Estimation of the Heat Capacity of Ionic Liquids: A Quantitative Structure–Property Relationship Approach

In this paper, a quantitative structure–property
relationship
model is developed using genetic function approximation (GFA) to predict
the liquid heat capacity at constant pressure (CpL) for ionic liquids at atmospheric pressure. The NIST Standard
Reference Database was used to prepare a data set of CpL data consisting of 3726 experimental data points comprised
of 82 ionic liquids. The data set was split into two subsets, with
80% of the data used as a training set and 20% as a test set. Instead
of using nonlinear modeling like artificial neural networks and a
support vector machine, the GFA method was used to determine a model
by a binary combination of descriptors rather than using single ones.
Statistical analysis of the model shows that it has an overall AARD
% of 1.70%, a coefficient of determination (R2) of 0.993, and a root mean square of error of 15.11 J mol–1 K–1.