Cited by 2,312Machine Learning in Materials ScienceAdvanced Chemical Physics StudiesComputational Drug Discovery Methods

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Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

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Rupp, Matthias, Tkatchenko, Alexandre, Müller, Klaus-Robert, von Lilienfeld, O. Anatole·2012·Physical Review Letters

DOI 10.1103/physrevlett.108.058301

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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 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.

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Authors: Rupp, M., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A.
Affiliations: University of California, Los Angeles (US)
Max Planck Society (DE)
ORCID: Rupp, Matthias
Tkatchenko, Alexandre
Müller, Klaus-Robert
Venue: Physical Review Letters
Publisher: American Physical Society (APS)
Published: 2012-01-31
Volume: 108
Issue: 5
Pages: 058301-058301
ISSN: 0031-9007 (ISSN-L)
1079-7114
DOI: 10.1103/physrevlett.108.058301
OpenAlex: W2104489082
Cited by: 2,312
References: 41
Citations by year: 2026: 143 · 2025: 224 · 2024: 260 · 2023: 227 · 2022: 225 · 2021: 308 · 2020: 326 · 2019: 194 · 2018: 178 · 2017: 93 · 2016: 42 · 2015: 32 · 2014: 25 · 2013: 25 · 2012: 8
FWCI: 13.24
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: link.aps.org/licenses/aps-default-license (vor)
link.aps.org/licenses/aps-default-accepted-manuscript-license (am, 365 d)
Locations: Physical Review Letters (publishedVersion)
PubMed (publishedVersion)
edoc (University of Basel) (acceptedVersion)
arXiv (Cornell University) (submittedVersion)
Open Repository and Bibliography (University of Luxembourg) (submittedVersion, cc-by-nc-sa)
OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
MPG.PuRe (Max Planck Society) (submittedVersion, other-oa)
arXiv (Cornell University)
edoc (University of Basel)
Funders: U.S. Department of Energy
Topics: Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Keywords: atomization energies, machine learning, organic compounds, hybrid density functional theory, regression models, nuclear charge

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Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

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