Molecular modeling of the standard state heat of formation

The standard heat of formation is a basic thermophysical property required in determining enthalpies of reaction and in thermodynamic stability analyses. Further, the enthalpies of formation are important in investigating bond energies, resonance energies and the nature of chemical bonds. Therefore, the development of accurate structure-based estimation methods for large varieties of chemical species is greatly beneficial in enhancing capability in process and product development.In this work, quantitative structure–property relationship (QSPR) models were developed for a structurally diverse DIPPR dataset of standard heats of formation comprising 1765 pure compounds involving 82 chemical classes. We have employed both linear and nonlinear QSPR modeling techniques. The linear approach involves the use of constricted binary particle swarm optimization (BPSO) for feature selection and multiple-linear regression. In the nonlinear approach, the optimum network architecture and its associated inputs are identified using a wrapper-based feature selection algorithm combining differential evolution and artificial neural networks. Model predictions for the root-mean-square error of the BPSO and nonlinear approaches were 138 and 97 kJ/mol, respectively.

► Predictive models were developed for standard heats of formation.
► Models consisted of quantitative structure–property relationship models.
► Database comprised of 1765 compounds involving 82 chemical classes.
► Predictions for the linear model were 138 kJ/mol root-mean-square error (RMSE).
► Predictions for the non-linear model were 97 kJ/mol RMSE.