The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Accuracy and transferability of Gaussian approximation potential models for tungsten
Abstract
We introduce interatomic potentials for tungsten in the bcc crystal phase and its defects within the Gaussian approximation potential framework, fitted to a database of first-principles density functional theory calculations. We investigate the performance of a sequence of models based on databases of increasing coverage in configuration space and showcase our strategy of choosing representative small unit cells to train models that predict properties observable only using thousands of atoms. The most comprehensive model is then used to calculate properties of the screw dislocation, including its structure, the Peierls barrier and the energetics of the vacancy-dislocation interaction. All software and raw data are available at www.libatoms.org.
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Self-citationA simple empirical N-body potential for transition metals
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Self-citationFast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
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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
Machine learning in materials informatics: recent applications and prospects
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0056-5 · Cited by 1,945
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
Smith, J. S., Isayev, O., & Roitberg, A. E. (2017) · Chemical Science · doi:10.1039/c6sc05720a · Cited by 1,921
Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials
Shapeev, A. V. (2016) · Multiscale Modeling & Simulation · doi:10.1137/15m1054183 · Cited by 1,510
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
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Metadata
- Authors
- Szlachta, W. J., Bartók, A. P., & Csányi, G.
- Venue
- Physical Review B
- Publisher
- American Physical Society (APS)
- Published
- 2014-09-24
- Volume
- 90
- Issue
- 10
- ISSN
- 1098-0121 (ISSN-L)
- 1550-235X
- OpenAlex
- W1972135189
- Cited by
- 327
- References
- 33
- FWCI
- 5.06
- Type
- journal article
- Language
- en
- Open access
- closed
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