The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
Generalized Gradient Approximation Made Simple
Perdew, J. P., Burke, K., & Ernzerhof, M. (1996) · Physical Review Letters · doi:10.1103/physrevlett.77.3865 · Cited by 215,824
From ultrasoft pseudopotentials to the projector augmented-wave method
Kresse, G., & Joubert, D. (1999) · Physical Review B · doi:10.1103/physrevb.59.1758 · Cited by 85,307
Kresse, G., & Furthmüller, J. (1996) · Computational Materials Science · doi:10.1016/0927-0256(96)00008-0 · Cited by 76,871
Self-Consistent Equations Including Exchange and Correlation Effects
Kohn, W., & Sham, L. J. (1965) · Physical Review · doi:10.1103/physrev.140.a1133 · Cited by 64,135
Hohenberg, P., & Kohn, W. (1964) · Physical Review · doi:10.1103/physrev.136.b864 · Cited by 53,541
Fast Parallel Algorithms for Short-Range Molecular Dynamics
Plimpton, S. (1995) · Journal of Computational Physics · doi:10.1006/jcph.1995.1039 · Cited by 44,721
Canonical dynamics: Equilibrium phase-space distributions
Hoover, W. G. (1985) · Physical Review A · doi:10.1103/physreva.31.1695 · Cited by 23,852
Polymorphic transitions in single crystals: A new molecular dynamics method
Parrinello, M., & Rahman, A. (1981) · Journal of Applied Physics · doi:10.1063/1.328693 · Cited by 20,009
A unified formulation of the constant temperature molecular dynamics methods
Nosé, S. (1984) · The Journal of Chemical Physics · doi:10.1063/1.447334 · Cited by 17,163
P ACKMOL : A package for building initial configurations for molecular dynamics simulations
Martı́nez, L., Andrade, R., Birgin, E. G., & Martínez, J. M. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21224 · Cited by 10,013
Research works printed but not linked: 8
- McGreevy, R. L. Reverse Monte Carlo modelling. J. Phys. 13, R877 (2001).
- Huan, T. D. et al. A universal strategy for the creation of machine learning-based atomistic force fields. npj Comput. Mater. 3, 37 (2017).
- Behler, J. Representing potential energy surfaces by high-dimensional neural network potentials. J. Phys. 26, 183001 (2014).
- Matthews, J. N. A. Semiconductor industry switches to hafnium-based transistors. Phys. Today 61, 25 (2008).
- Upadhya, K., Yang, J. M. & Hoffman, W. P. Advanced materials for ultrahigh temperature structural applications above 2000 °C. Am. Ceram. Soc. Bull. 76, 51–56 (1997).
- Bartók, A. P., Kermode, J., Bernstein, N. & Csányi, G. Machine learning a general-purpose interatomic potential for silicon. Phys. Rev. X 8, 041048 (2018).
- Kresse, G. & Furthmüller, J. Software VASP, Vienna. Phys. Rev. B 54, 169 (1999).
- Snoek, J. Larochelle, H. & Adams, R. P. Practical bayesian optimization of machine learning algorithms. In Advances in Neural Information Processing Systems, 2951–2959 (Curran Associates, Inc., New York, USA, 2012).
Cited by
Sorted by citations
Gaussian Process Regression for Materials and Molecules
Deringer, V. L., Bartók, A. P., Bernstein, N., Wilkins, D. M., Ceriotti, M., & Cśanyi, G. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00022 · Cited by 1,311
Machine-Learning Interatomic Potentials for Materials Science
Mishin, Y. M. (2021) · Acta Materialia · doi:10.1016/j.actamat.2021.116980 · Cited by 431
Neural Network Potentials: A Concise Overview of Methods
Kocer, E., Ko, T. W., & Behler, J. (2022) · Annual Review of Physical Chemistry · doi:10.1146/annurev-physchem-082720-034254 · Cited by 341
Machine learning potentials for extended systems: a perspective
Behler, J., & Cśanyi, G. (2021) · The European Physical Journal B · doi:10.1140/epjb/s10051-021-00156-1 · Cited by 215
Wu, T., Kent, P. R. C., Gogotsi, Y. G., & Jiang, D. (2022) · Chemistry of Materials · doi:10.1021/acs.chemmater.2c00224 · Cited by 191
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
