• DocumentCode
    534917
  • Title

    Accurate prediction of heats of formation for c1-c16 alkanes: The genetic algorithm and neural network approach with simple input descriptors

  • Author

    Gao, Ting ; Li, Hong-Zhi ; Lu, Ying-Hua

  • Author_Institution
    Sch. of Comput. Sci. & Inf. Technol., Northeast Normal Univ., Jilin, China
  • Volume
    1
  • fYear
    2010
  • fDate
    13-14 Sept. 2010
  • Firstpage
    273
  • Lastpage
    276
  • Abstract
    Recently,the combination of genetic algorithm and neural network approach(GANN) has been carried out to improve the calculation accuracy of density functional theory. In the present work, the GANN approach with three simple input descriptors is applied to improve the accuracy of B3LYP calculation for C1-C16 alkanes. The prediction result shows that GANN is a more effective and economical techniques. The mean absolute deviations of the heats of formation of C1-C16 alkanes are 13.92, 1.05 and 0.20 kal/mol for the B3LYP, G3 and GANN methods, respectively.
  • Keywords
    chemical engineering computing; density functional theory; genetic algorithms; heat of formation; neural nets; organic compounds; B3LYP calculation; C1-Cι6 alkane; GANN approach; density functional theory; economical technique; genetic algorithm; heat of formation; neural network approach; simple input descriptor; Artificial neural networks; Heating;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Natural Computing Proceedings (CINC), 2010 Second International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-7705-0
  • Type

    conf

  • DOI
    10.1109/CINC.2010.5643842
  • Filename
    5643842