AI for Science: don't spend data teaching models symmetries physics already encodes
bravo_abad · x · 2026-10-15
Using molecular energy prediction as an example, the author argues that representation choice in AI for Science determines what a model must learn: rotations, translations, and permutations of identical atoms leave energy unchanged, yet a generic model would waste data discovering these invariances.
Key points:
- Build symmetries in via invariant descriptors (distances, bond angles) or equivariant architectures, so known physics is guaranteed exactly rather than learned approximately.
- Representation is not mere preprocessing — it defines which differences the model can recognize, and matters most when data are scarce.
- Trade-off: every representation carries assumptions. Distance/angle-only descriptors can't distinguish mirror images; purely local descriptors can assign identical fingerprints to genuinely different structures.
Principle: don't spend data teaching the model something you already know how to encode.
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