The DOI matches its Crossref record; 39 of the 41 references it cites were found in a registry or an index.
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)-.”
From QSAR without borders (2020) · cites this paper as [176]
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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.”
From QSAR without borders (2020) · cites this paper as [208]
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- B. Schölkopf, 2002, Learning with Kernels
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Neural Message Passing for Quantum ChemistryPreprint
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01212 · Cited by 5,678
Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968
MoleculeNet: a benchmark for molecular machine learning
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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106
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Schmidt, J., Marques, M. R. G., Botti, S., & Marques, M. A. L. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0221-0 · Cited by 2,786
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
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