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中文摘要：
将基于平均影响值（Mean impact value,MIV）的反向传播神经网络（Back propagation neural netowrk,BPNN）（MIV-BPNN）方法用于提高密度泛函理论（Density functional theory,DFT）计算Y—NO（Y=N,S,O及C）键均裂能的精度.利用量子化学计算和MIV-BPNN联合方法计算92个含Y—NO键的有机分子体系的均裂能.结果表明,相对于单一的密度泛函理论B3LYP/6-31G（d）方法,利用全参数下的BPNN方法计算92个有机分子均裂能的均方根误差从22.25 kJ/mol减少到1.84 kJ/mol,而MIV-BPNN方法使均方根误差减少到1.36 kJ/mol,可见B3LYP/6-31G（d）和MIV-BPNN联合方法可以提高均裂能的量子化学计算精度,并可预测实验上无法获取的均裂能值.
英文摘要：
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.
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