e3nn/e3nn

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1280 stars · 185 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

e3nn is a Python/PyTorch library for building E(3)-equivariant neural networks that respect 3D rotation, translation, and mirror symmetries. It provides fundamental operations such as tensor products, spherical harmonics, and equivariant linear layers for geometric deep learning.

Use cases

• build neural networks that respect 3D rotational symmetry
• compute spherical harmonics in PyTorch
• create equivariant tensor product layers
• model molecules and materials with symmetry-aware networks
• predict 3D properties like forces and energies equivariantly
• develop geometric deep learning models for point clouds

When to choose

• your data has 3D Euclidean symmetry (rotations, translations, mirrors)
• you need equivariant operations on irreducible representations in PyTorch
• you are doing molecular, material, or point-cloud learning research

When to avoid

• your problem has no geometric or rotational symmetry
• you need a production-ready stable API with no breaking changes
• you do not use PyTorch

Facets

library · maturity active

machine-learning deep-learning math graphics machine-learning deep-learning chemistry simulation python equivariant-neural-networks e3-symmetry spherical-harmonics tensor-products pytorch geometric-deep-learning molecular-modeling algorithms

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markdown · JSON · MCP: product_card(name="e3nn/e3nn")