Prediction surface tension of ionic liquid–water mixtures using a hybrid group contribution and artificial neural network method

Abstract

In this work, a database was established to collect relevant surface tension data (4578 sets of data points in total) for 138 ionic liquids (ILs)-H2O hybrid systems. An ANN-GC model for predicting the surface tension of IL-H2O hybrid systems is proposed to be constructed by combining the group contribution (GC) method and artificial neural network (ANN) algorithm. This model correlates surface tension to temperature together with IL structure and is developed by using machine learning algorithms. The results show that the model with 4, 5 and 7 neurons in the hidden layer is able to provide reliable predictions for the surface tension of the IL-H2O hybrid system. When 4, 5 and 7 neurons are used in the hidden layer of the model, the squared correlation coefficients (R2) of the training set are 0.92, 0.93 and 0.93; the MAE are 0.0021, 0.0020 and 0.0020, respectively. While the squared correlation coefficients (R2) of the test set are 0.94, 0.91,0.91 and the MAE are 0.0023, 0.0029, 0.0027, respectively. Meanwhile, the ANN-GC model of the hybrid system was extended and an ANN-GC model of pure ILs was constructed based on 172 surface tension data of pure ionic liquids. The comparison shows that the model with 4 and 5 neurons in the hidden layer can be extended to the pure IL system to provide reliable prediction of the surface tension for the pure IL system.