EquiformerV3: Scaling Efficient, Expressive, and General SE(3)-Equivariant Graph Attention Transformers
As SE(3)-equivariant graph neural networks mature as a core tool for 3D atomistic modeling, improving their efficiency, expressivity, and physical consistency has become a central challenge for large-scale applications. In this work, we introduce EquiformerV3, the third generation of the SE(3)-equivariant graph attention Transformer, designed to advance all three dimensions: efficiency, expressivity, and generality. Building on EquiformerV2, we have the following three key advances. First, we optimize the software implementation, achieving 1.75times speedup. Second, we introduce simple and effective modifications to EquiformerV2, including equivariant merged layer normalization, improved feedforward network hyper-parameters, and attention with smooth radius cutoff. Third, we propose SwiGLU-S^2 activations to incorporate many-body interactions for better theoretical expressivity and to preserve strict equivariance while reducing the complexity of sampling S^2 grids. Together, SwiGLU-S^2 activations and smooth-cutoff attention enable accurate modeling of smoothly varying potential energy surfaces (PES), generalizing EquiformerV3 to tasks requiring energy-conserving simulations and higher-order derivatives of PES. With these improvements, EquiformerV3 trained with the auxiliary task of denoising non-equilibrium structures (DeNS) achieves state-of-the-art results on OC20, OMat24, and Matbench Discovery.
69dc3e7b2b6b8dc66ffeeb15
Yi-Lun Liao
claimed_verified
2026-04-13T19:24:24.714Z
64948a4a8d5ff0dd776655fe
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/65Z2SRYbzIXxraZE1e2Go.jpeg
Yi-Lun Liao
yilunliao
user
yilunliao
69dc3e7b2b6b8dc66ffeeb16
Alexander J. Hoffman
69dc3e7b2b6b8dc66ffeeb17
Sabrina C. Shen
69dc3e7b2b6b8dc66ffeeb18
Alexandre Duval
69dc3e7b2b6b8dc66ffeeb19
Sam Walton Norwood
69dc3e7b2b6b8dc66ffeeb1a
Tess Smidt
EquiformerV3: Scaling Efficient, Expressive, and General SE(3)-Equivariant Graph Attention Transformers
As SE(3)-equivariant graph neural networks mature as a core tool for 3D atomistic modeling, improving their efficiency, expressivity, and physical consistency has become a central challenge for large-scale applications. In this work, we introduce EquiformerV3, the third generation of the SE(3)-equivariant graph attention Transformer, designed to advance all three dimensions: efficiency, expressivity, and generality. Building on EquiformerV2, we have the following three key advances. First, we optimize the software implementation, achieving 1.75times speedup. Second, we introduce simple and effective modifications to EquiformerV2, including equivariant merged layer normalization, improved feedforward network hyper-parameters, and attention with smooth radius cutoff. Third, we propose SwiGLU-S^2 activations to incorporate many-body interactions for better theoretical expressivity and to preserve strict equivariance while reducing the complexity of sampling S^2 grids. Together, SwiGLU-S^2 activations and smooth-cutoff attention enable accurate modeling of smoothly varying potential energy surfaces (PES), generalizing EquiformerV3 to tasks requiring energy-conserving simulations and higher-order derivatives of PES. With these improvements, EquiformerV3 trained with the auxiliary task of denoising non-equilibrium structures (DeNS) achieves state-of-the-art results on OC20, OMat24, and Matbench Discovery.
EquiformerV3 advances SE(3)-equivariant graph neural networks through enhanced efficiency, expressivity, and generality via optimized implementation, improved architectural components, and novel activation functions for accurate 3D atomic modeling.
SE(3)-equivariant graph neural networks graph attention Transformer software implementation layer normalization feedforward network attention mechanism smooth radius cutoff SwiGLU-S² activations potential energy surfaces denoising non-equilibrium structures DeNS OC20 OMat24 Matbench Discovery