Optimization of neural networks architecture for impact sensitivity of energetic molecules

Soo Gyeong Cho, Kyoung Tai No, Eun Mee Goh, Jeong Kook Kim, Jae Hong Shin, Young Dae Joo, Seeyearl Seong

Research output: Contribution to journalArticlepeer-review

61 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)399-408
Number of pages10
JournalBulletin of the Korean Chemical Society
Issue number3
Publication statusPublished - 2005 Mar 20

All Science Journal Classification (ASJC) codes

  • Chemistry(all)


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