Computational Materials Science2022,Vol.20211.DOI:10.1016/j.commatsci.2021.110960

Machine-learning interatomic potential for radiation damage effects in bcc-iron

Wang, Yi Liu, Jianbo Li, Jiahao Mei, Jinna Li, Zhengcao Lai, Wensheng Xue, Fei
Computational Materials Science2022,Vol.20211.DOI:10.1016/j.commatsci.2021.110960

Machine-learning interatomic potential for radiation damage effects in bcc-iron

Wang, Yi 1Liu, Jianbo 2Li, Jiahao 2Mei, Jinna 1Li, Zhengcao 2Lai, Wensheng 2Xue, Fei1
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作者信息

  • 1. Suzhou Nucl Power Res Inst
  • 2. Tsinghua Univ
  • 折叠

Abstract

We introduce a machine-learning interatomic potential for bcc iron based on the moment tensor potential framework and a hybridization scheme of distinct sub-potentials. With an orientation on radiation damage effects, the potential shows good transferability from properties relevant to collision cascade to those relevant to plasticity. Specifically, the potential accurately reproduces the short-range repulsive interactions, the generalized stacking fault energies, the dislocation core structures and the formation energies of defect clusters. The general purposed applicability of the potential enables simulation of radiation damage effects in bcc iron with an accurate and an unprecedentedly unified theoretical model.

Key words

Interatomic potential/Machine-learning potential/Fe/Radiation damage/Molecular dynamics/AB-INITIO CALCULATIONS/INTERSTITIAL CLUSTERS/MOLECULAR-DYNAMICS/STACKING-FAULTS/CORE-STRUCTURE/ALPHA-FE/PHASE/POINTS

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出版年

2022
Computational Materials Science

Computational Materials Science

EISCI
ISSN:0927-0256
被引量6
参考文献量68
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