Prediction of ionic liquid surface tension via a generalized interpretable Structure-Surface Tension Relationship model

Abstract

Ionic liquids' (ILs) surface tension, vital in liquid interface research, faces challenges in measurement methods—time-consuming and labor-intensive. The Structure-Surface Tension Relationship (SSTR) is crucial for understanding the surface tension laws of ionic liquids, helping to predict surface tension and design ionic liquids that meet target requirements. In this study, SMILES string and group contribution methods were used to generate descriptors, and the random forest and multi-layer perceptron (MLP) models were cross combined with the two descriptor generation methods to establish the SSTR model, providing a comprehensive framework for predicting the surface tension of ionic liquids. String-MLP excels with high accuracy (R² = 0.995, RMSE = 0.686, AARD% = 0.71%) for diverse ILs' surface tension values. Meanwhile, the Shapley Additive exPlanning (SHAP) method was used to test the impact of different features on model prediction, increasing the transparency and interpretability of the model.