Predicting the Thermal Conductivity of Ionic Liquids Using a Quantitative Structure–Property Relationship

Thermal conductivity (λ) is an extremely crucial indicator of the heat transfer capability of ionic liquids (ILs) and plays a critical function in their industrial applications. In this study, there are two descriptors for model construction, namely, the charge density distribution area of ions at a specific interval (Sσi) obtained using the conductor-like screening model for the segment activity coefficient (COSMO-SAC) and the cavity volume of ILs (Vcosmo). Using the multiple linear regression (MLR) approach, a quantitative structure–property relationship (QSPR) model was proposed to describe the thermal conductivity of ILs. Furthermore, 606 experiment data points for 44 ILs at different temperatures and pressures were collected from the literature, which were randomly divided into a training set and a testing set for feasibility analysis. For the model built by the total data, its determination coefficients (R2), root mean square error (RMSE), and average absolute relative deviation (AARD) are 0.9713, 0.004304, and 2.18%, respectively; thus, the developed λ-QSPR model offers a relatively good prediction of λ for ILs. Meanwhile, the percentage of extraterritorial points in the model’s application domain (AD) analysis is only 3.80% and the double extraterritorial region is blank. Overall, the proposed model reproduces the change of λ with temperature (T) and pressure (p) well and outperforms other models of similar type. Moreover, it provides an effective approach to predicting the thermal conductivity of ILs.