Skip to main content

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

Project description

e3nn

Coverage Status DOI

Documentation | Code | ChangeLog | Colab

The aim of this library it to help the developement of E3 equivariant neural networks. It contains fundamental mathematical operations such as tensor products and spherical harmonics.

Installation

It is recommanded to install using pip. The main branch is considered as unstable.

See INSTALL.md

Previous version

e3nn has been recently refactored. The last version before refactoring can be installed with the command pip install e3nn==0.1.1

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 with Project Wanted.

Code of conduct

Our community abides by the Contributor Covenant Code of Conduct.

Citing

@software{mario_geiger_2021_4557591,
  author       = {Mario Geiger and
                  Tess Smidt and
                  Benjamin K. Miller and
                  Wouter Boomsma and
                  Kostiantyn Lapchevskyi and
                  Maurice Weiler and
                  Michał Tyszkiewicz and
                  Alby M. and
                  Bradley Dice and
                  Jes Frellsen and
                  Nuri Jung and
                  Sophia Sanborn and
                  Josh Rackers and
                  Simon Batzner},
  title        = {e3nn/e3nn 0.2.3},
  month        = feb,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {0.2.3},
  doi          = {10.5281/zenodo.4557591},
  url          = {https://doi.org/10.5281/zenodo.4557591}
}

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-0.2.4.tar.gz (342.7 kB view details)

Uploaded Source

Built Distribution

e3nn-0.2.4-py3-none-any.whl (362.3 kB view details)

Uploaded Python 3

File details

Details for the file e3nn-0.2.4.tar.gz.

File metadata

  • Download URL: e3nn-0.2.4.tar.gz
  • Upload date:
  • Size: 342.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.7.3 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.8.8

File hashes

Hashes for e3nn-0.2.4.tar.gz
Algorithm Hash digest
SHA256 f01cadfa65e467600e576b8b4537b381ffb4bb84afcd54a2697697c1c39bd258
MD5 86f7b8322068c22c2b135ecdefad2dbb
BLAKE2b-256 33e7492d844282ffde9c5e1a6df99eecf8347c3fdea4b9d8baa3a4c11ce0e0b7

See more details on using hashes here.

File details

Details for the file e3nn-0.2.4-py3-none-any.whl.

File metadata

  • Download URL: e3nn-0.2.4-py3-none-any.whl
  • Upload date:
  • Size: 362.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/3.7.3 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.59.0 CPython/3.8.8

File hashes

Hashes for e3nn-0.2.4-py3-none-any.whl
Algorithm Hash digest
SHA256 c16b8dec062a9c06ad3ab54afcc7d4d938cffff15ef96d28cd15575fa2b57617
MD5 9aa326267e0810e50459a30a833dd61b
BLAKE2b-256 49b89898ed599f16dd15f0d19bd1739f9dce8e333fdc58f97401f040d7dd5db4

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