README.md

e3nn-jax

Documentation

import e3nn_jax as e3nn

# Create a random array made of a scalar (0e) and a vector (1o)
array = e3nn.normal("0e + 1o", jax.random.PRNGKey(0))

print(array)  
# 1x0e+1x1o [ 1.8160863  -0.75488514  0.33988908 -0.53483534]

# Compute the norms
norms = e3nn.norm(array)
print(norms)
# 1x0e+1x0e [1.8160863  0.98560894]

# Compute the norm of the full array
total_norm = e3nn.norm(array, per_irrep=False)
print(total_norm)
# 1x0e [2.0662997]

# Compute the tensor product of the array with itself
tp = e3nn.tensor_square(array)
print(tp)
# 2x0e+1x1o+1x2e
# [ 1.9041989   0.25082085 -1.3709364   0.61726785 -0.97130704  0.40373924
#  -0.25657722 -0.18037902 -0.18178469 -0.14190137]

🚀 44% faster than pytorch*

*Speed comparison done with a full model (MACE) during training (revMD-17) on a GPU (NVIDIA RTX A5000)

Please always check the CHANGELOG for breaking changes.

Installation

To install the latest released version:

pip install --upgrade e3nn-jax

To install the latest GitHub version:

pip install git+https://github.com/e3nn/e3nn-jax.git

Need Help?

Ask a question in the discussions tab.

What is different from the PyTorch version?

The main difference is the presence of the class IrrepsArray. IrrepsArray contains the irreps (Irreps) along with the data array.

Citing

• Euclidean Neural Networks

@misc{thomas2018tensorfieldnetworksrotation,
      title={Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds}, 
      author={Nathaniel Thomas and Tess Smidt and Steven Kearnes and Lusann Yang and Li Li and Kai Kohlhoff and Patrick Riley},
      year={2018},
      eprint={1802.08219},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1802.08219}, 
}

@misc{weiler20183dsteerablecnnslearning,
      title={3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data}, 
      author={Maurice Weiler and Mario Geiger and Max Welling and Wouter Boomsma and Taco Cohen},
      year={2018},
      eprint={1807.02547},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.02547}, 
}

@misc{kondor2018clebschgordannetsfullyfourier,
      title={Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network}, 
      author={Risi Kondor and Zhen Lin and Shubhendu Trivedi},
      year={2018},
      eprint={1806.09231},
      archivePrefix={arXiv},
      primaryClass={stat.ML},
      url={https://arxiv.org/abs/1806.09231}, 
}

• e3nn

@misc{e3nn_paper,
    doi = {10.48550/ARXIV.2207.09453},
    url = {https://arxiv.org/abs/2207.09453},
    author = {Geiger, Mario and Smidt, Tess},
    keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Neural and Evolutionary Computing (cs.NE), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {e3nn: Euclidean Neural Networks},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}

@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
                  Dylan Madisetti and
                  Martin Uhrin and
                  Jes Frellsen and
                  Nuri Jung and
                  Sophia Sanborn and
                  Mingjian Wen and
                  Josh Rackers and
                  Marcel Rød and
                  Michael Bailey},
  title        = {Euclidean neural networks: e3nn},
  month        = apr,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {0.5.0},
  doi          = {10.5281/zenodo.6459381},
  url          = {https://doi.org/10.5281/zenodo.6459381}
}