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New empirical model for the structural properties of silicon
Abstract
An empirical interatomic potential for covalent systems is proposed, incorporating bond order in an intuitive way. The potential has the form of a Morse pair potential, but with the bond-strength parameter depending upon local environment. A model for Si accurately describes bonding and geometry for may structures, including highly rebonded surfaces.
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Citations
Cited by
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Iijima, S., Brabec, C., Maiti, A., & Bernholc, J. (1996) · The Journal of Chemical Physics · doi:10.1063/1.470966 · Cited by 1,250
Machine Learning for Molecular Simulation
Noé, F., Tkatchenko, A., Müller, K.-R., & Clementi, C. (2020) · Annual Review of Physical Chemistry · doi:10.1146/annurev-physchem-042018-052331 · Cited by 956
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
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
Metadata
- Authors
- Tersoff, J.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 1986-02-10
- Volume
- 56
- Issue
- 6
- Pages
- 632-635
- ISSN
- 0031-9007 (ISSN-L)
- OpenAlex
- W2039959185
- Cited by
- 1,387
- References
- 0
- FWCI
- 22.97
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: New empirical model for the structural properties of silicon. https://app.cochat.ai/papers/10.1103/physrevlett.56.632
- The paper
- Tersoff, J. (1986). New empirical model for the structural properties of silicon. Physical Review Letters. https://doi.org/10.1103/physrevlett.56.632
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
