Skip to content
Publicly Available Published by De Gruyter August 31, 2013

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

  • Farhad Gharagheizi EMAIL logo
From the journal e-Polymers

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.

Published Online: 2013-8-31
Published in Print: 2007-12-1

© 2013 by Walter de Gruyter GmbH & Co.

Downloaded on 2.4.2023 from https://www.degruyter.com/document/doi/10.1515/epoly.2007.7.1.1314/html
Scroll to top button