e3nn/e3nn-jax
jax library for E3 Equivariant Neural Networks
This library is for researchers and machine learning engineers working with 3D data, particularly in fields like molecular modeling or materials science. It provides tools to build neural networks that inherently understand symmetries and rotations in 3D space. You input 3D data representations (like atomic positions and features) and get out predictions or embeddings that respect the geometry of the system.
224 stars. Used by 2 other packages. No commits in the last 6 months. Available on PyPI.
Use this if you are developing machine learning models for 3D data where rotational and reflectional symmetries are important, and you need a high-performance JAX-based solution.
Not ideal if your data is not inherently 3D or if the symmetries of 3D space are not a critical aspect of your modeling problem.
Stars
224
Forks
20
Language
Python
License
Apache-2.0
Category
Last pushed
Aug 25, 2025
Commits (30d)
0
Dependencies
5
Reverse dependents
2
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