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Foundation-Model Supervision of Distilled Potentials for Million-Atom Molecular Dynamics
Machine-learned interatomic potentials distilled from foundation models enable fast large-scale molecular dynamics (MD), but they can fail locally in chemical environments that their training data did not sample. System-averaged error metrics and small random audits often miss such sparse or isolate
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