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http://dx.doi.org/10.5012/bkcs.2005.26.3.399

Optimization of Neural Networks Architecture for Impact Sensitivity of Energetic Molecules  

Cho, Soo-Gyeong (Agency for Defense Development)
No, Kyoung-Tai (Research Institute of Bioinformatics & Molecular Design)
Goh, Eun-Mee (Agency for Defense Development)
Kim, Jeong-Kook (Agency for Defense Development)
Shin, Jae-Hong (Research Institute of Bioinformatics & Molecular Design)
Joo, Young-Dae (Research Institute of Bioinformatics & Molecular Design)
Seong, See-Yearl (Research Institute of Bioinformatics & Molecular Design)
Publication Information
Bulletin of the Korean Chemical Society / v.26, no.3, 2005 , pp. 399-408 More about this Journal
Abstract
We have utilized neural network (NN) studies to predict impact sensitivities of various types of explosive molecules. Two hundreds and thirty four explosive molecules have been taken from a single database, and thirty nine molecular descriptors were computed for each explosive molecule. Optimization of NN architecture has been carried out by examining seven different sets of molecular descriptors and varying the number of hidden neurons. For the optimized NN architecture, we have utilized 17 molecular descriptors which were composed of compositional and topological descriptors in an input layer, and 2 hidden neurons in a hidden layer.
Keywords
Energetic molecule; Explosives; Impact sensitivity; Neural networks; Molecular descriptor;
Citations & Related Records

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