Machine learning for the prediction of viscosity of ionic liquid-water mixtures

In this work, a nonlinear model that integrates the group contribution (GC) method with a well-known machine learning algorithm, i.e., artificial neural network (ANN), is proposed to predict the viscosity of ionic liquid (IL)-water mixtures. After a critical assessment of all data points collected from literature, a dataset covering 8,523 viscosity data points of IL-H2O mixtures at different temperature (272.10K-373.15K) is selected and then applied to evaluate the proposed ANN-GC model.

The results show that this ANN-GC model with 4 or 5 neurons in the hidden layer is capable to provide reliable predictions on the viscosities of IL-H2O mixtures. With 4 neurons in the hidden layer, the ANN-GC model gives a mean absolute error (MAE) of 0.0091 and squared correlation coefficient (R2) of 0.9962 for the 6,586 training data points, and for the 1,937 test data points they are 0.0095 and 0.9952, respectively.

When this nonlinear model has 5 neurons in the hidden layer, it gives a MAE of 0.0098 and R2 of 0.9958 for the training dataset, and for the test dataset they are 0.0092 and 0.9990, respectively. In addition, comparisons show that the nonlinear ANN-GC model proposed in this work has much better prediction performance on the viscosity of IL-H2O mixtures than that of the linear mixed model.