Orbital-free bond breaking via machine learning

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

Research output: Contribution to journalArticlepeer-review

89 Citations (Scopus)


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.

Original languageEnglish
Article number224104
JournalJournal of Chemical Physics
Issue number22
Publication statusPublished - 2013 Dec 14

ASJC Scopus subject areas

  • General Physics and Astronomy
  • Physical and Theoretical Chemistry


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