Implement augmentation for rank-2 Cartesian tensors #547
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src/metatrain/utils/augmentation.py:319
- [nitpick] Verify that the tensor dimensions and index ordering in the einsum call correctly implement the intended transformation for rank-2 Cartesian tensors.
new_tensor = torch.einsum("Aa,iabp,bB->iABp", transformation, tensor, transformation.T)
| is_cartesian = False | ||
| if len(target_tmap.blocks()) == 1: | ||
| if len(target_tmap.block().components) > 0: | ||
| if "xyz" in target_tmap.block().components[0].names[0]: |
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[nitpick] Relying solely on a substring 'xyz' to detect Cartesian tensors might be fragile if the naming convention changes; consider using a more robust method or clearly document this assumption.
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| if "xyz" in target_tmap.block().components[0].names[0]: | |
| if target_tmap.block().components[0].names[0] in CARTESIAN_TENSOR_NAMES: |
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Allows training PET on rank-2 Cartesian tensors (stress, polarizability, etc.)
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📚 Documentation preview 📚: https://metatrain--547.org.readthedocs.build/en/547/