Determination of Resonant State Parameters from Ab Initio Data by Artificial Neural Network and Statistical Pade Approximation

Article

Determination of Resonant State Parameters from Ab Initio Data by Artificial Neural Network and Statistical Pade Approximation

Pichl, L.; Horáček, J.

Plasma Fusion Res. N, 000 (2014)

Abstract

Resonant states in electron molecule collisions mediate a variety of energy transfer processes in the plasma
edge region, such as vibrational excitation, dissociative recombination, dissociative attachment, associative detachment
etc. Here we show that the resonance parameters, in general dicult to obtain, can be computed from
standard bound-state ab initio data by means of analytical continuation in the coupling constant. The procedure
uses artificial neural network and statistical Pade approximation to extrapolate from the bound-state region to that
of the resonant state by varying the strength of the attractive potential term added. We present benchmark data
for the ethylene molecule and demonstrate a resonable stability of the results over the quantum chemical basis
sets employed.

Keywords

fusion edge plasma, electron-molecule collision, resonance energy, resonance width, artificial neural network, statistical Pade approximation

Institute authors

Horáček, Jiří

BibTeX

@article{UTF534,
  author        = {Pichl, L. and Horáček, J.},
  title         = {{Determination of Resonant State Parameters from Ab Initio Data by Artificial Neural Network and Statistical Pade Approximation}},
  journal       = {Plasma Fusion Res.},
  volume        = {N},
  pages         = {000},
  year          = {2014},
  doi           = {10.1585/pfr.4.000},
}