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Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide
Open accessAbstract
We propose an active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP). Our active learning scheme consists of an unsupervised machine learning (ML) scheme coupled with a Bayesian optimization technique that evaluates the GAP model. We apply this scheme to a Hafnium dioxide (HfO 2 ) dataset generated from a “melt-quench” ab initio molecular dynamics (AIMD) protocol. Our results show that the active learning scheme, with no prior knowledge of the dataset, is able to extract a configuration that reaches the required energy fit tolerance. Further, molecular dynamics (MD) simulations performed using this active learned GAP model on 6144 atom systems of amorphous and liquid state elucidate the structural properties of HfO 2 with near ab initio precision and quench rates (i.e., 1.0 K/ps) not accessible via AIMD. The melt and amorphous X-ray structural factors generated from our simulation are in good agreement with experiment. In addition, the calculated diffusion constants are in good agreement with previous ab initio studies.
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“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 [44]
In the citing paper
“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 [44]
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Metadata
- Authors
- Sivaraman, G., Krishnamoorthy, A. N., Baur, M., Holm, C., Stan, M., Csányi, G., Benmore, C., & Vázquez-Mayagoitia, Á.
- Affiliations
- Argonne National Laboratory (US)
- University of Stuttgart (DE)
- Venue
- npj Computational Materials
- Published
- 2020-07-23
- Volume
- 6
- Issue
- 1
- ISSN
- 2057-3960 (ISSN-L)
- OpenAlex
- W2981603605
- Cited by
- 194
- References
- 57
- Citations by year
- 2026: 28 · 2025: 32 · 2024: 43 · 2023: 35 · 2022: 22 · 2021: 27 · 2020: 7
- FWCI
- 8.86
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Computational Materials (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Energy (DE‐ AC02‐06CH11357, Contract No. DE-AC02-06CH11357., AC02‐06CH11357, contract DE-AC02-06CH11357, 06CH11357, No. DE-AC02-06CH11357, DE-AC02-)
- Deutsche Forschungsgemeinschaft (EXC2075390740016, DE-AC02-06CH11357, EXC 2075, 390740016)
- Office of Science (Contract No. DE-AC02-06CH11357, AC02‐06CH11357, DE-AC02, No. DE-AC02-06CH11357., 06CH11357, DE-AC02-06CH11357)
- Basic Energy Sciences (DE-AC02, DE-AC- 02-06CH11357, AC02‐06CH11357, Contract No. DE-AC02-06CH11357, 06CH11357)
- Laboratory Directed Research and Development (DE‐AC02‐06CH11357)
- Argonne National Laboratory (DE- AC02-06-CH11357, DE-AC02, AC02–06CH11357, Contract DE-AC02-06CH11357, No. DE-AC02-06CH11357, 06CH11357, Contract No. DE-AC02-06CH11357., under contract DE-AC02-06CH11357)
- 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 › Earth and Planetary Sciences › Geophysics › High-pressure geophysics and materials
- Keywords
- active learning (machine learning), Gaussian approximation potential, interatomic potentials, hafnium oxide, amorphous HfO2, ab initio molecular dynamics, Bayesian optimization, unsupervised learning, diffusion coefficient, machine learning interatomic potentials
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