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Int. J. Mol. Sci. 2011, 12(4), 2242-2261; doi:10.3390/ijms12042242
Article

Improving the Accuracy of Density Functional Theory (DFT) Calculation for Homolysis Bond Dissociation Energies of Y-NO Bond: Generalized Regression Neural Network Based on Grey Relational Analysis and Principal Component Analysis

1
, 1
, 2
, 2
, 1,2,*  and 1,*
1 Institute of Functional Material Chemistry, Faculty of Chemistry, Northeast Normal University, Changchun, 130024, China 2 School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130017, China
* Authors to whom correspondence should be addressed.
Received: 2 February 2011 / Revised: 21 February 2011 / Accepted: 31 March 2011 / Published: 1 April 2011
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Abstract

We propose a generalized regression neural network (GRNN) approach based on grey relational analysis (GRA) and principal component analysis (PCA) (GP-GRNN) to improve the accuracy of density functional theory (DFT) calculation for homolysis bond dissociation energies (BDE) of Y-NO bond. As a demonstration, this combined quantum chemistry calculation with the GP-GRNN approach has been applied to evaluate the homolysis BDE of 92 Y-NO organic molecules. The results show that the full-descriptor GRNN without GRA and PCA (F-GRNN) and with GRA (G-GRNN) approaches reduce the root-mean-square (RMS) of the calculated homolysis BDE of 92 organic molecules from 5.31 to 0.49 and 0.39 kcal mol−1 for the B3LYP/6-31G (d) calculation. Then the newly developed GP-GRNN approach further reduces the RMS to 0.31 kcal mol−1. Thus, the GP-GRNN correction on top of B3LYP/6-31G (d) can improve the accuracy of calculating the homolysis BDE in quantum chemistry and can predict homolysis BDE which cannot be obtained experimentally.
Keywords: Y-NO bond; homolysis bond dissociation energy; density functional theory; grey relational analysis; principal component analysis; generalized regression neural network Y-NO bond; homolysis bond dissociation energy; density functional theory; grey relational analysis; principal component analysis; generalized regression neural network
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Li, H.Z.; Tao, W.; Gao, T.; Li, H.; Lu, Y.H.; Su, Z.M. Improving the Accuracy of Density Functional Theory (DFT) Calculation for Homolysis Bond Dissociation Energies of Y-NO Bond: Generalized Regression Neural Network Based on Grey Relational Analysis and Principal Component Analysis. Int. J. Mol. Sci. 2011, 12, 2242-2261.

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