The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Extending the accuracy of the SNAP interatomic potential form
Open accessAbstract
The Spectral Neighbor Analysis Potential (SNAP) is a classical interatomic potential that expresses the energy of each atom as a linear function of selected bispectrum components of the neighbor atoms. An extension of the SNAP form is proposed that includes quadratic terms in the bispectrum components. The extension is shown to provide a large increase in accuracy relative to the linear form, while incurring only a modest increase in computational cost. The mathematical structure of the quadratic SNAP form is similar to the embedded atom method (EAM), with the SNAP bispectrum components serving as counterparts to the two-body density functions in EAM. The effectiveness of the new form is demonstrated using an extensive set of training data for tantalum structures. Similar to artificial neural network potentials, the quadratic SNAP form requires substantially more training data in order to prevent overfitting. The quality of this new potential form is measured through a robust cross-validation analysis.
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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
Self-citationRecent 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
Performance and Cost Assessment of Machine Learning Interatomic Potentials
Zuo, Y., Chen, C., Li, X., Deng, Z., Chen, Y., Behler, J., Csányi, G., Shapeev, A. V., Thompson, A. P., Wood, M. A., et al. (2020) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b08723 · Cited by 968
Self-citationAtomic cluster expansion for accurate and transferable interatomic potentials
Drautz, R. (2019) · Physical Review B · doi:10.1103/physrevb.99.014104 · Cited by 959
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
Metadata
- Authors
- Wood, M. A., & Thompson, A. P.
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2018-03-29
- Volume
- 148
- Issue
- 24
- Pages
- 241721-241721
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W2774180927
- Cited by
- 215
- References
- 9
- FWCI
- 5.51
- Type
- journal article
- Language
- en
- Open access
- green
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