Comments (1)
Hi @Atomu2014
Yeah. The model can be applicable to SE(3)-invariant molecule learning, and so are other SE(3)-equivariant networks.
I think it would be great for you to start with the example in the codebase to understand how the code is working.
To adapt to molecucle learning as mentioned by the inputs/outputs provided by you, you can change the outputs of EquiformerV2 (containing both energy and forces for OC20) to atom types and atomic positions. Energy and atom types are both type-0 vectors, and atomic positions and forces are type-1 vectors. Therefore, that would require minimal changes to the code.
Let me know if you need more details.
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Related Issues (12)
- Upload models to the Hugging Face Hub HOT 4
- Incorporating vector node features HOT 1
- Training logs of QM9 dataset for EquiformerV2 HOT 4
- Large performance gap in MD17/22 dataset HOT 4
- How to train on a customied dataset? HOT 1
- some questions HOT 2
- Multi-node multi-gpu training HOT 1
- Small equivariant example HOT 7
- Speed compared to `TorchMD-Net` HOT 1
- How to use the v2 to train IS2RE datasets? HOT 1
- e3nn tensors compatibility issue HOT 3
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