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 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.