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.
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 ull-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.
authors: Institute of Functional Material Chemistry, Faculty of Chemistry, Northeast Normal University, Changchun, 130024, China; E-Mails: lihz857@nenu.edu.cn (H.Z.L.); taow587@nenu.edu.cn (W.T.).
authors: Hong Zhi
authors: HZ
authors: Li
authors: Wei
authors: W
authors: Tao
authors: Ting
authors: T
authors: Gao
authors: Hui
authors: H
authors: Li
authors: Ying Hua
authors: YH
authors: Lu
authors: Zhong Min
authors: ZM
authors: Su
mesh_terms: D008958:Models, Molecular; D009569:Nitric Oxide / Q000737:chemistry; D025341:Principal Component Analysis; D011789:Quantum Theory; D013816:Thermodynamics
chemical_list: D009569:Nitric Oxide