Published in
American Institute of Physics, The Journal of Chemical Physics, 22(139), p. 224104
DOI: 10.1063/1.4834075
Links
- ORCID
- American Institute of Physics
- [arxiv.org] | PDF
- [hdl.handle.net] | PDF
- [cloudfront.escholarship.org] | PDF
- [dft.uci.edu] | PDF
- [arxiv.org] | PDF
- [hdl.handle.net] | PDF
- [cloudfront.escholarship.org] | PDF
- [pubman.mpdl.mpg.de] | PDF
Tools
Orbital-free bond breaking via machine learning
,
Katja Hansen,
Leo Blooston,
Klaus-Robert Müller,
Kieron Burke
Full text: Download
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.