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Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess Smidt, Boris Kozinsky
This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.
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Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J. P., Kornbluth, M., Molinari, N., Smidt, T., & Kozinsky, B. (2022). E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nature Communications, 13(1), 2453. https://doi.org/10.1038/s41467-022-29939-5
Batzner, Simon, et al. "E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials." Nature Communications, vol. 13, no. 1, 2022, pp. 2453, https://doi.org/10.1038/s41467-022-29939-5.
Batzner, Simon, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess Smidt, and Boris Kozinsky. "E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials." Nature Communications 13, no. 1 (2022): 2453. https://doi.org/10.1038/s41467-022-29939-5.
Batzner S., Musaelian A., Sun L. [и др.] E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials // Nature Communications. — 2022. — Vol. 13, № 1. — P. 2453. — URL: https://doi.org/10.1038/s41467-022-29939-5
@article{batzner2022eequivariant,
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 Smidt and Boris Kozinsky},
journal = {Nature Communications},
year = {2022},
volume = {13},
number = {1},
pages = {2453},
doi = {10.1038/s41467-022-29939-5},
url = {https://doi.org/10.1038/s41467-022-29939-5}
}TY - JOUR AU - Batzner, Simon AU - Musaelian, Albert AU - Sun, Lixin AU - Geiger, Mario AU - Mailoa, Jonathan P. AU - Kornbluth, Mordechai AU - Molinari, Nicola AU - Smidt, Tess AU - Kozinsky, Boris TI - E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials JO - Nature Communications VL - 13 IS - 1 SP - 2453 PY - 2022 DA - 2022/05/04 DO - 10.1038/s41467-022-29939-5 UR - https://doi.org/10.1038/s41467-022-29939-5 ER -
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