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]

:rocket: 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} }