• DocumentCode
    2952152
  • Title

    Combined Density Functional Theory and Ensembled Elman Network Correction Approach for Electronic Excitation Energies

  • Author

    Li, Hui ; Gao, Ting ; Lu, Yinghua ; Li, Hongzhi ; Su, Zhongmin

  • Author_Institution
    Sch. of Comput. Sci. & Inf. Technol., Northeast Normal Univ., Changchun, China
  • fYear
    2011
  • fDate
    30-31 July 2011
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    An ensemble of Elman networks (EEN) is formed by bagging to enhance the performance of the individual networks. The combined density functional theory (DFT) with EEN correction approach has been applied to evaluate the electronic excitation energies of organic molecules. The EEN approach improved DFT calculation results and reduced the RMS deviations from 0.48 to 0.23 eV for the training set. For the testing set, it is reduced from 0.41 to 0.22 eV. In general, the EEN approach leads to better results and shows the good generalization ability.
  • Keywords
    physics computing; recurrent neural nets; density functional theory; discrete Fourier transforms; electronic excitation energy; ensembled Elman network correction approach; organic molecule; Accuracy; Artificial neural networks; Bagging; Context; Discrete Fourier transforms; Testing; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control, Automation and Systems Engineering (CASE), 2011 International Conference on
  • Conference_Location
    Singapore
  • Print_ISBN
    978-1-4577-0859-6
  • Type

    conf

  • DOI
    10.1109/ICCASE.2011.5997564
  • Filename
    5997564