The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Modified embedded-atom potentials for cubic materials and impurities
Abstract
In a comprehensive study, the modified embedded-atom method is extended to a variety of cubic materials and impurities. In this extension, all functions are analytic and computationally simple. The basic equations of the method are developed and applied to 26 elements: ten fcc, ten bcc, three diamond cubic, and three gaseous materials. The materials modeled include metals, semiconductors, and diatomic gases, all of which exhibit different types of bonding. Properties of these materials, including equation of state, elastic moduli, structural energies and lattice constants, simple defects, and surfaces, are calculated. The formalism for applying the method to combinations of these elements is developed and applied to the calculation of dilute heats of solution. In all cases, comparison is made to experiment or higher-level calculations when possible.
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Cited by
Thompson, A. P., Aktulga, H. M., Berger, R., Bolintineanu, D. S., Brown, W. M., Crozier, P. S., in 't Veld, P. J., Kohlmeyer, A., Moore, S. G., Nguyen, T. D., et al. (2022) · Computer Physics Communications · doi:10.1016/j.cpc.2021.108171 · Cited by 12,598
The ReaxFF reactive force-field: development, applications and future directions
Senftle, T. P., Hong, S., Islam, M. M., Kylasa, S. B., Zheng, Y., Shin, Y. K., Junkermeier, C., Engel-Herbert, R., Janik, M. J., Aktulga, H. M., et al. (2016) · npj Computational Materials · doi:10.1038/npjcompumats.2015.11 · Cited by 2,363
Atomic-level structure and structure–property relationship in metallic glasses
Cheng, Y. Q., & Ma, E. (2011) · Progress in Materials Science · doi:10.1016/j.pmatsci.2010.12.002 · Cited by 1,771
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
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
- Baskes, M. I.
- Venue
- Physical Review B
- Publisher
- American Physical Society (APS)
- Published
- 1992-08-01
- Volume
- 46
- Issue
- 5
- Pages
- 2727-2742
- ISSN
- 0163-1829 (ISSN-L)
- 1095-3795
- OpenAlex
- W2036034530
- Cited by
- 2,093
- References
- 18
- FWCI
- 39.28
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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