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e3nn composable Euclidean-equivariant neural-network software

Contribution grade

Graph and geometric machine learning · Human-designed research method or resource

Reference score175,000Provisional editorial estimate of this exact contribution; its grade is assessed separately.

What changed

Provides reusable parity-aware irreducible representations, spherical harmonics and trainable tensor-product primitives for assembling E(3)-equivariant networks.

Scientific scope

Provides reusable parity-aware irreducible representations, spherical harmonics and trainable tensor-product primitives for assembling E(3)-equivariant networks. 2022 arXiv source retains a January 2021 draft date; source version not backdated to a finalized journal article. Geiger and Smidt are its two source authors, not the entire predecessor-author union. Framework generality depends on valid representations and equivariant composition; no automatic physical accuracy or claim to invent representation theory. Bio/medical applications omitted.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. 2022 arXiv source retains a January 2021 draft date; source version not backdated to a finalized journal article. Geiger and Smidt are its two source authors, not the entire predecessor-author union. Framework generality depends on valid representations and equivariant composition; no automatic physical accuracy or claim to invent representation theory. Bio/medical applications omitted.

Contributors

Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.

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