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 journalArticle

46 Citations (Scopus)

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.

Original languageEnglish
Article number224104
JournalJournal of Chemical Physics
Volume139
Issue number22
DOIs
Publication statusPublished - 2013 Dec 14

ASJC Scopus subject areas

  • Physics and Astronomy(all)
  • Physical and Theoretical Chemistry

Fingerprint Dive into the research topics of 'Orbital-free bond breaking via machine learning'. Together they form a unique fingerprint.

  • Cite this

    Snyder, J. C., Rupp, M., Hansen, K., Blooston, L., Müller, K. R., & Burke, K. (2013). Orbital-free bond breaking via machine learning. Journal of Chemical Physics, 139(22), [224104]. https://doi.org/10.1063/1.4834075