Coupling machine learning and group contribution method to predict density and viscosity of ionic liquid-inorganic solvent-water ternary mixtures

Accurate prediction of the physical properties of mixed systems is essential for efficient industrial design and process optimization. This study explores the use of a hybrid approach that combines the group contribution (GC) method with advanced machine learning (ML) algorithms to predict the density and viscosity of ternary mixtures comprising ionic liquids (ILs), inorganic solvents (ICs), and water. The machine learning algorithms employed include artificial neural networks (ANN), XGBoost, and LightGBM. A comprehensive dataset was compiled, encompassing 5738 density data points for 25 classes of ILs and 34 classes of ICs, and 1551 viscosity data points for 12 classes of ILs and 16 classes of ICs. The results indicate that all three ML-GC models significantly outperformed traditional GC models without ML algorithms. Notably, the XGBoost-GC model exhibited the highest prediction accuracy for density, with an R2 of 0.9983, while the ANN-GC model with 7 neurons in the hidden layer delivered the best predictions for viscosity, with an R2 of 0.9967. Additionally, the Shapley Additive Interpretation (SHAP) analysis reveals that the molar fractions of water and IC primarily influence density, whereas the presence of the IC anion NO3 and temperature significantly impact viscosity. Moreover, a practical example is provided to illustrate the real-world applicability of the ML-GC models developed in this work, highlighting their potential to enhance industrial processes through precise property predictions.