The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
High-dimensional neural network potentials for metal surfaces: A prototype study for copper
Abstract
The atomic environments at metal surfaces differ strongly from the bulk, and, in particular, in case of reconstructions or imperfections at ``real surfaces,'' very complicated atomic configurations can be present. This structural complexity poses a significant challenge for the development of accurate interatomic potentials suitable for large-scale molecular dynamics simulations. In recent years, artificial neural networks (NN) have become a promising new method for the construction of potential-energy surfaces for difficult systems. In the present work, we explore the applicability of such high-dimensional NN potentials to metal surfaces using copper as a benchmark system. A detailed analysis of the properties of bulk copper and of a wide range of surface structures shows that NN potentials can provide results of almost density functional theory (DFT) quality at a small fraction of the computational costs.
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Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
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Self-citationGaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Bartók, A. P., Payne, M. C., Kondor, R., & Csányi, G. (2010) · Physical Review Letters · doi:10.1103/physrevlett.104.136403 · Cited by 3,242
Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
Behler, J. (2011) · Physical Chemistry Chemical Physics · doi:10.1039/c1cp21668f · Cited by 781
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Cited by
Recent advances and applications of machine learning in solid-state materials science
Schmidt, J., Marques, M. R. G., Botti, S., & Marques, M. A. L. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0221-0 · Cited by 2,607
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
Atomic cluster expansion for accurate and transferable interatomic potentials
Drautz, R. (2019) · Physical Review B · doi:10.1103/physrevb.99.014104 · Cited by 959
Constructing high‐dimensional neural network potentials: A tutorial review
Behler, J. (2015) · International Journal of Quantum Chemistry · doi:10.1002/qua.24890 · Cited by 884
Self-citationFour Generations of High-Dimensional Neural Network Potentials
Behler, J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00868 · Cited by 873
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Metadata
- Authors
- Artrith, N., & Behler, J.
- Venue
- Physical Review B
- Publisher
- American Physical Society (APS)
- Published
- 2012-01-24
- Volume
- 85
- Issue
- 4
- ISSN
- 1098-0121 (ISSN-L)
- 1550-235X
- OpenAlex
- W2316524229
- Cited by
- 325
- References
- 7
- FWCI
- 3.19
- Type
- journal article
- Language
- en
- Open access
- closed
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