{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"e3nn provides PyTorch-based operations for building E(3)-equivariant neural networks, including tensor products, spherical harmonics, and linear layers that respect rotation, translation, and mirror symmetries.","skillfed_tags":["geometric-deep-learning","equivariant-networks","pytorch-extension"],"use_cases":["Build neural networks for molecular property prediction that respect 3D rotational symmetry of atomic structures.","Process point cloud data with layers that automatically handle arbitrary rotations and translations.","Implement steerable convolutional networks for volumetric data like medical imaging or climate simulations.","Compose custom equivariant architectures using tensor products and irreducible representations.","Accelerate geometric deep learning research by reusing tested equivariance operations instead of deriving them from scratch."],"what_it_does":"e3nn is a PyTorch library for building neural networks that respect Euclidean symmetries\u2014rotations, translations, and reflections in 3D space. It provides building blocks like equivariant linear layers, tensor products, and spherical harmonics that automatically enforce these symmetries during computation, eliminating the need to manually encode geometric constraints.\n\nThe library is designed for researchers and practitioners working with 3D geometric data: point clouds, molecular structures, volumetric data, and other problems where rotational or translational invariance is a natural property of the problem. It depends on PyTorch for computation, sympy and scipy for mathematical operations, and opt_einsum_fx for tensor contraction optimization.","worth_installing":"Yes, if you are building neural networks for 3D geometric or molecular data and need built-in equivariance guarantees. The library is mature enough for research use, has no known vulnerabilities, and low install friction. The aging maintenance status (182 days since last release) is a minor concern for a research tool but not a blocker if your use case aligns with the current API."},"id":"e3nn","links":{"html":"https://skillfed.io/packages/e3nn","md":"https://skillfed.io/packages/e3nn.md","pypi":"https://pypi.org/project/e3nn/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2026-02-13","license_spdx":null,"license_treatment":"permissive","name":"e3nn","python_support":"supports_current","summary":"Equivariant convolutional neural networks for the group E(3) of 3 dimensional rotations, translations, and mirrors."},"popularity":{"monthly_downloads":532675,"position":6146,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.6.0"}