Skip to main content

Equivariant convolutional neural networks for the group E(3) of 3 dimensional rotations, translations, and mirrors.

Project description

Euclidean neural networks

Coverage Status DOI

Documentation | Code | ChangeLog | Colab

The aim of this library is to help the developement 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

@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
                  Martin Uhrin and
                  Jes Frellsen and
                  Nuri Jung and
                  Sophia Sanborn and
                  Josh Rackers and
                  Michael Bailey},
  title        = {Euclidean neural networks: e3nn},
  year         = 2020,
  publisher    = {Zenodo},
  version      = {0.4.0},
  doi          = {10.5281/zenodo.5292912},
  url          = {https://doi.org/10.5281/zenodo.5292912}
}

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 IPO@lbl.gov.

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

e3nn-0.4.0.tar.gz (358.8 kB view details)

Uploaded Source

Built Distribution

e3nn-0.4.0-py3-none-any.whl (383.7 kB view details)

Uploaded Python 3

File details

Details for the file e3nn-0.4.0.tar.gz.

File metadata

  • Download URL: e3nn-0.4.0.tar.gz
  • Upload date:
  • Size: 358.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.12

File hashes

Hashes for e3nn-0.4.0.tar.gz
Algorithm Hash digest
SHA256 a0ed619348874788b9c0f3ab6922b57337c17c8d46856103eaabc99ef6aea42e
MD5 dedd6a56ea6509e779a679967de2b048
BLAKE2b-256 1c88418c3b548d11ab582410f5404f93ebfc532f4134ffbc2dc56d709db01b8e

See more details on using hashes here.

File details

Details for the file e3nn-0.4.0-py3-none-any.whl.

File metadata

  • Download URL: e3nn-0.4.0-py3-none-any.whl
  • Upload date:
  • Size: 383.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.8.1 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.3 CPython/3.8.12

File hashes

Hashes for e3nn-0.4.0-py3-none-any.whl
Algorithm Hash digest
SHA256 fc7a213380035bda45f29317606ab3422504f4726562c0a4dc8a133390c63757
MD5 295395e4d35403aec225e2ed48026c9b
BLAKE2b-256 a4dae97c1c185facba5173dabb4efbe723656dc0888df02661889b5b8f0a6848

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page