(191ab) Theory-Based Quantitative Structure-Property Relationship Models for Standard Heat of Formation Predictions

The standard heat of formation is a basic thermophysical property required in determining enthalpies of reaction and in thermodynamic stability analyses. The enthalpies of formation are important in investigating bond energies, resonance energies and the chemical bond nature. Therefore, the development of accurate structure-based estimation methods for large varieties of chemical species is greatly beneficial in enhancing our 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 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.