The DOI matches its OpenAlex record; 4 of the 4 references it cites were found in a registry or an index.
Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions.
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Citations
Cites
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
Self-citationMachine learning for the structure–energy–property landscapes of molecular crystals
Musil, F., De, S., Yang, J., Campbell, J. E., Day, G. M., & Ceriotti, M. (2018) · Chemical Science · doi:10.1039/c7sc04665k · Cited by 216
Self-citationThe many-body expansion combined with neural networks
Yao, K., Herr, J. E., & Parkhill, J. (2017) · The Journal of Chemical Physics · doi:10.1063/1.4973380 · Cited by 120
Mapping and classifying molecules from a high-throughput structural database
De, S., Musil, F., Ingram, T., Baldauf, C., & Ceriotti, M. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0192-4 · Cited by 42
Self-citation
Cited by
Performance and Cost Assessment of Machine Learning Interatomic Potentials
Zuo, Y., Chen, C., Li, X., Deng, Z., Chen, Y., Behler, J., Csányi, G., Shapeev, A. V., Thompson, A. P., Wood, M. A., et al. (2020) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b08723 · Cited by 968
Self-citationAtomic cluster expansion for accurate and transferable interatomic potentials
Drautz, R. (2019) · Physical Review B · doi:10.1103/physrevb.99.014104 · Cited by 959
Four Generations of High-Dimensional Neural Network Potentials
Behler, J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00868 · Cited by 873
Self-citationFrom DFT to machine learning: recent approaches to materials science–a review
Schleder, G. R., Padilha, A. C. M., Acosta, C. M., Costa, M., & Fazzio, A. (2019) · Journal of Physics: Materials · doi:10.1088/2515-7639/ab084b · Cited by 852
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
- Nguyen, T. T. M., Székely, E., Imbalzano, G., Behler, J., Cśanyi, G., Ceriotti, M., Götz, A. W., & Paesani, F.
- Venue
- Apollo (University of Cambridge)
- Publisher
- University of Cambridge
- Published
- 2018-06-28
- OpenAlex
- W3100571530
- Cited by
- 167
- References
- 4
- FWCI
- 11.80
- Type
- journal article
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions. https://app.cochat.ai/papers/10.17863/cam.22160
- The paper
- Nguyen, T. T. M., Székely, E., Imbalzano, G., Behler, J., Cśanyi, G., Ceriotti, M., Götz, A. W., & Paesani, F. (2018). Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions. Apollo (University of Cambridge). https://doi.org/10.17863/cam.22160
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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.
