{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/11"}],"enrichment":{"capability":"Implements equivariant convolutional neural networks for 3D rotations, translations, and mirrors using JAX, with an IrrepsArray data structure that tracks irreducible representations alongside tensor data.","skillfed_tags":["equivariance","geometric-deep-learning","jax-ecosystem"],"use_cases":["Train equivariant neural networks on molecular structures or point clouds where 3D symmetries matter for prediction accuracy.","Build MACE-style interatomic potential models for molecular dynamics simulations with guaranteed rotation/translation equivariance.","Prototype geometric deep learning models in JAX that respect Euclidean symmetries without manual constraint engineering.","Compute tensor products and irrep decompositions for group-theoretic operations in physics-informed machine learning.","Develop models on GPU/TPU hardware using JAX's compilation and automatic differentiation with built-in symmetry guarantees."],"what_it_does":"e3nn-jax is a JAX implementation of Euclidean neural networks that enforce equivariance to 3D rotations, translations, and mirror symmetries. It wraps tensor data in an IrrepsArray structure that tracks irreducible representations (irreps) of the E(3) group, allowing neural networks to respect these geometric symmetries by design. This is particularly useful for molecular systems, point clouds, and other 3D data where physical symmetries should be preserved or exploited by the model.\n\nThe package provides operations like tensor products, norms, and convolutions that work directly on irreps-annotated arrays. It depends on jax, jaxlib, numpy, sympy, and attrs. The library is actively maintained, supports modern Python versions (3.9+), and is distributed under Apache 2.0, making it suitable for both research and commercial use.","worth_installing":"Yes. The package is actively maintained, has low install friction, carries a permissive license, and fills a specific niche\u2014equivariant neural networks in JAX with explicit irreps tracking. Install it if you need to build or experiment with E(3)-equivariant models; skip it if you don't work with 3D geometric data or prefer PyTorch."},"id":"e3nn-jax","links":{"html":"https://skillfed.io/packages/e3nn-jax","md":"https://skillfed.io/packages/e3nn-jax.md","pypi":"https://pypi.org/project/e3nn-jax/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-04-01","license_spdx":null,"license_treatment":"permissive","name":"e3nn-jax","python_support":"supports_current","summary":"Equivariant convolutional neural networks for the group E(3) of 3 dimensional rotations, translations, and mirrors."},"popularity":{"monthly_downloads":76628,"position":14606,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.21.0"}