E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
- DOI
- 10.1038/s41467-022-29939-5
- Published
- 2022-05-04
- Container
- Nature Communications
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41467-022-29939-5,
title = {E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials},
author = {Simon Batzner and Albert Musaelian and Lixin Sun and Mario Geiger and Jonathan P. Mailoa and Mordechai Kornbluth and Nicola Molinari and Tess E. Smidt and Boris Kozinsky},
year = {2022},
journal = {Nature Communications},
doi = {10.1038/s41467-022-29939-5},
url = {https://doi.org/10.1038/s41467-022-29939-5}
}RIS
TY - JOUR TI - E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials AU - Simon Batzner AU - Albert Musaelian AU - Lixin Sun AU - Mario Geiger AU - Jonathan P. Mailoa AU - Mordechai Kornbluth AU - Nicola Molinari AU - Tess E. Smidt AU - Boris Kozinsky PY - 2022 JO - Nature Communications DO - 10.1038/s41467-022-29939-5 UR - https://doi.org/10.1038/s41467-022-29939-5 ER -
APA
Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J. P., Kornbluth, M., Molinari, N., Smidt, T. E., & Kozinsky, B. (2022). E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nature Communications. https://doi.org/10.1038/s41467-022-29939-5
Source records
- crossref · retrieved 2026-11-10T14:55:52.408Z