期刊
ACTA MATERIALIA
卷 186, 期 -, 页码 467-474出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.actamat.2019.12.060
关键词
Machine learned SNAP force field; ZrB2; Atomistic simulation; Interatomic potential; Temperature effect
资金
- European Union's Horizon 2020 Research and innovation programme [685594]
Determining thermal and physical quantities across a broad temperature domain, especially up to the ultra-high temperature region, is a formidable theoretical and experimental challenge. At the same time it is essential for understanding the performance of ultra-high temperature ceramic (UHTC) materials. Here we present the development of a machine-learning force field for ZrB2, one of the primary members of the UHTC family with a complex bonding structure. The force field exhibits chemistry accuracy for both energies and forces and can reproduce structural, elastic and phonon properties, including thermal expansion and thermal transport. A thorough comparison with available empirical potentials shows that our force field outperforms the competitors with the merits of high accuracy and great versatility. Most importantly, its effectiveness is extended from room temperature to the ultra-high temperature region (up to similar to 2500 K), where measurements are very difficult, costly and some time impossible. Our work demonstrates that machine-learning force fields (MLFF) can be used for simulations of materials in a harsh environment, where no experimental tools are available, but crucial for a number of engineering applications, such as in aerospace, aviation and nuclear. (C) 2020 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
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