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]*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.
To install the latest released version:
pip install --upgrade e3nn-jaxTo install the latest GitHub version:
pip install git+https://github.com/e3nn/e3nn-jax.gitAsk a question in the discussions tab.
The main difference is the presence of the class IrrepsArray.
IrrepsArray contains the irreps (Irreps) along with the data array.
- 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}
}
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