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  Orbital-free bond breaking via machine learning

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

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1306.1812v1.pdf (Preprint), 314KB
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arXiv:1306.1812v1 [physics.chem-ph] 7 Jun 2013
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 Creators:
Snyder, John C.1, Author
Rupp, Matthias2, 3, Author
Hansen, Katja4, Author           
Blooston, Leo5, Author
Müller, Klaus-Robert6, 7, Author
Burke, Kieron1, Author
Affiliations:
1Departments of Chemistry and of Physics, University of California, Irvine, California 92697, USA, ou_persistent22              
2Institute of Pharmaceutical Sciences, ETH Zurich, 8093 Zürich, Switzerland, ou_persistent22              
3Present address: Institute of Physical Chemistry, Department of Chemistry, University of Basel,, Klingelbergstr. 80, 4056 Basel, Switzerland., ou_persistent22              
4Theory, Fritz Haber Institute, Max Planck Society, ou_634547              
5Department of Chemistry, University of California, Irvine, California 92697, USA, ou_persistent22              
6Machine Learning Group, Technical University of Berlin, 10587 Berlin, Germany, ou_persistent22              
7Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 136-713, South Korea, ou_persistent22              

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Free keywords: Density functional theory; Laser Doppler velocimetry; Ground states; Manifolds; Dissociation energies; Bond cleavage; Machine learning; Interpolation; Molecular dynamics
 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.

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Language(s): eng - English
 Dates: 2013-06-072013-11-132013-12-102013-12
 Publication Status: Issued
 Pages: 10
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1063/1.4834075
 Degree: -

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Project name : PASCAL2 - Pattern Analysis, Statistical Modelling and Computational Learning 2
Grant ID : 216886
Funding program : Funding Programme 7 (FP7)
Funding organization : European Commission (EC)

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Title: The Journal of Chemical Physics
  Other : J. Chem. Phys.
Source Genre: Journal
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Publ. Info: Woodbury, N.Y. : American Institute of Physics
Pages: - Volume / Issue: 139 (22) Sequence Number: 224104 Start / End Page: - Identifier: ISSN: 0021-9606
CoNE: https://pure.mpg.de/cone/journals/resource/954922836226