Orbital-free bond breaking via machine learning
- Authors
- Snyder, John C.; Rupp, Matthias; Hansen, Katja; Blooston, Leo; Mueller, Klaus-Robert; Burke, Kieron
- Issue Date
- 14-12월-2013
- Publisher
- AMER INST PHYSICS
- Citation
- JOURNAL OF CHEMICAL PHYSICS, v.139, no.22
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF CHEMICAL PHYSICS
- Volume
- 139
- Number
- 22
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/101293
- DOI
- 10.1063/1.4834075
- ISSN
- 0021-9606
- 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. (C) 2013 AIP Publishing LLC.
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- Appears in
Collections - Graduate School > Department of Brain and Cognitive Engineering > 1. Journal Articles
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