Orbital-free bond breaking via machine learning

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...

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Bibliographic Details
Published in:The Journal of chemical physics 2013-12, Vol.139 (22), p.224104-224104
Main Authors: Snyder, John C, Rupp, Matthias, Hansen, Katja, Blooston, Leo, Müller, Klaus-Robert, Burke, Kieron
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Computer Simulation
Flux density
Forecasting
Interpolation
Kinetic energy
Machine learning
One dimensional models
Quantum Theory
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Summary: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.
ISSN:0021-9606
1089-7690
DOI:10.1063/1.4834075