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A Nested Molecule-independent Neural Network Approach for High-quality Potential Fits
Journal of Physical Chemistry A
  • Sergei Manzhos
  • Xiao-Gang Wang
  • Richard Dawes, Missouri University of Science and Technology
  • Tucker Carrington, Jr.

It is shown that neural networks (NNs) are efficient and effective tools for fitting potential energy surfaces. For H2O, a simple NN approach works very well. to fit surfaces for HOOH and H2CO, we develop a nested neural network technique in which we first fit an approximate NN potential and then use another NN to fit the difference of the true potential and the approximate potential. the root-mean-square error (RMSE) of the H 2O surface is 1 cm-1. For the 6-D HOOH and H2CO surfaces, the nested approach does almost as well attaining a RMSE of 2 cm -1. the quality of the NN surfaces is verified by calculating vibrational spectra. For all three molecules, most of the low-lying levels are within 1 cm-1 of the exact results. on the basis of these results, we propose that the nested NN approach be considered a method of choice for both simple potentials, for which it is relatively easy to guess a good fitting function, and complicated (e.g., double well) potentials for which it is much harder to deduce an appropriate fitting function. the number of fitting parameters is only moderately larger for the 6-D than for the 3-D potentials, and for all three molecules, decreasing the desired RMSE increases only slightly the number of required fitting parameters (nodes). NN methods, and in particular the nested approach we propose, should be good universal potential fitting tools.

Keywords and Phrases
  • Error analysis,
  • Molecular vibrations,
  • Potential energy,
  • Surface chemistry, Nested neural network technique,
  • Potential energy surfaces,
  • Potential fits,
  • Root-mean-square error (RMSE), Neural networks
Document Type
Article - Journal
Document Version
File Type
© 2006, American Chemical Society (ACS), All rights reserved.
Publication Date
Citation Information
Sergei Manzhos, Xiao-Gang Wang, Richard Dawes and Tucker Carrington. "A Nested Molecule-independent Neural Network Approach for High-quality Potential Fits" Journal of Physical Chemistry A Vol. 110 Iss. 16 (2006) p. 5295 - 5304 ISSN: 1089-5639
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