Papers › EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations
Yi-Lun Liao, Brandon Wood, Abhishek Das, Tess Smidt
Equivariant Transformers such as Equiformer have demonstrated the efficacy of applying Transformers to the domain of 3D atomistic systems. However, they are limited to small degrees of equivariant representations due to their computational complexity. In this paper, we investigate whether these architectures can scale well to higher degrees. Starting from Equiformer, we first replace SO(3) convolutions with eSCN convolutions to efficiently incorporate higher-degree tensors. Then, to better leverage the power of higher degrees, we propose three architectural improvements -- attention re-normalization, separable S² activation and separable layer normalization. Putting this all together, we propose EquiformerV2, which outperforms previous state-of-the-art methods on large-scale OC20 dataset by up to 9% on forces, 4% on energies, offers better speed-accuracy trade-offs, and 2× reduction in DFT calculations needed for computing adsorption energies. Additionally, EquiformerV2 trained on only OC22 dataset outperforms GemNet-OC trained on both OC20 and OC22 datasets, achieving much better data efficiency. Finally, we compare EquiformerV2 with Equiformer on QM9 and OC20 S2EF-2M datasets to better understand the performance gain brought by higher degrees.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Graph Property Prediction | QM9 | EquiformerV2 | Standardized MAE | 0.67 | #4 of 10 | Archive leaderboard | report |
| Graph Property Prediction | QM9 | EquiformerV2 | alpha (ma) | 47 | #4 of 10 | Archive leaderboard | report |
| Graph Property Prediction | QM9 | EquiformerV2 | gap (meV) | 29 | #4 of 10 | Archive leaderboard | report |
| Graph Property Prediction | QM9 | EquiformerV2 | logMAE | -5.87 | #4 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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