Predicting viscosity of ionic liquids-water mixtures by bridging UNIFAC modeling with interpretable machine learning

Predicting the viscosity of ionic liquids (ILs)-water mixtures precisely is considerable for diversity applications in chemical industries. In this work, interpretable machine learning models incorporate physics information developed to link the UNIversal quasi-chemical Functional group Activity Coefficient (UNIFAC) models as new predictive tools for the viscosity of ILs-water mixtures. Machine learning algorithms including Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), K-Nearest-Neighbour Regression (KNNR) and Random Forest (RF) are compared, with the Catboost model achieving the best accu-racies (RMSE = 0.0084, MSE = 0.0001, MAE = 0.0020, R2 = 0.9941). We demonstrate that the UNIFAC model and the Stokes-Einstein relation assisted to reduce the feature dimensionality, and improve the predictive power of the viscosity of ILs-water mixtures. The describe of the Shapley's additive explanations (SHAP) and Partial dependence plots (PDP) are giveing expression to the features importance of the UNIFAC model. Our work created a new paradigm to hyperlink the machine learning models with ILs-related properties, the UNIFAC model contains prior physical information to optimize the features of the experiment, and fill the gap of the input features and the viscosity of ILs-water mixtures.