摘要
Based on the density functional theory,we described here a method to investigate the quantitative relationship between nucleophilicity/basicity and HSAB-theory-based properties of compounds with lone-pair electrons.Descriptors including global softness,Fukui function,local softness and local mulliken charge were calculated at SVWN/DN~* level of DFT with PC Spartan Pro.Nucleophilicity and basicity of 28 selected compounds were classified based on intensity.BP algorithm of artificial neural network(ANN) was employed to study the relationship between the descriptors and nucleophilicity/basicity.Cross-validation was carried out to avoid the over-fitting in training of ANN.A BP network was trained to quantify the relationship between HSAB-theory-based properties and nucleophilicity/basicity of compounds with lone-pair electrons.The results show that the prediction based on the network matches with the experimental results well.The local softness and Fukui function have a better relationship with nucleophilicity and local mulliken charge than with the basicity.The trained BP network could be utilized for predicting the nucleophilicity/basicity of compounds or functional groups with lone-pair electrons.
在密度泛函理论的基础上,根据软、硬酸碱原理,通过孤对电子计算得到的各种化学性质,我们建立了一种定量研究有机化合物亲核性/碱性的方法。这些化学性质描述符包括全局柔性,Fukui方程,局部柔性,局部Mulliken电荷等,它们是通过PC Spartan Pro软件,在密度泛函理论SVWN/DN~*层面上计算得到的量。在本研究工作中,选择了28个化合物,基于计算得到的密度将它们按照亲核性/碱性进行了分类。神经网络中的BP算法被用来研究所选取描述符与亲核性/碱性之间的关系。通过交叉验证避免了在神经网络训练中可能会出现的过度拟合问题。结果显示基于所建立神经网络的预测结果与已知的实验结果符合的很好。局部柔性与Fukui方程与亲核性之间有着相当好的对应关系,而局部Mulliken电荷更好地反映了碱性。我们期望所建立和训练得到的BP网络模型可以被用来预测未知化合物和功能基团的亲核性/碱性。
基金
National Science & Technology Major Project of China(Grant No.2009ZX09501-002)
National Natural Science Foundation of China(Grant No.20802006).
