Fast and accurate modeling of molecular atomization energies with machine learning

Matthias Rupp, Alexandre Tkatchenko, Klaus Robert Müller, O. Anatole Von Lilienfeld

Research output: Contribution to journalArticle

631 Citations (Scopus)

Abstract

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 ∼10kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves.

Original languageEnglish
Article number058301
JournalPhysical review letters
Volume108
Issue number5
DOIs
Publication statusPublished - 2012 Jan 31

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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