The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Machine learning based interatomic potential for amorphous carbon
Open accessAbstract
We introduce a Gaussian approximation potential (GAP) for atomistic simulations of liquid and amorphous elemental carbon. Based on a machine learning representation of the density-functional theory (DFT) potential-energy surface, such interatomic potentials enable materials simulations with close-to DFT accuracy but at much lower computational cost. We first determine the maximum accuracy that any finite-range potential can achieve in carbon structures; then, using a hierarchical set of two-, three-, and many-body structural descriptors, we construct a GAP model that can indeed reach the target accuracy. The potential yields accurate energetic and structural properties over a wide range of densities; it also correctly captures the structure of the liquid phases, at variance with a state-of-the-art empirical potential. Exemplary applications of the GAP model to surfaces of ``diamondlike'' tetrahedral amorphous carbon ($\mathit{ta}$-C) are presented, including an estimate of the amorphous material's surface energy and simulations of high-temperature surface reconstructions (``graphitization''). The presented interatomic potential appears to be promising for realistic and accurate simulations of nanoscale amorphous carbon structures.
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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
Unke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., & Müller, K.-R. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01111 · Cited by 1,647
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-citationFour Generations of High-Dimensional Neural Network Potentials
Behler, J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00868 · Cited by 873
Metadata
- Authors
- Deringer, V. L., & Csányi, G.
- Venue
- Physical Review B
- Publisher
- American Physical Society (APS)
- Published
- 2017-03-03
- Volume
- 95
- Issue
- 9
- ISSN
- 2469-9950 (ISSN-L)
- 2469-9969
- OpenAlex
- W2566642125
- Cited by
- 669
- References
- 9
- FWCI
- 26.08
- Type
- journal article
- Language
- en
- Open access
- green
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