Orbital-free Bond Breaking via Machine Learning (bibtex)
by John C. Snyder, Matthias Rupp, Katja Hansen, Leo Blooston, Klaus-Robert Müller, Kieron Burke
Abstract:
Machine learning is used to approximate the kinetic energy of one dimensional diatomics as a functional of the electron density. The functional can accurately dissociate a diatomic, and can be systematically improved with training. Highly accurate self-consistent densities and molecular forces are found, indicating the possibility for ab-initio molecular dynamics simulations.
Reference:
Orbital-free Bond Breaking via Machine Learning John C. Snyder, Matthias Rupp, Katja Hansen, Leo Blooston, Klaus-Robert Müller, Kieron Burke, J. Chem. Phys. 139, 224104 (2013).
Bibtex Entry:
@article{SRHB13,
	Pub-num = {144},
	Abstract = {Machine learning is used to approximate the kinetic energy of one dimensional diatomics as a functional of the electron density. The functional can accurately dissociate a diatomic, and can be systematically improved with training. Highly accurate self-consistent densities and molecular forces are found,
indicating the possibility for ab-initio molecular dynamics simulations.},
	Author = {John C. Snyder and Matthias Rupp and Katja Hansen and Leo Blooston and Klaus-Robert M\"uller and Kieron Burke},
	Journal = {J. Chem. Phys.},
	volume = {139},
    issue = {22},
    pages = {224104},
    year = {2013},
    doi = {10.1063/1.4834075},
	Title = {Orbital-free Bond Breaking via Machine Learning},
	Url = {http://scitation.aip.org/content/aip/journal/jcp/139/22/10.1063/1.4834075},
	keywords = {ML}
	}
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