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 R 2 of the linear MLR model is 0.988 for the entire set, while the ELM model has a higher value of R 2 = 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.