Skip to main content
Open Access Publications from the University of California

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 Commons Attribution 4.0 International Public License

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.

Many UC-authored scholarly publications are freely available on this site because of the UC Academic Senate's Open Access Policy. Let us know how this access is important for you.

Main Content
Current View