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 is to help the developement of E3 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{mario_geiger_2021_5006322,
  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
                  Jes Frellsen and
                  Nuri Jung and
                  Sophia Sanborn and
                  Josh Rackers and
                  Michael Bailey},
  title        = {e3nn/e3nn: 2021-06-21},
  month        = jun,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {0.3.3},
  doi          = {10.5281/zenodo.5006322},
  url          = {https://doi.org/10.5281/zenodo.5006322}
}

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-0.3.4.tar.gz (358.7 kB view details)

Uploaded Source

Built Distribution

e3nn-0.3.4-py3-none-any.whl (383.5 kB view details)

Uploaded Python 3

File details

Details for the file e3nn-0.3.4.tar.gz.

File metadata

  • Download URL: e3nn-0.3.4.tar.gz
  • Upload date:
  • Size: 358.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.6.4 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.1 CPython/3.8.11

File hashes

Hashes for e3nn-0.3.4.tar.gz
Algorithm Hash digest
SHA256 203543effa121aa80389b59ccdf5b8dd97dc0b6f6d8b2f69508a32bafe1994e7
MD5 fa7f35b00becc9abf246dc23cd6be386
BLAKE2b-256 23f61ef3c5a2cfd9e4f6ab2e18da6e36a48db0b53753219d93b0bb5748a86db5

See more details on using hashes here.

File details

Details for the file e3nn-0.3.4-py3-none-any.whl.

File metadata

  • Download URL: e3nn-0.3.4-py3-none-any.whl
  • Upload date:
  • Size: 383.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.2 importlib_metadata/4.6.4 pkginfo/1.7.1 requests/2.26.0 requests-toolbelt/0.9.1 tqdm/4.62.1 CPython/3.8.11

File hashes

Hashes for e3nn-0.3.4-py3-none-any.whl
Algorithm Hash digest
SHA256 ff4925b4f17f3a5171ee0799a8980cffab124f4541d799e6e6ae753149307b3c
MD5 5728a90e13714f20e438ec412a01b2c6
BLAKE2b-256 06e31a8950f70b50efbf982145a4cd79d36338f1c646a22ee557d684c5a7331e

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