Prediction of Vaporization Enthalpy of Pure Compounds using a Group Contribution-Based Method

Abstract

In this work, the artificial neural network–group contribution (ANN-GC) method is applied to estimate the vaporization enthalpy of pure chemical compounds at their normal boiling point. A group of 4907 pure compounds from various chemical families are investigated to propose a comprehensive and predictive model. The obtained results show the squared correlation coefficient (R2) of 0.993, root mean square error of 1.1 kJ/mol, and average absolute deviation lower than 1.5% for the estimated properties from existing experimental values.