Robust modeling of refractive index of ionic liquids: Toward explainable and interpretable chemical structure-based machine learning frameworks

Ionic liquids (ILs) are a promising class of sustainable solvents due to their negligible vapor pressure, high thermal and chemical stability, and exceptional tunability. These unique physicochemical properties have attracted considerable attention in industrial and academic fields, particularly in catalysis, separation processes, electrochemical systems, and green chemistry. Among the key properties of ILs, the refractive index (RI) is an important optical parameter that reflects molecular interactions, purity, and solvation behavior. However, experimental determination of RI is often expensive and time-consuming, especially for large datasets involving numerous IL structures under varying conditions. In this work, three machine learning (ML) techniques, including Extra Trees (ET), Elman Neural Network (ENN), and Cascaded Forward Neural Network (CFNN), were employed to predict the RI of ILs using a comprehensive dataset containing more than 6000 experimental data points covering 483 IL structures. The CFNN model demonstrated the best performance, achieving a root mean square error (RMSE) of 0.0020 and a coefficient of determination (R 2 ) of 0.9977. Unlike some recent studies that rely on different sizes of datasets and focus primarily on predictive accuracy, the present work ensures a more comprehensive framework that integrates multiple complementary ML architectures with thorough interpretability and physical consistency analyses. In particular, this study goes beyond accuracy improvements by incorporating SHAP-based explainable artificial intelligence (XAI), detailed trend analysis, and leverage-based applicability domain evaluation to ensure both transparency and reliability of predictions. The proposed CFNN framework provides a reliable and transparent tool for accelerating IL screening, design, and application in green solvent engineering.