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Evaluation of the Rate Constants of Reactions of Phenyl Radicals with Hydrocarbons with the Use of Artificial Neural Network

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Current Approaches in Applied Artificial Intelligence (IEA/AIE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9101))

Abstract

This paper discusses the use of feed-forward artificial neural network to predict the rate constants of organic molecules in the bimolecular radical reactions Ph + R1H in the liquid phase on the experimental data. The hybrid algorithm of calculation of rate constants of bimolecular radical reactions on the experimental thermochemical data and an empirical index of the reactionary center is offered. This algorithm uses an artificial neural network for prediction of classical barrier of bimolecular radical reactions at a temperature of 333 K, a database of experimental characteristics of reaction and Arrhenius’s formula for calculation of rate constant. Results of training and prediction of the network are discussed. Results of comparison of logarithms of the calculated and experimental rate constants are given.

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Authors and Affiliations

  1. Laboratory of Information Support for Research, Institute of Problems of Chemical Physics RAS, 142432, Chernogolovka, Semenov ave, 1, RUSSIAN FEDERATION

    V. E. Tumanov & B. N. Gaifullin

Authors
  1. V. E. Tumanov
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  2. B. N. Gaifullin
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Corresponding author

Correspondence to V. E. Tumanov .

Editor information

Editors and Affiliations

  1. Texas State University, San Marcos, Texas, USA

    Moonis Ali

  2. Dongguk University, Seoul, Korea, Republic of (South Korea)

    Young Sig Kwon

  3. Dongguk University, Seoul, Korea, Republic of (South Korea)

    Chang-Hwan Lee

  4. Dongguk University, Seoul, Korea, Republic of (South Korea)

    Juntae Kim

  5. Seoul National University, Seoul, Korea, Republic of (South Korea)

    Yongdai Kim

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© 2015 Springer International Publishing Switzerland

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Tumanov, V.E., Gaifullin, B.N. (2015). Evaluation of the Rate Constants of Reactions of Phenyl Radicals with Hydrocarbons with the Use of Artificial Neural Network. In: Ali, M., Kwon, Y., Lee, CH., Kim, J., Kim, Y. (eds) Current Approaches in Applied Artificial Intelligence. IEA/AIE 2015. Lecture Notes in Computer Science(), vol 9101. Springer, Cham. https://doi.org/10.1007/978-3-319-19066-2_38

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  • DOI: https://doi.org/10.1007/978-3-319-19066-2_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19065-5

  • Online ISBN: 978-3-319-19066-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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