Abstract
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.
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