基于平均影响值的反向传播神经网络方法用于提高密度泛函理论计算Y—NO键均裂能精度

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.

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

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.