CITATION.bib

@article{batzner20223,
  title={E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials},
  author={Batzner, Simon and Musaelian, Albert and Sun, Lixin and Geiger, Mario and Mailoa, Jonathan P and Kornbluth, Mordechai and Molinari, Nicola and Smidt, Tess E and Kozinsky, Boris},
  journal={Nature communications},
  volume={13},
  number={1},
  pages={2453},
  year={2022},
  publisher={Nature Publishing Group UK London}
}
@inproceedings{kozinsky2023scaling,
  title={Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size},
  author={Kozinsky, Boris and Musaelian, Albert and Johansson, Anders and Batzner, Simon},
  booktitle={Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis},
  pages={1--12},
  year={2023}
}
@article{musaelian2023learning,
  title={Learning local equivariant representations for large-scale atomistic dynamics},
  author={Musaelian, Albert and Batzner, Simon and Johansson, Anders and Sun, Lixin and Owen, Cameron J and Kornbluth, Mordechai and Kozinsky, Boris},
  journal={Nature Communications},
  volume={14},
  number={1},
  pages={579},
  year={2023},
  publisher={Nature Publishing Group UK London}
}
@article{zhu2023fast,
  title={Fast uncertainty estimates in deep learning interatomic potentials},
  author={Zhu, Albert and Batzner, Simon and Musaelian, Albert and Kozinsky, Boris},
  journal={The Journal of Chemical Physics},
  volume={158},
  number={16},
  year={2023},
  publisher={AIP Publishing}
}
@article{geiger2022e3nn,
  title={e3nn: Euclidean neural networks},
  author={Geiger, Mario and Smidt, Tess},
  journal={arXiv preprint arXiv:2207.09453},
  year={2022}
}