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Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Open accessAbstract
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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“Initial mean absolute errors (MAE) ,using the coulomb matrix representation,176 were around 10 kcal/mol.”
From QSAR without borders (2020) · cites this paper as [176]
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“QM7 contains 7165 energy-minimized molecules consisting of up to 7 heavy atoms and several properties computed with density functional theory (DFT)-.”
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“Rupp et al.208 used kernel ridge regression for modeling molecular atomization energies with mean absolute error of ~10 kcal/mol.”
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Metadata
- Authors
- Rupp, M., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A.
- Affiliations
- University of California, Los Angeles (US)
- Max Planck Society (DE)
- 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
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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