• DocumentCode
    3207089
  • Title

    Improving the Accuracy of Low Level Density Functional Theory Calculation for Absorption Energies: The Least Squares Support Vector Machine

  • Author

    Gao, Ting ; Pu, Dong-Bing ; Li, Hui ; Lu, Ying-Hua ; Li, Hai-Bin ; Li, Hong-Zhi ; Su, Zhong-Min

  • Author_Institution
    Sch. of Comput. Sci. & Inf. Technol., Northeast Normal Univ., Changchun, China
  • Volume
    2
  • fYear
    2010
  • fDate
    11-12 May 2010
  • Firstpage
    522
  • Lastpage
    525
  • Abstract
    Least squares support vector machines (LS-SVM) was introduced to improve the calculation accuracy of low level density functional theory. As a demonstration, this combined low level quantum mechanical calculation with LS-SVM correction approach has been applied to evaluate the absorption energies of 160 organic molecules. After LS-SVM correction, the RMS deviations of the calculated absorption energies reduce from 0.95 to 0.16 eV for B3LYP/STO-3G methods. And, this LS-SVM/B3LYP/STO-3G is a excellent method to predict absorption energies and can be used as an alternative to experimental measurements and high-accurate quantum chemical calculations to obtain the value of absorption energies reliably and efficiently.
  • Keywords
    absorption; chemistry computing; learning (artificial intelligence); least squares approximations; quantum chemistry; support vector machines; LS-SVM/B3LYP/STO-3G method; absorption energies; least squares support vector machine; low level density functional theory; quantum chemical calculations; quantum mechanical calculation; Absorption; Chemicals; Chemistry; Density functional theory; Least squares methods; Machine intelligence; Machine learning; Neural networks; Quantum mechanics; Support vector machines; absorption energies; quantum mechanical calculation; support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on
  • Conference_Location
    Changsha
  • Print_ISBN
    978-1-4244-7279-6
  • Electronic_ISBN
    978-1-4244-7280-2
  • Type

    conf

  • DOI
    10.1109/ICICTA.2010.862
  • Filename
    5523441