Heat capacity prediction of ionic liquids based on quantum chemistry descriptors

Heat capacity is an important and fundamental physicochemical property of ionic liquids (ILs). Here, a new class of quantum chemical descriptor, namely electrostatic potential surface area (S_EP) descriptor, is employed to predict the heat capacity of ILs. In this study, 2416 experimental data points (254.0-1805.7 J mol^-1 K^-1) covering a wide temperature range (223.1-663 K) were employed. Multiple linear regression (MLR) and extreme learning machine (ELM) are applied to establish the linear and nonlinear models based on the S_EP descriptors, respectively. The obtained six-parameter models show good predictive performance. The R² of the linear MLR model is 0.988 for the entire set, while the ELM model has a higher value of R²=0.999, indicating the robustness of the nonlinear model. The results suggest that the S_EP descriptors are closely related to the heat capacity of ILs and can be potentially used to predict the properties of ILs.