• DocumentCode
    2133055
  • Title

    Accurate Prediction of Transition Energies in Organic Molecules

  • Author

    Ting Gao ; Hui Li ; Ying-hua Lu ; Hai-Bin Li ; Hong-Zhi Li ; Zhong-Min Su

  • Author_Institution
    Sch. of Comput. Sci. & Inf. Technol., Northeast Normal Univ., Changchun, China
  • fYear
    2010
  • fDate
    18-22 Aug. 2010
  • Firstpage
    479
  • Lastpage
    482
  • Abstract
    Least squares support vector machines (LSSVM) has been carried out in order to obtain a statistically meaningful analysis of the extended set of molecules. The combined HF with LSSVM correction approach (LSSVM/HF) has been applied to evaluate the transition energies of organic molecules. After LSSVM correction, the RMS deviations of the calculated transition energies reduce from 0.91 to 0.26 eV for HF methods. And, this LSSVM/HF is a excellent method to predict transition energies and extend the reliably and efficiently of calculated transition energies.
  • Keywords
    chemistry computing; statistical analysis; support vector machines; HF methods; RMS deviations; least squares support vector machines; organic molecules; statistically meaningful analysis; transition energy prediction; Accuracy; Artificial neural networks; Hafnium; Heating; Support vector machines; Testing; Training; HF; Least squares support vector machines; transition energies;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Frontier of Computer Science and Technology (FCST), 2010 Fifth International Conference on
  • Conference_Location
    Changchun, Jilin Province
  • Print_ISBN
    978-1-4244-7779-1
  • Type

    conf

  • DOI
    10.1109/FCST.2010.9
  • Filename
    5575527