquids (ILs) in terms of group contributions (GCs) is proposed and broadly presented. A statistical learning method including stepwise multiple linear regression and two machine learning methods including feed-forward artificial neural network and least-squares support vector machine was applied to express σ as a function of GCs. The models were developed using the largest experimental data compilation reported thus far (570 ILs, 1008 datasets, 6114 data points). The GC assignments, the “reference + correction” modeling scheme, as well as the model validation protocol were adopted from the previous contributions of the series [Paduszyński, K. Ind. Eng. Chem. Res. 2019, 58, 5322−5338; Paduszyński, K. Ind. Eng. Chem. Res. 2019, 58, 17049–17066]. The influence of the chemical family of both cation and anion on the quality of predictions is discussed. The potential applications of the proposed model in estimating the critical temperature of ILs are discussed. Finally, the obtained model is confronted with other methods reported in the literature. In particular, an extensive comparative analysis is presented in the case of the selected QSPRs accounting for atomic contributions and topological descriptors.