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yilunliao avatar yilunliao commented on June 15, 2024

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.

from equiformer_v2.

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