| Autor: |
ZHAO Liang, NIU Hongwei, JING Yuhang |
| Jazyk: |
čínština |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Journal of Aeronautical Materials, Vol 43, Iss 6, Pp 80-89 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
1005-5053 |
| DOI: |
10.11868/j.issn.1005-5053.2023.000011 |
| Popis: |
Interatomic potential is a key component of large-scale atomic simulation of materials. For scientific problems in complex environments such as high temperature，high pressure and irradiation，the interactions between atoms are often very complex. The empirical force field only considers two-body，three-body or four-body interactions between atoms. The physical assumption is simple，and it is often difficult to accurately describe the potential energy surface of complex environment. Machine learning force fields can obtain potential energy surfaces that are more accurate than empirical force fields. In this paper，a machine learning force field based on physical model is proposed for BaZrO3，the most common perovskite system，to describe the static properties，phase stability and mechanical properties of BaZrO3. The density functional theory database is used to train the machine learning force field based on physical model，and the static properties，phase stability and mechanical properties are calculated. For the static properties，the elastic constants C11，C12 and C44 were computed using both a pure machine-learning force field and a machine-learning force field based on a physical model，and the simulation results were much better than the empirical force field when compared to the DFT with errors of 0.34%，8.75% and 10.71% for the former，and 0.34%，2.5% and 7.14% for the latter. As for the phase stability，it is found that the machine learning force field based on physical model inherits the advantage of the empirical force field in maintaining the phase stability，which is better than the pure machine learning force field. For mechanical properties，Young's modulus of four different crystal directions of BaZrO3 are calculated. It was found that the errors between the calculated and experimental values for the machine learning force field and the machine learning force field based on the physical model were 9.22% and 1.6%，which were much lower than the results of the empirical force field. It can be seen that integrating physical models into the development of machine learning force field is an important way to improve the accuracy of atomic simulations. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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