The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Machine learning of accurate energy-conserving molecular force fields
Open accessAbstract
The law of energy conservation is used to develop an efficient machine learning approach to construct accurate force fields.
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Cites
Generalized Gradient Approximation Made Simple
Perdew, J. P., Burke, K., & Ernzerhof, M. (1996) · Physical Review Letters · doi:10.1103/physrevlett.77.3865 · Cited by 215,824
Tkatchenko, A., & Scheffler, M. (2009) · Physical Review Letters · doi:10.1103/physrevlett.102.073005 · Cited by 6,286
Self-citationGaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Bartók, A. P., Payne, M. C., Kondor, R., & Csányi, G. (2010) · Physical Review Letters · doi:10.1103/physrevlett.104.136403 · Cited by 3,242
On representing chemical environments
Bartók, A. P., Kondor, R., & Csányi, G. (2013) · Physical Review B · doi:10.1103/physrevb.87.184115 · Cited by 2,572
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-citationHansen, K., Biegler, F., Ramakrishnan, R., Pronobis, W., von Lilienfeld, O. A., Müller, K.-R., & Tkatchenko, A. (2015) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.5b00831 · Cited by 815
Self-citationNeural network potential-energy surfaces in chemistry: a tool for large-scale simulations
Behler, J. (2011) · Physical Chemistry Chemical Physics · doi:10.1039/c1cp21668f · Cited by 781
Comparing molecules and solids across structural and alchemical space
De, S., Bartók, A. P., Csányi, G., & Ceriotti, M. (2016) · Physical Chemistry Chemical Physics · doi:10.1039/c6cp00415f · Cited by 778
Cited by
Methods for interpreting and understanding deep neural networks
Montavon, G., Samek, W., & Müller, K.-R. (2018) · Digital Signal Processing · doi:10.1016/j.dsp.2017.10.011 · Cited by 2,665
Self-citationE(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J. P., Kornbluth, M., Molinari, N., Smidt, T. E., & Kozinsky, B. (2022) · Nature Communications · doi:10.1038/s41467-022-29939-5 · Cited by 2,285
SchNet – A deep learning architecture for molecules and materials
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., & Müller, K.-R. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5019779 · Cited by 2,217
Self-citationMachine learning in materials informatics: recent applications and prospects
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0056-5 · Cited by 1,945
Unke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., & Müller, K.-R. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01111 · Cited by 1,647
Self-citation
Metadata
- Authors
- Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., & Müller, K.-R.
- Venue
- Science Advances
- Published
- 2017-05-05
- Volume
- 3
- Issue
- 5
- Pages
- e1603015-e1603015
- ISSN
- 2375-2548 (ISSN-L)
- OpenAlex
- W2585152223
- Cited by
- 1,175
- References
- 8
- FWCI
- 38.28
- Type
- journal article
- Language
- en
- Open access
- gold
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- CoChat. (2026). Citation check: Machine learning of accurate energy-conserving molecular force fields. https://app.cochat.ai/papers/10.1126/sciadv.1603015
- The paper
- Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., & Müller, K.-R. (2017). Machine learning of accurate energy-conserving molecular force fields. Science Advances. https://doi.org/10.1126/sciadv.1603015
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
