Euclidean neural networks

Documentation | Code | CHANGELOG | Colab

The aim of this library is to help the development of E(3) equivariant neural networks. It contains fundamental mathematical operations such as tensor products and spherical harmonics.

Installation

Important: install pytorch and only then run the command

pip install --upgrade pip
pip install --upgrade e3nn

For details and optional dependencies, see INSTALL.md

Breaking changes

e3nn is under development. It is recommanded to install using pip. The main branch is considered as unstable. The second version number is incremented every time a breaking change is made to the code.

0.(increment when backwards incompatible release).(increment for backwards compatible release)

Help

We are happy to help! The best way to get help on e3nn is to submit a Question or Bug Report.

Want to get involved? Great!

If you want to get involved in and contribute to the development, improvement, and application of e3nn, introduce yourself in the discussions.

Code of conduct

Our community abides by the Contributor Covenant Code of Conduct.

Citing

@misc{e3nn_paper,
    doi = {10.48550/ARXIV.2207.09453},
    url = {https://arxiv.org/abs/2207.09453},
    author = {Geiger, Mario and Smidt, Tess},
    keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Neural and Evolutionary Computing (cs.NE), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {e3nn: Euclidean Neural Networks},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}

@software{e3nn,
  author       = {Mario Geiger and
                  Tess Smidt and
                  Alby M. and
                  Benjamin Kurt Miller and
                  Wouter Boomsma and
                  Bradley Dice and
                  Kostiantyn Lapchevskyi and
                  Maurice Weiler and
                  Michał Tyszkiewicz and
                  Simon Batzner and
                  Dylan Madisetti and
                  Martin Uhrin and
                  Jes Frellsen and
                  Nuri Jung and
                  Sophia Sanborn and
                  Mingjian Wen and
                  Josh Rackers and
                  Marcel Rød and
                  Michael Bailey},
  title        = {Euclidean neural networks: e3nn},
  month        = apr,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {0.5.0},
  doi          = {10.5281/zenodo.6459381},
  url          = {https://doi.org/10.5281/zenodo.6459381}
}

Copyright

Euclidean neural networks (e3nn) Copyright (c) 2020, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy), Ecole Polytechnique Federale de Lausanne (EPFL), Free University of Berlin and Kostiantyn Lapchevskyi. All rights reserved.

If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at [email protected].

NOTICE. This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit others to do so.