EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations

Yi-Lun Liao, Brandon Wood, Abhishek Das, Tess Smidt

EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations: 5 upvotes on Hugging Face Daily Papers, #5 of 5 papers on 2023-06-22. Day-by-day upvote history.

Equivariant Transformers such as Equiformer have demonstrated the efficacy of applying Transformers to the domain of 3D atomistic systems. However, they are still 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 S^2 activation and separable layer normalization. Putting this all together, we propose EquiformerV2, which outperforms previous state-of-the-art methods on the large-scale OC20 dataset by up to 12% on forces, 4% on energies, offers better speed-accuracy trade-offs, and 2times reduction in DFT calculations needed for computing adsorption energies.

Paper page on Hugging Face · arXiv

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