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

1email, 1email, 2email, 2email, 1,2,* email and 1,* email
Received: 2 February 2011; in revised form: 21 February 2011 / Accepted: 31 March 2011 / Published: 1 April 2011
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.
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
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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.

AMA Style

Li HZ, Tao W, Gao T, Li H, Lu YH, Su ZM. 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. International Journal of Molecular Sciences. 2011; 12(4):2242-2261.

Chicago/Turabian Style

Li, Hong Zhi; Tao, Wei; Gao, Ting; Li, Hui; Lu, Ying Hua; Su, Zhong Min. 2011. "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. 12, no. 4: 2242-2261.


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