Prediction of the Standard Enthalpy of Formation of Pure Compounds Using Molecular Structure

2009 ◽  
Vol 62 (4) ◽  
pp. 376 ◽  
Author(s):  
Farhad Gharagheizi

A predictive approach has been presented to calculate the standard enthalpy of formation of pure compounds based on a quantitative structure–property relationship technique. A large number (1692) of pure compounds were used in this study. A genetic algorithm based on multivariate linear regression was used to subset variable selection. Using the selected molecular descriptors an optimized feed forward neural network was presented to predict the ΔHfo of pure compounds.

e-Polymers ◽  
2007 ◽  
Vol 7 (1) ◽  
Author(s):  
Farhad Gharagheizi

Abstract In this study, a new neural network quantitative structure-property relationship model for prediction of θ (LCST ) of polymer solutions is presented. The parameters of this model are eight molecular descriptors which are calculated only from the chemical structure of polymer and solvent. These eight molecular descriptors were selected from 3328 molecular descriptors of polymer and solvent available in polymer solution by genetic algorithm-based multivariate linear regression (GA-MLR) technique. The obtained neural network model can predict the θ (LCST ) of 169 polymer solutions with mean relative error of 1.67% and squared correlation coefficient of 0.9736.


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