A New Accurate Neural Network Quantitative Structure- Property Relationship for Prediction of θ (Lower Critical Solution Temperature) of Polymer Solutions

Abstract

In this study, a new neural network quantitative structure-property relationship model for prediction of ) (LCST θ 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 ) (LCST θ of 169 polymer solutions with mean relative error of 1.67% and squared correlation coefficient of 0.9736.

A QSPR model for estimation of lower flammability limit temperature of pure compounds based on molecular structure

“…This methodology has been extensively presented in the previous works of the author and the results are satisfactory [4][5][6][7][8][9][10][11].…”

“…Molecular descriptors are computed only from chemical structure of a molecule using the known mathematical algorithms. Application of this methodology to correlate various physical and chemical properties has been showed promising results [4][5][6][7][8][9][10][11]. Therefore in this study, this methodology is used to develop a molecular-based model to predict LFLT of pure compounds.…”

Prediction of upper flammability limit percent of pure compounds from their molecular structures

“…This methodology has been extensively presented in the previous works of the author and the results are satisfactory [12][13][14][15][16][17][18][19][20][21][22][23][24][25].…”

An accurate model for prediction of autoignition temperature of pure compounds

“…estimations of physical and chemical properties of different pure compounds [28][29][30][31][32][33][34][35][36][37][38][39][40][41]. These capable mathematical tools are generally applied to study the complicated systems .…”