Affordable Access

deepdyve-link
Publisher Website

Orbital-free bond breaking via machine learning.

Authors
  • Snyder, John C
  • Rupp, Matthias
  • Hansen, Katja
  • Blooston, Leo
  • Müller, Klaus-Robert
  • Burke, Kieron
Type
Published Article
Journal
The Journal of Chemical Physics
Publisher
American Institute of Physics
Publication Date
Dec 14, 2013
Volume
139
Issue
22
Pages
224104–224104
Identifiers
DOI: 10.1063/1.4834075
PMID: 24329053
Source
Medline
License
Unknown

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.

Report this publication

Statistics

Seen <100 times