Why uncertainty quantification for MLIPs is hard — and universal MLIPs may be a myth
rishabh16_ · x · 2026-09-19
Rishabh Anand writes about uncertainty quantification (UQ) for machine learning interatomic potentials (MLIPs). MLIPs are neural networks that predict per-atom energies and forces from a molecule's 3D conformer, aiming to replace or supplement molecular dynamics; academia has produced models like MACE, SevenNet, and Allegro, while industry teams (Orb, Meta's FAIR Chemistry, IBM) are building leading models, with force-energy datasets (OMat24, OMol25, OPoly26) growing larger.
Key arguments: UQ methods transferred from vision/text carry distinct pitfalls when applied to 3D molecular data, and "universal" MLIPs that work across all molecule types and conformers may simply not be achievable. The post draws on conversations with MIT PhD student Ty Perez, co-first-author on the author's latest work Zatom-1, which features MLIP experiments.
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