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

Volumn 12, Issue 4, 2011, Pages 2242-2261

  Li, Hong Zhi ^a   Tao, Wei ^a   Gao, Ting ^a   Li, Hui ^a   Lu, Ying Hua ^a   Su, Zhong Min ^a  

^a NORTHEAST NORMAL UNIVERSITY   (China)

Author keywords

Density functional theory; Generalized regression neural network; Grey relational analysis; Homolysis bond dissociation energy; Principal component analysis; Y NO bond

Indexed keywords

NITRIC OXIDE;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; CHEMICAL BOND; CHEMICAL STRUCTURE; CONTROLLED STUDY; DENSITY FUNCTIONAL THEORY; DISSOCIATION; GENERALIZED REGRESSION NEURAL NETWORK; GREY RELATIONAL ANALYSIS; HOMOLYSIS BOND DISSOCIATION ENERGY; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PRINCIPAL COMPONENT ANALYSIS; QUANTUM CHEMISTRY; CHEMISTRY; QUANTUM THEORY; THERMODYNAMICS;

MODELS, MOLECULAR; NITRIC OXIDE; PRINCIPAL COMPONENT ANALYSIS; QUANTUM THEORY; THERMODYNAMICS;

EID: 79953843973     PISSN: None     EISSN: 14220067     Source Type: Journal    
DOI: 10.3390/ijms12042242     Document Type: Article

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