Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9591791 | Journal of Molecular Structure: THEOCHEM | 2005 | 6 Pages |
Abstract
A novel approach to the prediction of the glass transition temperature (Tg) for high molecular polymers is presented. A new quantitative structure-property relationship (QSPR) model is obtained using Radial Basis Function (RBF) neural networks and a set of four-parameter descriptors, âMV(ter)(Rter), LF, ÎXSB and âPEI. The produced QSPR model (R2=0.9269) proved to be considerably more accurate compared to a multiple linear regression model (R2=0.8227).
Related Topics
Physical Sciences and Engineering
Chemistry
Physical and Theoretical Chemistry
Authors
Antreas Afantitis, Georgia Melagraki, Kalliopi Makridima, Alex Alexandridis, Haralambos Sarimveis, Olga Iglessi-Markopoulou,