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- John C. Snyder
- University of California 1 Departments of Chemistry and of Physics, , Irvine, California 92697, USA
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- Matthias Rupp
- ETH Zurich 2 Institute of Pharmaceutical Sciences, , 8093 Zürich, Switzerland
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- Katja Hansen
- Fritz-Haber-Institut der Max-Planck-Gesellschaft 3 , 14195 Berlin, Germany
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- Leo Blooston
- University of California 4 Department of Chemistry, , Irvine, California 92697, USA
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- Klaus-Robert Müller
- Technical University of Berlin 5 Machine Learning Group, , 10587 Berlin, Germany
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- Kieron Burke
- University of California 1 Departments of Chemistry and of Physics, , Irvine, California 92697, USA
書誌事項
- 公開日
- 2013-12-10
- DOI
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- 10.1063/1.4834075
- 公開者
- AIP Publishing
この論文をさがす
説明
<jats:p>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.</jats:p>
収録刊行物
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- The Journal of Chemical Physics
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The Journal of Chemical Physics 139 (22), 224104-, 2013-12-10
AIP Publishing
