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Orbital-free bond breaking via machine learning

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2013

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Snyder, John C.
Hansen, Katja
Blooston, Leo
Müller, Klaus-Robert
Burke, Kieron

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https://doi.org/10.1063/1.4834075
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http://arxiv.org/abs/1306.1812

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The Journal of Chemical Physics. American Institute of Physics (AIP). 2013, 139(22), 224104. ISSN 0021-9606. eISSN 1089-7690. Available under: doi: 10.1063/1.4834075

Zusammenfassung

Using a one-dimensional model, we explore the ability of machine learning to approximate the non-interacting kinetic energy density functional of diatomics. This nonlinear interpolation between Kohn-Sham reference calculations can (i) accurately dissociate a diatomic, (ii) be systematically improved with increased reference data and (iii) generate accurate self-consistent densities via a projection method that avoids directions with no data. With relatively few densities, the error due to the interpolation is smaller than typical errors in standard exchange-correlation functionals.

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ISO 690SNYDER, John C., Matthias RUPP, Katja HANSEN, Leo BLOOSTON, Klaus-Robert MÜLLER, Kieron BURKE, 2013. Orbital-free bond breaking via machine learning. In: The Journal of Chemical Physics. American Institute of Physics (AIP). 2013, 139(22), 224104. ISSN 0021-9606. eISSN 1089-7690. Available under: doi: 10.1063/1.4834075
BibTex
@article{Snyder2013-12-14Orbit-52140,
  year={2013},
  doi={10.1063/1.4834075},
  title={Orbital-free bond breaking via machine learning},
  number={22},
  volume={139},
  issn={0021-9606},
  journal={The Journal of Chemical Physics},
  author={Snyder, John C. and Rupp, Matthias and Hansen, Katja and Blooston, Leo and Müller, Klaus-Robert and Burke, Kieron},
  note={Article Number: 224104}
}
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