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
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 demonst...
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doaj-b9f4f8958aa945799789358fd4f133832020-11-25T00:20:33ZengMDPI AGInternational Journal of Molecular Sciences1422-00672011-04-011242242226110.3390/ijms12042242Improving 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 AnalysisZhong Min SuYing Hua LuHui LiWei TaoTing GaoHong Zhi LiWe 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. http://www.mdpi.com/1422-0067/12/4/2242/Y-NO bondhomolysis bond dissociation energydensity functional theorygrey relational analysisprincipal component analysisgeneralized regression neural network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhong Min Su Ying Hua Lu Hui Li Wei Tao Ting Gao Hong Zhi Li |
spellingShingle |
Zhong Min Su Ying Hua Lu Hui Li Wei Tao Ting Gao Hong Zhi Li 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 Y-NO bond homolysis bond dissociation energy density functional theory grey relational analysis principal component analysis generalized regression neural network |
author_facet |
Zhong Min Su Ying Hua Lu Hui Li Wei Tao Ting Gao Hong Zhi Li |
author_sort |
Zhong Min Su |
title |
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 |
title_short |
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 |
title_full |
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 |
title_fullStr |
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 |
title_full_unstemmed |
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 |
title_sort |
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 |
publisher |
MDPI AG |
series |
International Journal of Molecular Sciences |
issn |
1422-0067 |
publishDate |
2011-04-01 |
description |
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. |
topic |
Y-NO bond homolysis bond dissociation energy density functional theory grey relational analysis principal component analysis generalized regression neural network |
url |
http://www.mdpi.com/1422-0067/12/4/2242/ |
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