Nuclear Engineering and Technology

Korean Nuclear Society (한국원자력학회)
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Defect structure classification of neutron-irradiated graphite using supervised machine learning

  • Kim, Jiho (Department of Nuclear Engineering, Kyung Hee University) ;
  • Kim, Geon (Department of Nuclear Engineering, Kyung Hee University) ;
  • Heo, Gyunyoung (Department of Nuclear Engineering, Kyung Hee University) ;
  • Chang, Kunok (Department of Nuclear Engineering, Kyung Hee University)
  • Received : 2021.12.06
  • Accepted : 2022.02.20
  • Published : 2022.08.25
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Abstract

Molecular dynamics simulations were performed to predict the behavior of graphite atoms under neutron irradiation using large-scale atomic/molecular massively parallel simulator (LAMMPS) package with adaptive intermolecular reactive empirical bond order (AIREBOM) potential. Defect structures of graphite were compared with results from previous studies by means of density functional theory (DFT) calculations. The quantitative relation between primary knock-on atom (PKA) energy and irradiation damage on graphite was calculated. and the effect of PKA direction on the amount of defects is estimated by counting displaced atoms. Defects are classified into four groups: structural defects, energy defects, vacancies, and near-defect structures, where a structural defect is further subdivided into six types by decision tree method which is one of the supervised machine learning techniques.

Keywords

Acknowledgement

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2017M2B2B1072806). This work was also supported by the "Human Resources Program in Energy Technology" of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resources from the Ministry of Trade, Industry Energy, Republic of Korea (No. 20214000000070).

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