Chem. J. Chinese Universities ›› 2012, Vol. 33 ›› Issue (02): 346.doi: 10.3969/j.issn.0251-0790.2012.02.024

• Physical Chemistry • Previous Articles     Next Articles

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

LI Hong-Zhi1, TAO Wei2, GAO Ting1, LI Hui1, LV Ying-Hua1,2, SU Zhong-Min2   

  1. 1. College of Computer Science and Information Technology, Northeast Normal University, Changchun 130017, China;
    2. Institute of Functional Material Chemistry, College of Chemistry, Northeast Normal University, Changchun 130024, China
  • Received:2011-05-03 Online:2012-02-10 Published:2012-01-13

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.

Key words: Y-NO bond, Homolysis bond dissociation energy, Density functional theory, Mean impact value, Back propagation neural network

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