期刊
PHYSICAL REVIEW B
卷 99, 期 6, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevB.99.064114
关键词
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资金
- Russian Science Foundation [18-13-00479, 16-13-10459]
- U.S. Department of Energy (DOE) Office of Science [DE-AC52-06NA25396]
- Sandia National Laboratories [DE-NA-0003525]
- Russian Science Foundation [18-13-00479] Funding Source: Russian Science Foundation
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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