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-0.4.1.tar.gz (359.0 kB view details)

Uploaded Source

Built Distribution

e3nn-0.4.1-py3-none-any.whl (384.1 kB view details)

Uploaded Python 3

File details

Details for the file e3nn-0.4.1.tar.gz.

File metadata

  • Download URL: e3nn-0.4.1.tar.gz
  • Upload date:
  • Size: 359.0 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-0.4.1.tar.gz
Algorithm Hash digest
SHA256 854497f4cf5c6794d4c0db3c4cd04b9a1d1b34fce9557c3bf712bab1b379400d
MD5 75bf1c265b493b72c8216df1e1f82704
BLAKE2b-256 f3e4d4a6facf4d24bd0f3a558dae9e49961b921051d2ce8767239773c7553f47

See more details on using hashes here.

File details

Details for the file e3nn-0.4.1-py3-none-any.whl.

File metadata

  • Download URL: e3nn-0.4.1-py3-none-any.whl
  • Upload date:
  • Size: 384.1 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-0.4.1-py3-none-any.whl
Algorithm Hash digest
SHA256 2a1f4686e0975cfafda60503ed5c799c177bf800eb8abb89822dd60a25aa60e6
MD5 ebc43220533acbc24d996542844a8eb6
BLAKE2b-256 66724222e20e3757f4d10353ced1358a0b1dc28391bee9f4800ba1b9d2c549c1

See more details on using hashes here.

Supported by

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