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化学学报
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化学学报 ›› 2010, Vol. 68 ›› Issue (22): 2264-2272. 上一篇    下一篇

研究论文

自由基共聚合中单体Q-e活性参数预测

禹新良*,1,2   

  1. (1湖南工程学院化学化工学院 湘潭 411104)
    (2湘潭大学化学学院 环境友好化学与应用教育部重点实验室 湘潭 411105)
  • 投稿日期:2010-04-18 修回日期:2010-06-27 发布日期:2010-07-22
  • 通讯作者: 禹新良 E-mail:yxl@hnie.edu.cn
  • 基金资助:

    功能化离子液体负载的手性膦金属催化剂的合成及应用研究

Prediction of Q-e Parameters of Monomers in Free-radical Copolymerizations

Yu Xinliang*,1,2   

  1. (1 College of Chemistry and Chemical Engineering, Hunan Institute of Engineering, Xiangtan 411104)
    (2 Key Laboratory of Environmentally Friendly Chemistry and Applications of Ministry of Education, College of Chemistry, Xiangtan University, Xiangtan 411105)
  • Received:2010-04-18 Revised:2010-06-27 Published:2010-07-22
  • Contact: Xinliang YU E-mail:yxl@hnie.edu.cn

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Q-e方程在解释自由基共聚合中单体的活性时相当有效. 为得到可靠的Q, e活性参数结构-性能定量关系(QSPR)模型, 采用密度泛函理论(DFT)UB3LYP方法在6-31G(d)基组水平上对60种结构为CH3C1H2—C2HR3•自由基(C1H2＝C2HR3＋CH3•→CH3C1H2—C2HR3•)进行了计算. 两组包含原子电荷及前线分子轨道能级的量子化学参数分别用来建立Q, e活性参数的人工神经网络(ANN)模型. 通过试差法调整网络参数得到最佳ANN模型, 两者均为3-5-1结构. 参数Q, e预测值与实验值接近, 测试集相关系数分别为0.990 (rms＝0.269)和0.943 (rms＝0.331). 而且Q, e模型的外部验证系数 分别为0.980和0.873, 这结果显示两模型具有好的推广预测能力. 因此本工作介绍的ANN模型是精确而可靠的|从自由基CH3C1H2—C2HR3•结构获得参数预测Q及e值是可行的.

关键词: 人工神经网络, 自由基共聚合, Q-e方程, 结构-性能定量关系, 量子化学参数, 自由基

The Q-e scheme is remarkably useful in the interpretation of the reactivity of a monomer in free-radical copolymerizations. To develop reliable quantitative structure-property relationship (QSPR) models for the prediction of Q and e parameters, density functional theory (DFT) calculations were carried out for 60 radicals with structure CH3C1H2—C2HR3•|(C1H2＝C2HR3＋CH3•→CH3C1H2—C2HR3•), at the UB3LYP level of theory with 6-31G(d) basis set. Two sets of quantum chemical descriptors including atomic charges and frontier molecular orbital energies were used to develop artificial neural network (ANN) models for Q and e parameters, respectively. The final optimum ANN models were obtained by adjusting various parameters by trial-and-error, both of them have the network architectures of 3-5-1. The predicted Q and e parameters values are in good agreement with the experimental data, with test sets possessing correlation coefficients of 0.990 (rms＝0.269) for Q and 0.943 (rms＝0.331) for e. Furthermore, the external correlation coefficients of 0.980 for Q and 0.873 for e show that each of the two ANN models has true predictive ability. Thus, the present ANN models are accurate and reliable|calculating quantum chemical descriptors from radicals CH3C1H2—C2HR3•|to predict Q and e values is feasible.

Key words: artificial neural network, free-radical copolymerization, Q-e scheme, QSPR, quantum chemical descriptor, radical