Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
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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 using the Coulomb matrix representation for QM7 were around 10 kcal/mol.
“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]
QM7 benchmark contains 7165 energy-minimized molecules consisting of up to 7 heavy atoms and several properties computed with DFT.
“QM7 contains 7165 energy-minimized molecules consisting of up to 7 heavy atoms and several properties computed with density functional theory (DFT)-.”
From QSAR without borders (2020) · cites this paper as [176]
Rupp et al. used kernel ridge regression for modeling molecular atomization energies with mean absolute error of ~10 kcal/mol.
“Rupp et al.208 used kernel ridge regression for modeling molecular atomization energies with mean absolute error of ~10 kcal/mol.”
From QSAR without borders (2020) · cites this paper as [208]
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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)
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- atomization energies, machine learning, organic compounds, hybrid density functional theory, regression models, nuclear charge
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