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E3x: $\mathrm{E}(3)$-Equivariant Deep Learning Made Easy

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arxiv 2401.07595 v3 pith:HWTB3W5V submitted 2024-01-15 cs.LG cs.AIphysics.chem-ph

Oliver T. Unke , Hartmut Maennel This is my paper · ORCID
classification cs.LGcs.AIphysics.chem-ph
keywords mathrmequivariantnetworksneuraldatahttpslearningmodels
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abstract

This work introduces E3x, a software package for building neural networks that are equivariant with respect to the Euclidean group $\mathrm{E}(3)$, consisting of translations, rotations, and reflections of three-dimensional space. Compared to ordinary neural networks, $\mathrm{E}(3)$-equivariant models promise benefits whenever input and/or output data are quantities associated with three-dimensional objects. This is because the numeric values of such quantities (e.g. positions) typically depend on the chosen coordinate system. Under transformations of the reference frame, the values change predictably, but the underlying rules can be difficult to learn for ordinary machine learning models. With built-in $\mathrm{E}(3)$-equivariance, neural networks are guaranteed to satisfy the relevant transformation rules exactly, resulting in superior data efficiency and accuracy. The code for E3x is available from https://github.com/google-research/e3x, detailed documentation and usage examples can be found on https://e3x.readthedocs.io.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. An Efficient Sparse Kernel Generator for O(3)-Equivariant Deep Networks

    cs.LG 2025-01 conditional novelty 7.0 of 10

    A JIT kernel generator for the Clebsch-Gordan tensor product reports up to 1.3x speedup over NVIDIA cuEquivariance, over 10x over e3nn, and a 6.2x FP64 inference speedup for the MACE model over its unoptimized baseline.

  2. Edge Cluster Expansion with Radial Rotary Attention for Interatomic Potentials

    stat.ML 2026-07 conditional novelty 6.0 of 10

    Edge Cluster Expansion plus Radial Rotary Complex Attention yield TECE-OAM-RRA-1.0, a Matbench Discovery SOTA SO(2) MLIP trained on OMat24, sAlex, and MPTrj.

  3. The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products

    cs.LG 2025-06 conditional novelty 6.0 of 10

    The reported speedups of Gaunt and matrix tensor products over the full Clebsch-Gordan tensor product come from reduced expressivity, and the only true per-expressivity speedup comes from fast spherical harmonic transforms.

  4. Machine Learning Interatomic Potentials: library for efficient training, model development and simulation of molecular systems

    physics.chem-ph 2025-05 conditional novelty 6.0 of 10

    InstaDeep's mlip library ports MACE, NequIP, and ViSNet to JAX with a JAX-MD backend, ships SPICE2-trained organics models, reports faster MD steps than its own Torch routes, and proposes a faster gated MACE variant i...

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