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

Hong Zhi Li 1 email, Wei Tao 1 email, Ting Gao 2 email, Hui Li 2 email, Ying Hua Lu 1,2,* email and Zhong Min Su 1,* email
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; in revised form: 21 February 2011 / Accepted: 31 March 2011 / Published: 1 April 2011
(This article belongs to the Section Physical Chemistry, Theoretical and Computational Chemistry)
PDF Full-text Download PDF Full-Text [690 KB, uploaded 1 April 2011 11:37 CEST]
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

Article Statistics

Click here to load and display the download statistics.

Cite This Article

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.

Int. J. Mol. Sci. EISSN 1422-0067 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert