Fast and accurate modeling of molecular atomization energies with machine learning

We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schrödinger equation is mapped onto a nonlinear statistical regression problem of reduced complexity....

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Bibliographic Details
Published in:Physical review letters 2012-01, Vol.108 (5), p.058301-058301, Article 058301
Main Authors: Rupp, Matthias, Tkatchenko, Alexandre, Müller, Klaus-Robert, von Lilienfeld, O Anatole
Format: Article
Language:English
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Summary:We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schrödinger equation is mapped onto a nonlinear statistical regression problem of reduced complexity. Regression models are trained on and compared to atomization energies computed with hybrid density-functional theory. Cross validation over more than seven thousand organic molecules yields a mean absolute error of ∼10  kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves.
ISSN:0031-9007
1079-7114
DOI:10.1103/PhysRevLett.108.058301