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-  2019 

神经网络法研究硝基含能化合物的撞击感度 Research on the Impact Sensitivity of Nitro Energetic Compounds by Neural Network

Keywords: 撞击感度,硝基含能化合物,神经网络,分子结构参数,定量结构-性质相关性

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Abstract:

为了研究硝基含能化合物的撞击感度与分子结构之间的关系,基于邻接矩阵和空间结构,计算了36个硝基含能化合物的分子连接性指数X和电性拓扑状态指数E,建立了这些分子的撞击感度(lg H50)与1X、2X、4X、5X、E13、E16和E28共7种分子结构参数的定量结构-性质相关性(QSPR)模型。将这7种分子结构参数作为神经网络法的输入神经元,采用7∶2∶1的神经网络结构,建立了预测精度较好的神经网络模型,总相关系数为0.975 5。利用该模型计算得到的撞击感度预测值与其实验值的相对平均误差为4.03%,优于多元回归模型的相对平均误差(9.57%)。预测值与实验值较吻合,表明硝基含能化合物的撞击感度与7种分子结构参数具有非线性关系

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