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Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Open accessAbstract
We introduce a class of interatomic potential models that can be automatically generated from data consisting of the energies and forces experienced by atoms, as derived from quantum mechanical calculations. The models do not have a fixed functional form and hence are capable of modeling complex potential energy landscapes. They are systematically improvable with more data. We apply the method to bulk crystals, and test it by calculating properties at high temperatures. Using the interatomic potential to generate the long molecular dynamics trajectories required for such calculations saves orders of magnitude in computational cost.
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Existing MLIP efforts typically train models per material, with data often sampled from ab initio molecular dynamics, which markedly limits their general applicability and adoption.
“Existing efforts typically train models per material, with data often sampled from ab initio molecular dynamics (AIMD). This markedly limits their general applicability and adoption, requiring expensive data collection and training a new potential from scratch for each system.”
From Scaling deep learning for materials discovery (2023) · cites this paper as [48]
The paper describes potentials modeled for particular bulk materials and a long-term goal of making more general potentials (opening discussion and concluding passage; page unavailable). It also describes quantum-mechanical training data as expensive, but says its reference configurations came from random displacements, not AIMD. It does not establish that existing efforts typically use AIMD, that adoption is markedly limited, or that every new system requires training from scratch. (LLM, conf=0.84)
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Metadata
- Authors
- Bartók, A. P., Payne, M. C., Kondor, R., & Csányi, G.
- Affiliations
- University of Cambridge (GB)
- California Institute of Technology (US)
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2010-04-01
- Volume
- 104
- Issue
- 13
- Pages
- 136403-136403
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2083415705
- Cited by
- 3,242
- References
- 1
- Citations by year
- 2026: 470 · 2025: 468 · 2024: 454 · 2023: 400 · 2022: 288 · 2021: 304 · 2020: 307 · 2019: 189 · 2018: 128 · 2017: 79 · 2016: 40 · 2015: 31 · 2014: 31 · 2013: 23 · 2012: 18
- FWCI
- 7.28
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Physical Review Letters (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- CaltechAUTHORS (California Institute of Technology) (submittedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- arXiv (Cornell University)
- Funders
- University of Cambridge
- Engineering and Physical Sciences Research Council (EP/C52392X/1, EP/F032773/1)
- 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 › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Keywords
- Gaussian approximation potential, interatomic potentials, machine learning interatomic potentials, potential energy surface, bulk crystals, high-temperature properties, molecular dynamics, computational complexity reduction
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