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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

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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 (CC BY 3.0).
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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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