基于BPNN的无机离子晶体晶格能的QSPR研究(英文)  

QSPR modeling of mineral crystal lattice energy by BPNN

作　　者：张运陶[1] 赵鑫[1] 程正军[1] 

机构地区：[1]西华师范大学应用化学研究所,四川南充637002

出　　处：《计算机与应用化学》2009年第12期1593-1597,共5页Computers and Applied Chemistry

摘　　要：以86个离子化合物的正、负离子的有效核电荷Z^(*+)、Z^(*-)、离子半径r_+、r_-,以及正离子的荷径比Z^(*+)/Υ_+5种结构参数作为自变量,以晶格能U作为因变量,采用BP神经网络建立关于无机离子晶体晶格能的结构-性质关系(QSPR)模型.该模型由输入层、隐含层和输出层构成3层BP神经网络,86个离子化合物样本则按文献分别划分为训练集和验证集.研究表明,当隐含层神经元个数为5时模型效果最佳:该模型对训练集拟合结果的决定系数R^2=0.9965,平均相对误差MRE=1.63%;对验证集预测结果的R^2=0.9952,MRE=1.85%.QSPR model of the lattice energy of mineral crystal was built by BPNN consisted of three layers, with the descriptors of Z^＊＋ （effective nuclear charges on cations）, Z^＊- （effective nuclear charges on anions）, r＋ （Goldshmidt radius of cations）, r- （Goldshmidt radius of anions） and Z^＊-/r＋86 samples of the mineral crystal were divided into training and test set as the literature. The coefficient of determination R^2 for the training set is 0.996 5 and for the test set is 0.995 2, which can be considered very satisfactory. In addition, the mean relative error MRE was within 1.63% and 1.85% for the training set and the test set respectively. The study showed that the descriptors in the model were of clear physical and chemical meaning and the predicting results were superior to those in literature evidently.

关 键 词：无机离了晶体 晶格能 结构-性质关系 BP神经网络 

分 类 号：O611.3[理学—无机化学] TP183[理学—化学]