Uncertainty quantification for misspecified machine learned interatomic potentials
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01758-4 |
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| author | Danny Perez Aparna P. A. Subramanyam Ivan Maliyov Thomas D. Swinburne |
| author_facet | Danny Perez Aparna P. A. Subramanyam Ivan Maliyov Thomas D. Swinburne |
| author_sort | Danny Perez |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-b94d3d8a6c624c7c95c29e491b81fe05 |
| institution | DOAJ |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-b94d3d8a6c624c7c95c29e491b81fe052025-08-20T03:07:27ZengNature Portfolionpj Computational Materials2057-39602025-08-0111111510.1038/s41524-025-01758-4Uncertainty quantification for misspecified machine learned interatomic potentialsDanny Perez0Aparna P. A. Subramanyam1Ivan Maliyov2Thomas D. Swinburne3Theoretical Division T-1, Los Alamos National LaboratoryTheoretical Division T-1, Los Alamos National LaboratoryAix-Marseille Université, CNRS, CINaM UMR 7325, Campus de LuminyAix-Marseille Université, CNRS, CINaM UMR 7325, Campus de LuminyAbstract 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.https://doi.org/10.1038/s41524-025-01758-4 |
| spellingShingle | Danny Perez Aparna P. A. Subramanyam Ivan Maliyov Thomas D. Swinburne Uncertainty quantification for misspecified machine learned interatomic potentials npj Computational Materials |
| title | Uncertainty quantification for misspecified machine learned interatomic potentials |
| title_full | Uncertainty quantification for misspecified machine learned interatomic potentials |
| title_fullStr | Uncertainty quantification for misspecified machine learned interatomic potentials |
| title_full_unstemmed | Uncertainty quantification for misspecified machine learned interatomic potentials |
| title_short | Uncertainty quantification for misspecified machine learned interatomic potentials |
| title_sort | uncertainty quantification for misspecified machine learned interatomic potentials |
| url | https://doi.org/10.1038/s41524-025-01758-4 |
| work_keys_str_mv | AT dannyperez uncertaintyquantificationformisspecifiedmachinelearnedinteratomicpotentials AT aparnapasubramanyam uncertaintyquantificationformisspecifiedmachinelearnedinteratomicpotentials AT ivanmaliyov uncertaintyquantificationformisspecifiedmachinelearnedinteratomicpotentials AT thomasdswinburne uncertaintyquantificationformisspecifiedmachinelearnedinteratomicpotentials |