Viscosity prediction of ionic liquids using NLR and SVM approaches

Abstract

Using the molecular functional group, two alternative models for predicting the dynamic viscosity of ionic liquids (ILs) as function of temperature are presented. The group contributions were regressed by traditional nonlinear regression (NLR) and machine learning technique including support vector machine (SVM) regression. A new group contribution scheme was proposed that allows to describe a wide variety of ILs and to distinguish among isomers. The obtained models were developed using the largest experimental viscosity database assembled to date (15 251 accepted experimental data points covering 1654 distinct ILs). The hyperparameters of SVM regression model were obtained through Bayesian optimization method with k-folds cross-validation. The two proposed models were tested using n-times repeated k-folds cross-validation. The proposed models are discussed in detail according to various statistics, both in global values and in detailed values for each chemical family, and compared to other models reported in the literature. The results indicate that SVM regression model is much better than the traditional nonlinear model. For the sake of transparency, the proposed models were implemented using common spreadsheets and given in the supplementary material as Excel file.