Surface tension of binary and ternary mixtures mapping with ASP and UNIFAC models based on machine learning

Modeling predictions of surface tension for binary and ternary liquid mixtures is difficult. In this work, we propose a machine learning model to accurately predict the surface tension of binary mixtures of organic solvents-ionic liquids and ternary mixtures of organic solvents-ionic liquids-water and analytically characterize the proposed model. In total, 1593 binary mixture data points and 216 ternary mixture data points were collected to develop the machine learning model. The model was developed by combining machine learning algorithms, UNIFAC (UNIversal quasi-chemical Functional group Activity Coefficient) and ASP (Abraham solvation parameter). UNIFAC parameters are used to describe ionic liquids, and ASP is used to describe organic solvents. The effect of each parameter on the surface tension is characterized by SHAP (SHapley Additive exPlanation). We considered support vector regression, artificial neural network, K nearest neighbor regression, random forest regression, LightGBM (light gradient boosting machine), and CatBoost (categorical boosting) algorithms. The results show that the CatBoost algorithm works best, MAE = 0.3338, RMSE = 0.7565, and R-2 = 0.9946. The SHAP results show that the surface tension of the liquid decreases as the volume and surface area of the anion increase. This work not only accurately predicts the surface tension of binary and ternary mixtures, but also provides illuminating insight into the microscopic interactions between physical empirical models and physical and chemical properties.

Automated summary

Modeling predictions of surface tension for binary and ternary liquid mixtures is challenging, but this study proposes a machine learning model that accurately predicts the surface tension of such mixtures. The model combines machine learning algorithms, UNIFAC, ASP, and SHAP to analyze and characterize the effect of parameters. Among various algorithms, CatBoost performs the best, with MAE = 0.3338, RMSE = 0.7565, and R-2 = 0.9946. The SHAP results reveal that the surface tension decreases with an increase in the volume and surface area of the anion. This work not only provides accurate predictions for surface tension, but also offers insights into microscopic interactions and properties.