Regularity Priors for the Linear Atomic Cluster Expansion

Fuente: arXiv
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Hauptverfasser: Darby, James P., Morrow, Joe D., Bartók, Albert P., Deringer, Volker L., Csányi, Gábor, Ortner, Christoph
Format: Preprint
Veröffentlicht: 2026
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author Darby, James P.
Morrow, Joe D.
Bartók, Albert P.
Deringer, Volker L.
Csányi, Gábor
Ortner, Christoph
author_facet Darby, James P.
Morrow, Joe D.
Bartók, Albert P.
Deringer, Volker L.
Csányi, Gábor
Ortner, Christoph
contents Machine-learned interatomic potentials enable large systems to be simulated for long time scales at near ab-initio accuracy. This accuracy is achieved by fitting extremely flexible model architectures to high quality reference data. In practice, this flexibility can cause unwanted behavior such as jagged predicted potential energy surfaces and generally poor out-of-distribution behavior. We investigate a general strategy for incorporating prior beliefs on the regularity of the target energy into linear Atomic Cluster Expansion (ACE) models and explore to what extent this approach improves the quality of the fitted models. Our main focus is an over-regularisation that replicates the Gaussian broadening used in Smooth Overlap of Atomic Positions (SOAP) descriptors within the ACE framework. Numerical tests indicate that the exact form of the prior is non-critical but that including such a prior leads to significant improvement in test errors, consistent repulsion at close-approach, eliminates spurious false minima in the potential energy and enhances stability during molecular dynamics simulations.
format Preprint
id arxiv_https___arxiv_org_abs_2601_15072
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Regularity Priors for the Linear Atomic Cluster Expansion
Darby, James P.
Morrow, Joe D.
Bartók, Albert P.
Deringer, Volker L.
Csányi, Gábor
Ortner, Christoph
Chemical Physics
Machine-learned interatomic potentials enable large systems to be simulated for long time scales at near ab-initio accuracy. This accuracy is achieved by fitting extremely flexible model architectures to high quality reference data. In practice, this flexibility can cause unwanted behavior such as jagged predicted potential energy surfaces and generally poor out-of-distribution behavior. We investigate a general strategy for incorporating prior beliefs on the regularity of the target energy into linear Atomic Cluster Expansion (ACE) models and explore to what extent this approach improves the quality of the fitted models. Our main focus is an over-regularisation that replicates the Gaussian broadening used in Smooth Overlap of Atomic Positions (SOAP) descriptors within the ACE framework. Numerical tests indicate that the exact form of the prior is non-critical but that including such a prior leads to significant improvement in test errors, consistent repulsion at close-approach, eliminates spurious false minima in the potential energy and enhances stability during molecular dynamics simulations.
title Regularity Priors for the Linear Atomic Cluster Expansion
topic Chemical Physics
url https://arxiv.org/abs/2601.15072