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Orbital-free bond breaking via machine learning

  • Author(s): Snyder, JC
  • Rupp, M
  • Hansen, K
  • Blooston, L
  • Müller, KR
  • Burke, K
  • et al.

Published Web Location

https://doi.org/10.1063/1.4834075Creative Commons Attribution 4.0 International Public License
Abstract

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. © 2013 AIP Publishing LLC.

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