Title: Improving the Accuracy of DFT Calculation for Homolysis Bond Dissociation Energies of Y—NO Bond via Back Propagation Neural Network Based on Mean Impact Value
Abstract:The back propagation neural network(BPNN) approach based on mean impact value(MIV)(MIV-BPNN) was used to improve the accuracy of density functional theory(DFT) calculation for homolysis bond dissociat...The back propagation neural network(BPNN) approach based on mean impact value(MIV)(MIV-BPNN) was used to improve the accuracy of density functional theory(DFT) calculation for homolysis bond dissociation energies of Y—NO bond.Quantum chemistry calculations and MIV-BPNN were used jointly to calculate the homolysis bond dissociation energy(BDE) of 92 Y—NO organic molecular systems.The results show that compared to a single density functional theory B3LYP/6-31G(d) approach,full parameters BPNN approach reduces the root-mean-square(RMS) of the calculated homolysis BDE of 92 organic molecules from 22.25 kJ/mol to 1.84 kJ/mol and MIV-BPNN approach further reduces the RMS to 1.36 kJ/mol.It is clear that the combined B3LYP/6-31G(d) and MIV-BPNN approach can improve the accuracy of the homolysis BDE calculation in quantum chemistry and can predict homolysis BDE which can not be obtained experimentally.Read More
Publication Year: 2012
Publication Date: 2012-01-01
Language: en
Type: article
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Cited By Count: 1
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