Predictive modeling on the surface tension and viscosity of ionic liquid-organic solvent mixtures via machine learning

Abstract

Backgrounda comprehensive collection of reliable open-source data was compiled, encompassing 3454 data points for surface tension and 28,548 data points for viscosity of IL-organic solvent mixtures. Methodspredictive modeling was conducted for these two crucial properties using a combination of the GC method and three well-known machine learning algorithms: Artificial Neural Network (ANN), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). The modeling results demonstrate that the GC models, employing all three machine learning algorithms, offer dependable predictions. Significant FindingsThe XGBoost-based model exhibits the most superior performance in surface tension predictions, achieving a square correlation coefficient (R2) of 0.9829 and a mean absolute error (MAE) of 0.0007 N/m for the randomly generated test dataset. In contrast, the ANN-based model with optimized neurons delivers the best predictive performance in viscosity, achieving an R2 of 0.9582 and an MAE of 0.0975 Pa·s. Furthermore, the Shapley Additive Explanations (SHAP) analysis is performed to evaluate the impact of each feature on the prediction of surface tension and viscosity in IL-organic solvent binary mixtures. The results reveal that the mole fraction of IL has the greatest positive effect on the prediction for both surface tension and viscosity.