Combining phonon accuracy with high transferability in Gaussian approximation potential models
Open accessAbstract
Machine learning driven interatomic potentials, including Gaussian approximation potential (GAP) models, are emerging tools for atomistic simulations. Here, we address the methodological question of how one can fit GAP models that accurately predict vibrational properties in specific regions of configuration space while retaining flexibility and transferability to others. We use an adaptive regularization of the GAP fit that scales with the absolute force magnitude on any given atom, thereby exploring the Bayesian interpretation of GAP regularization as an “expected error” and its impact on the prediction of physical properties for a material of interest. The approach enables excellent predictions of phonon modes (to within 0.1 THz–0.2 THz) for structurally diverse silicon allotropes, and it can be coupled with existing fitting databases for high transferability across different regions of configuration space, which we demonstrate for liquid and amorphous silicon. These findings and workflows are expected to be useful for GAP-driven materials modeling more generally.
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The total NNP errors are similar to other state-of-the-art (rotationally invariant) MLPs for the modeling of reactive events.
“The total NNP errors are similar to other state-of-the-art (rotationally invariant) MLPs38,42,44 for the modeling of reactive events.”
From A reactive neural network framework for water-loaded acidic zeolites (2024) · cites this paper as [42]
These errors are similar to other reactive (rotationally invariant) MLPs and about the same as for previously developed silica NNPs.
“These errors are similar to other reactive (rotationally invariant) MLPs42,44,52 and about the same as for our previously developed silica NNPs38.”
From A reactive neural network framework for water-loaded acidic zeolites (2024) · cites this paper as [42]
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- Authors
- George, J., Hautier, G., Bartók, A. P., Csányi, G., & Deringer, V. L.
- Affiliations
- UCLouvain (BE)
- University of Warwick (GB)
- Engineering Laboratory, University of Cambridge 3 , Cambridge CB2 1PZ, United Kingdom
- University of Oxford (GB)
- Corresponding author
- Deringer, Volker L.
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2020-07-23
- Volume
- 153
- Issue
- 4
- Pages
- 044104-044104
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W3024746059
- Cited by
- 44
- References
- 79
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 6 · 2023: 9 · 2022: 11 · 2021: 6 · 2020: 3
- FWCI
- 2.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- The Journal of Chemical Physics (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) (submittedVersion, other-oa)
- Science and Technology Facilities Council (submittedVersion)
- arXiv (Cornell University)
- Funders
- Leverhulme Trust (ECF-2017-278)
- Engineering and Physical Sciences Research Council (EP/P022561/1)
- Horizon 2020 Framework Programme (730897, 837910)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
- Keywords
- Gaussian approximation potential, interatomic potentials, phonon modes, vibration characteristics, adaptive regularization, Bayesian regularization, transferability, silicon allotropes, liquid silicon, amorphous silicon, machine learning interatomic potentials
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