Dielectric constant prediction of pure organic liquids and their mixtures with water based on interpretable machine learning

Abstract

The thermodynamic properties of mixed-solvent electrolytes are functions of pressure, temperature, and composition (PTC), and are generally considered to be characterized by their dielectric constant. In this work, an interpretable dielectric constant model is proposed based on a machine learning algorithm. The model combines machine learning algorithms, Abraham Solvation Parameters (ASP) and SHapley Additive exPlanations (SHAP) methods to accurately predict the dielectric constants of pure organic liquids and their mixtures with water, the significance of each feature and its impact on the results are explained. The predictions demonstrate extremely low mean square errors, and the effect of each feature on the dielectric constant is clearly characterized. This model provides the ability to accurately predict the dielectric constant for any pure organic liquids and their mixtures with water, and can analyze the sensitivity and influence of each feature to the dielectric constant. Furthermore, this work extends the application of the Abraham solvation parameter to prediction of solution properties. The interpretability of the model will make this a great resource to direct the prediction of physical and chemical properties of materials.