The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Accelerating crystal structure prediction by machine-learning interatomic potentials with active learning
Open accessAbstract
We propose a methodology for crystal structure prediction that is based on the evolutionary algorithm USPEX and the machine-learning interatomic potentials actively learning on-the-fly. Our methodology allows for an automated construction of an interatomic interaction model from scratch, replacing the expensive density functional theory (DFT) and giving a speedup of several orders of magnitude. Predicted low-energy structures are then tested on DFT, ensuring that our machine-learning model does not introduce any prediction error. We tested our methodology on prediction of crystal structures of carbon, high-pressure phases of sodium, and boron allotropes, including those that have more than 100 atoms in the primitive cell. All the the main allotropes have been reproduced, and a hitherto unknown 54-atom structure of boron has been predicted with very modest computational effort.
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Cites
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Cited by
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-citationOn-the-fly active learning of interpretable Bayesian force fields for atomistic rare events
Vandermause, J., Torrisi, S. B., Batzner, S., Xie, Y., Sun, L., Kolpak, A. M., & Kozinsky, B. (2020) · npj Computational Materials · doi:10.1038/s41524-020-0283-z · Cited by 459
Emerging materials intelligence ecosystems propelled by machine learning
Batra, R., Song, L., & Ramprasad, R. (2020) · Nature Reviews Materials · doi:10.1038/s41578-020-00255-y · Cited by 446
Innovative Materials Science via Machine Learning
Gao, C., Min, X., Fang, M., Tao, T., Zheng, X., Liu, Y., Wu, X., & Huang, Z. (2022) · Advanced Functional Materials · doi:10.1002/adfm.202108044 · Cited by 198
Machine learning in materials design: Algorithm and application*
Song, Z., Chen, X., Meng, F., Cheng, G., Wang, C., Sun, Z., & Yin, W.-J. (2020) · Chinese Physics B · doi:10.1088/1674-1056/abc0e3 · Cited by 63
Generative AI for crystal structures: A review
De Breuck, P.-P., Wang, H.-C., Rignanese, G.-M., Botti, S., & Marques, M. A. L. (2025) · npj Computational Materials · doi:10.1038/s41524-025-01881-2 · Cited by 24
From QM/MM to ML/MM: A new era in multiscale modeling
Grassano, J. S., Pickering, I., Roitberg, A. E., Estrin, D. A., & Semelak, J. A. (2025) · Chemical Physics Reviews · doi:10.1063/5.0260078 · Cited by 20
“Dividing and Conquering” and “Caching” in Molecular Modeling
Cao, X., & Tian, P. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22095053 · Cited by 10
Metadata
- Authors
- Podryabinkin, E. V., Tikhonov, E. V., Shapeev, A. V., & Oganov, A. R.
- Venue
- Physical Review B
- Publisher
- American Physical Society (APS)
- Published
- 2019-02-27
- Volume
- 99
- Issue
- 6
- ISSN
- 2469-9950 (ISSN-L)
- 2469-9969
- OpenAlex
- W2788484525
- Cited by
- 403
- References
- 6
- FWCI
- 15.28
- Type
- journal article
- Language
- en
- Open access
- green
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