Uncertainty quantification for misspecified machine learned interatomic potentials

Danny Pérez

doi:10.1038/s41524-025-01758-4

Uncertainty quantification for misspecified machine learned interatomic potentials

2025-08-16

0

OA

AI

D

Danny Pérez *

A

Aparna P. A. Subramanyam

I

Ivan Maliyov

T

Thomas D. Swinburne *

DOI：10.1038/s41524-025-01758-4

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The use of high-dimensional regression techniques from machine learning has significantly improved the quantitative accuracy of interatomic potentials. Atomic simulations can now plausibly target quantitative predictions in a variety of settings, which has brought renewed interest in robust means to quantify uncertainties. In many practical settings where model complexity is constrained (e.g., due to performance considerations), misspecification — the inability of any one choice of model parameters to exactly match all training data — is a key contributor to errors that is often disregarded. Here, we employ a recent misspecification-aware regression technique to quantify parameter uncertainties, which is then propagated to a broad range of phase and defect properties in tungsten. The propagation is performed through both brute-force resampling and implicit Taylor expansion. The propagated misspecification uncertainties robustly quantify and bound errors on a broad range of material properties. We demonstrate application to recent foundational machine learning interatomic potentials, accurately predicting and bounding errors in MACE-MPA-0 energy predictions across the diverse materials project database.

Uncertainty quantification for misspecified machine learned interatomic potentials

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