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 (SEP) 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 SEP 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 SEP descriptors are closely related
to the heat capacity of ILs and can be potentially used to predict
the properties of ILs.