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Artificial neural network prediction of steric hindrance parameter of polymers

Xinliang Yu, Wenhao Yu, Bing Yi, and Xueye Wang

College of Chemistry and Chemical Engineering, Hunan Institute of Engineering, Xiangtan, Hunan, 411104, China



Received: 5 October 2008  Revised: 16 November 2008  Accepted: 19 November 2008

Abstract: An artificial neural network (ANN) model for modeling and prediction of the steric hindrance parameter σ of polymers with three quantum chemical descriptors, the average polarizability of a molecule α, entropy S, and dipole moment μ, was developed. These descriptors were calculated from the monomers of the respective polymers according to the density functional theory at the B3LYP level of the theory with the 6-31G(d) basis set. Optimal conditions were obtained by adjusting various parameters by trial-and-error. Simulated with the final optimum BP neural network [3-1-1], the results show that the predicted σ values are in good agreement with the experimental ones, with the root mean square (rms) error being 0.080 (R = 0.945) for the training set, and 0.078 (R = 0.918) for the test set, which indicates that the proposed model has better predictive capability than the existing one.

Keywords: artificial neural network - DFT - QSPR - steric hindrance parameter

Full paper is available at

DOI: 10.2478/s11696-009-0036-4


Chemical Papers 63 (4) 432–437 (2009)

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