The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Machine Learning for Quantum Mechanical Properties of Atoms in Molecules
Open accessAbstract
We introduce machine learning models of quantum mechanical observables of atoms in molecules. Instant out-of-sample predictions for proton and carbon nuclear chemical shifts, atomic core level excitations, and forces on atoms reach accuracies on par with density functional theory reference. Locality is exploited within nonlinear regression via local atom-centered coordinate systems. The approach is validated on a diverse set of 9 k small organic molecules. Linear scaling of computational cost in system size is demonstrated for saturated polymers with up to submesoscale lengths.
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Cites
Molecular Electronic‐Structure Theory
Helgaker, T., Jørgensen, P., & Olsen, J. (2000) · doi:10.1002/9781119019572 · Cited by 2,594
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Rupp, M., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.058301 · Cited by 2,312
Self-citationQuantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2014) · Scientific Data · doi:10.1038/sdata.2014.22 · Cited by 1,966
Self-citationEnumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17
Ruddigkeit, L., van Deursen, R., Blum, L. C., & Reymond, J.-L. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300415d · Cited by 1,505
Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00099 · Cited by 911
Self-citation
Cited by
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
Smith, J. S., Isayev, O., & Roitberg, A. E. (2017) · Chemical Science · doi:10.1039/c6sc05720a · Cited by 1,921
Perspective: Machine learning potentials for atomistic simulations
Behler, J. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4966192 · Cited by 1,448
Four Generations of High-Dimensional Neural Network Potentials
Behler, J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00868 · Cited by 873
Physics-Inspired Structural Representations for Molecules and Materials
Musil, F., Grisafi, A., Bartók, A. P., Ortner, C., Csányi, G., & Ceriotti, M. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00021 · Cited by 523
Quantum Chemistry in the Age of Machine Learning
Dral, P. O. (2020) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.9b03664 · Cited by 417
Metadata
- Authors
- Rupp, M., Ramakrishnan, R., & von Lilienfeld, O. A.
- Venue
- The Journal of Physical Chemistry Letters
- Publisher
- American Chemical Society (ACS)
- Published
- 2015-08-10
- Volume
- 6
- Issue
- 16
- Pages
- 3309-3313
- ISSN
- 1948-7185 (ISSN-L)
- OpenAlex
- W1843396089
- Cited by
- 217
- References
- 5
- FWCI
- 6.66
- Type
- journal article
- Language
- en
- Open access
- green
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- Rupp, M., Ramakrishnan, R., & von Lilienfeld, O. A. (2015). Machine Learning for Quantum Mechanical Properties of Atoms in Molecules. The Journal of Physical Chemistry Letters. https://doi.org/10.1021/acs.jpclett.5b01456
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