基于密度泛函理论预测苯并咪唑类缓蚀剂的缓蚀效率  

Estimation of inhibition efficiency of benzimidazole corrosion inhibitors based on density functional theory

作　　者：堵锡华[1] 冯长君[1] 

机构地区：[1]徐州工程学院化学化工学院,江苏徐州221111

出　　处：《南京理工大学学报》2014年第3期424-430,共7页Journal of Nanjing University of Science and Technology

基　　金：国家自然科学基金(20776149);江苏省自然科学基金(09KJD150012);徐州市绿色技术重点实验室项目(SYS2012009)

摘　　要：为了研究苯并咪唑类缓蚀剂的缓蚀性能,采用密度泛函理论方法,在B3LYP/6-31G基组水平上,计算了20种苯并咪唑衍生物的量子化学参数。得到了苯并咪唑衍生物的配分函数、原子电荷等,并计算了20种苯并咪唑衍生物分子的电性拓扑状态指数。通过最佳变量子集回归分析建立这些化合物缓蚀效率(IE)的定量结构-活性相关性(QSAR)模型。结果显示,配分函数、原子电荷和电性拓扑状态指数直接影响这些化合物的IE,所建QSAR模型具有良好的鲁棒性和预测能力。将上述参数作为神经网络输入层结点,采用4∶2∶1的网络结构,得到1个反向传播神经网络模型,其相关系数为0.976,预测值与实验值的相对平均误差为2.51%。根据构建的QSAR模型设计出了IE显著提高的12种苯并咪唑类化合物分子。In order to study the inhibition performance of benzimidazole corrosion inhibitors, the quantum chemical parameters of 20 benzimidazole derivatives are calculated at the B3LYP/ 6-31G level using the density functional theory. The partition functions and atomic charges of benzimidazole inhibitors are got. The electrotopological state indices of 20 benzimidazole derivatives are calculated. The quantitative structure-activity relationship（QSAR） models of the corrosion inhibition efficiency （IE）of the compounds are established by the leaps-and-bounds regression analysis. The results show that the partition functions,atomic charges and electrotopological state indices affect the corrosion IE of these compounds directly,and the QSAR models have both favorable robustness and good prediction capability. The structural parameters mentioned above are used as the input neurons of an artificial neural network. A back-propagation neural network model is constructed with the network architecture＆nbsp;of 4 ：10 ：1. The correlation coefficient is 0. 976,and the average relative error between predicted values and experimental values is 2. 51% . Twelve new benzimidazole derivatives are designed with their corrosion IE improved obviously based on the QSAR models.

关 键 词：密度泛函理论 苯并咪唑类缓蚀剂 缓蚀效率 配分函数 原子电荷 电性拓扑状态指数 最佳变量子集回归 神经网络 

分 类 号：TB37[一般工业技术—材料科学与工程] O64[理学—物理化学]