江苏师范大学学报(自然科学版)2024,Vol.42Issue(4) :47-50.DOI:10.3969/j.issn.2095-4298.2024.04.008

有机过氧化物自加速分解温度的神经网络研究

Research on the self-accelerating decomposition temperature of organic peroxides by neural network

秦正龙
江苏师范大学学报(自然科学版)2024,Vol.42Issue(4) :47-50.DOI:10.3969/j.issn.2095-4298.2024.04.008

有机过氧化物自加速分解温度的神经网络研究

Research on the self-accelerating decomposition temperature of organic peroxides by neural network

秦正龙1
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作者信息

  • 1. 江苏师范大学化学与材料科学学院,江苏徐州 221116
  • 折叠

摘要

为了研究有机过氧化物自加速分解温度(SADT)与其结构之间的定量构效关系,计算了46种有机过氧化物的分子形状指数(nK)和电性距离矢量指数(Mi).通过筛选优化,得到分子形状指数的2K、3K、4K及电性距离矢量指数的M9、M21、M26、M27、M33,共8个结构参数,并将它们作为BP神经网络的输入层变量,有机过氧化物SADT作为输出层变量,采用8:4:1的网络结构,获得了令人满意的定量结构与性质关系(QSPR)神经网络预测模型,其总相关系数为0.997,自加速分解温度的预测值与实验值的一致性令人满意,平均误差仅为0.9 ℃,优于文献方法.结果表明,有机过氧化物自加速分解温度与8种结构参数之间呈现良好的非线性关系.

Abstract

In order to study the quantitative structure-activity relationship between the self-accelerating decomposi-tion temperature(SADT)and the structure of organic peroxides,molecular shape index(nK)and electrical distance vector index(M,)of 46 organic peroxides were calculated and 8 structural parameters,namely 2K,3K and 4K of the molecular shape indices,and M9,M21,M26,M27 and M33 of the electrical distance vector indices were obtained by screening and optimization.With the eight parameters as input variables of neural network and the SADT as output variable,the 8:4:1 network structure was adopted and BP neural network method was used to establish a satisfying quantitative structure-property relationship(QSPR)prediction model.The total correlation coefficient was 0.997.The predicted values of SADT by the model were in satisfactory agreement with the experiment values,with an average er-ror of only 0.9℃,which was better than literature methods.The results showed that there was a good nonlinear relation-ship between the self-accelerating decomposition temperature and the eight molecular structure parameters.

关键词

有机过氧化物/自加速分解温度/分子形状指数/电性距离矢量指数/定量结构-性质相关性/神经网络

Key words

organic peroxide/self-accelerating decomposition temperature/molecular shape index/electrical distance vector index/quantitative structure-property relationship/neural network

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出版年

2024
江苏师范大学学报(自然科学版)
江苏师范大学

江苏师范大学学报(自然科学版)

影响因子:0.323
ISSN:1007-6573
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