Metadata-Version: 2.1
Name: e3nn
Version: 0.2.0
Summary: Equivariant convolutional neural networks for the group E(3) of 3 dimensional rotations, translations, and mirrors.
Home-page: https://e3nn.org
License: MIT
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: sympy
Requires-Dist: scipy
Requires-Dist: torch (>=1.4.0)
Requires-Dist: torch-geometric

# e3nn

**[Documentation](https://docs.e3nn.org)** | **[Code](https://github.com/e3nn/e3nn)**

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
```
pip install e3nn
```

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

![](https://user-images.githubusercontent.com/333780/79220728-dbe82c00-7e54-11ea-82c7-b3acbd9b2246.gif)

## Help
We are happy to help! The best way to get help on `e3nn` is to submit a [Question](https://github.com/e3nn/e3nn/issues/new?assignees=&labels=question&template=question.md&title=%E2%9D%93+%5BQUESTION%5D) or [Bug Report](https://github.com/e3nn/e3nn/issues/new?assignees=&labels=bug&template=bug-report.md&title=%F0%9F%90%9B+%5BBUG%5D).

## 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](https://github.com/e3nn/e3nn/issues/new?assignees=&labels=projectwanted&template=project-wanted.md&title=%F0%9F%91%8B++Hi%21+I%27m+%5BYOUR_NAME%5D+and+I%27m+interested+in+%5BYOUR_INTERESTS%5D.).

## Code of conduct
Our community abides by the [Contributor Covenant Code of Conduct](code_of_conduct.md).

## Citing
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3724963.svg)](https://doi.org/10.5281/zenodo.3724963)

```
@software{e3nn_2020_3724963,
  author       = {Mario Geiger and
                  Tess Smidt and
                  Benjamin K. Miller and
                  Wouter Boomsma and
                  Kostiantyn Lapchevskyi and
                  Maurice Weiler and
                  Michał Tyszkiewicz and
                  Bradley Dice and
                  Jes Frellsen and
                  Sophia Sanborn and
                  M. Alby},
  title        = {\texttt{e3nn}: a modular framework for Euclidean Neural Networks, github.com/e3nn/e3nn}},
  month        = dec,
  year         = 2020,
  publisher    = {Zenodo},
  version      = {0.1.1},
  doi          = {10.5281/zenodo.3724963},
  url          = {https://doi.org/10.5281/zenodo.3724963}
}
```

### 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.


