Molecular modeling annual

, Volume 1, Issue 1, pp 22–35 | Cite as

A Combined Semiempirical MO/Neural Net Technique for Estimating 13C Chemical Shifts

  • Timothy Clark
  • Guntram Rauhut
  • Andreas Breindl
ORIGINAL PAPER

Abstract

A back-propagation artificial neural net has been trained to estimate 13C chemical shifts from the results of AM1 and PM3 semiempirical MO calculations. The input descriptors include the atom-centered monopole, dipole and quadrupole moments derived from the natural atomic orbital/point charge (NAO/PC) model, the four highest bond orders to the carbon atom being considered and the elements to which these bonds are made. The resulting net estimates the chemical shifts of a test set of 156 chemical shifts with a standard deviation of less than 7 ppm from the experimental values for AM1 and slightly more for PM3.

Keywords:13C Chemical Shift, Semiempirical MO, AM1, PM3, Neural Net 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Timothy Clark
    • 1
  • Guntram Rauhut
    • 1
  • Andreas Breindl
    • 1
  1. 1.Computer-Chemie-Centrum des Instituts für Organische Chemie der Friedrich-Alexander Universität Erlangen-Nürnberg, Nägelsbachstraße 25, D - 91052 Erlangen, Germany. (clark@organik.uni-erlangen.de)DE

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