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American Physical Society, Physical Review Letters, 5(108)

DOI: 10.1103/physrevlett.108.058301

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Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

Journal article published in 2011 by Matthias Rupp ORCID, Alexandre Tkatchenko, Klaus-Robert Müller, O. Anatole von Lilienfeld
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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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 Schr̈odinger equation is mapped onto a non-linear 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 small organic molecules yields a mean absolute error of ~10 kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves.