CHINESE JOURNAL OF ENERGETIC MATERIALS
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应用神经网络预测炸药撞击感度
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王国栋(1981-),男,硕士研究生,从事含能材料的分子设计与性能预测研究。e-mail: shiran1981@163.com

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Predicting the Impact Sensitivity of Explosives by Artificial Neural Network
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    摘要:

    运用密度泛函理论,在DFT-B3LYP/6-31G*的水平上对36种CHON炸药分子进行了分子优化和频率振动分析,得到了各个炸药分子量化结构参数。然后结合炸药分子的拓扑结构参数分析,最终确定电子能 、最低空轨道能量、氧平衡指数、氧原子数目、芳香性(0/1)、α-CH (0/1)、活性指数七个参数与撞击感度具有较好相关性。以这七个参数作为神经网络的输入参数,构建网络模型,得到预测集的均方根误差RMS=17.84 cm,优于分别由氧平衡指数和活性指数确定的两种传统模型,它们的均方根误差分别为42.71 cm和36.47 cm。

    Abstract:

    A method was introduced for predicting impact sensitivity of explosives by the artificial neural networks. Combining with the topological parameters and the quantum-chemical parameters which obtained by analyzing the fully optimized geometries and the vibration analysis of 36 CHON explosive molecules using the density functional theory (DFT) method at the B3LYP/6-31G* level, seven molecular descriptors close related to H50 were selected, including total electronic energy,lower unoccupied molecular orbital energy,oxygen balance index,number of oxygen atoms, active index, indicator of aromaticity (0 or 1), indicator of —CH in α (0 or 1). And the artificial neural network (ANN) with these descriptors as neurons in the input layer was established to predict impact sensitivity of explosives. The predicted data of the ANN were compared with experimental and those of two traditional models established by the oxygen balance index (OB100) and the active index (F) respectively. Results show that the root mean squares errors of ANN model is 17.84 cm and that of the two traditional models is 42.71 cm and 36.47 cm respectively.

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王国栋,刘玉存.应用神经网络预测炸药撞击感度[J].含能材料, 2008, 16(2):167-170.
WANG Guo-dong, LIU Yu-cun. Predicting the Impact Sensitivity of Explosives by Artificial Neural Network[J]. Chinese Journal of Energetic Materials, 2008, 16(2):167-170.

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  • 收稿日期: 2006-11-10
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  • 在线发布日期: 2011-05-06
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