基于基团贡献型描述符的QSPR预测离子液体的离子电导率和粘度

Hiroyuki Matsuda, H. Yamamoto, Kiyofumi Kurihara, K. Tochigi
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引用次数: 21

摘要

采用多项式展开模型,结合基团贡献型描述符,建立了离子液体离子电导率和离子粘度的预测模型。这些方程适用于烷基胺、吡咯、哌啶、吡啶、咪唑和吡唑阳离子与以下阴离子:TFSI、Br、Cl、PF6、BF4、CF3SO3、CF3BF3和C2F5BF3。为了确定非线性方程中的系数,采用了遗传算法。计算结果表明,离子电导率与粘度具有较好的相关性。通过将计算值与未用于确定参数的实验值进行比较,还尝试了对预测性能的检查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of The Ionic Conductivity and Viscosity of Ionic Liquids by QSPR Using Descriptors of Group Contribution Type
Predictive models for representing the ionic conductivity and viscosity of ionic liquids (ILs) were constructed using a polynomial expansion model, coupled with descriptors of group contribution type. These equations could be applied to cations of alkyl amine, pyrole, piperidine, pyridine, imidazole and pyrazole, with the following anions: TFSI, Br, Cl, PF6, BF4, CF3SO3, CF3BF3, and C2F5BF3. To determine coefficients in non-linear equations, genetic algorithms were adapted. Calculated results gave good correlation accuracy of ionic conductivity and viscosity. An examination of the predictive performance was also attempted by comparing the calculated with experimental values not used for the determination of the parameters.
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来源期刊
Journal of Computer Aided Chemistry
Journal of Computer Aided Chemistry CHEMISTRY, MULTIDISCIPLINARY-
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