The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide
Abstract
Artificial neural networks represent an accurate and efficient tool to construct high-dimensional potential-energy surfaces based on first-principles data. However, so far the main drawback of this method has been the limitation to a single atomic species. We present a generalization to compounds of arbitrary chemical composition, which now enables simulations of a wide range of systems containing large numbers of atoms. The required incorporation of long-range interactions is achieved by combining the numerical accuracy of neural networks with an electrostatic term based on environment-dependent charges. Using zinc oxide as a benchmark system we show that the neural network potential-energy surface is in excellent agreement with density-functional theory reference calculations, while the evaluation is many orders of magnitude faster.
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Cites
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Self-citationGaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
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Cited by
On representing chemical environments
Bartók, A. P., Kondor, R., & Csányi, G. (2013) · Physical Review B · doi:10.1103/physrevb.87.184115 · Cited by 2,572
Performance and Cost Assessment of Machine Learning Interatomic Potentials
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Self-citationCHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling
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Constructing high‐dimensional neural network potentials: A tutorial review
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Self-citationFrom DFT to machine learning: recent approaches to materials science–a review
Schleder, G. R., Padilha, A. C. M., Acosta, C. M., Costa, M., & Fazzio, A. (2019) · Journal of Physics: Materials · doi:10.1088/2515-7639/ab084b · Cited by 852
Metadata
- Authors
- Artrith, N., Morawietz, T., & Behler, J.
- Venue
- Physical Review B
- Publisher
- American Physical Society (APS)
- Published
- 2011-04-22
- Volume
- 83
- Issue
- 15
- ISSN
- 1098-0121 (ISSN-L)
- 1550-235X
- OpenAlex
- W2083956694
- Cited by
- 396
- References
- 24
- FWCI
- 2.94
- Type
- journal article
- Language
- en
- Open access
- closed
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