DOI QR코드

DOI QR Code

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)
  • 투고 : 2021.12.06
  • 심사 : 2022.02.20
  • 발행 : 2022.08.25

초록

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.

키워드

과제정보

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).

참고문헌

  1. Z. Dal, 17 - thorium molten salt reactor nuclear energy system (tmsr), in: T.J. Dolan (Ed.), Molten Salt Reactors and Thorium Energy, 1st, Woodhead Publishing, 2017, pp. 531-540.
  2. T. Burehell, 4.10 - radiation effects in graphite, in: R.J. Konings (Ed.), Comprehensive Nuclear Materials, Elsevier, Oxford, 2021, pp. 299-324.
  3. R. Taylor, B. Kelly, K. Gilchrist, The thermal conductivity of fast neutron irradiated graphite, J. Phys. Chem. Solid. 30 (9) (1969) 2251-2267. https://doi.org/10.1016/0022-3697(69)90152-8
  4. A. Perks, J. Simmons, Dimensional changes and radiation creep of graphite at very high neutron doses, Carbon 4 (1) (1966) 85-98. https://doi.org/10.1016/0008-6223(66)90013-3
  5. Irradiation Damage in Graphite Due to Fast Neutrons in Fission and Fusion Systems, No. 1154 in TECDOC Series, INTERNATIONAL ATOMIC ENERGY AGENCY, Vienna, 2000.
  6. G. Haag, Properties of ATR-2E Graphite and Property Changes Due to Fast Neutron Irradiation, 2005.
  7. A.A. Campbell, Y. Katoh, Report on Effects of Irradiation on Material Ig-110, prepared for toyo tanso co, ltd, 2017.
  8. Z. Zhou, W. Bouwman, H. Schut, T. van Staveren, M. Heijna, C. Pappas, Influence of neutron irradiation on the microstructure of nuclear graphite: an X-ray diffraction study, J. Nucl. Mater. 487 (2017) 323-330. https://doi.org/10.1016/j.jnucmat.2017.02.004
  9. R. McElroy, T. Williams, F. Boydon, B. Hemsworth, Low temperature embrittlement of LWR RPV support structures, Int. J. Pres. Ves. Pip. 54 (1993) 171-211. https://doi.org/10.1016/0308-0161(93)90133-E
  10. M.-H. Kim, Utilization of AGN-201Kfor education and research in Korea, Tech. Rep. (2011).
  11. Management of Research Reactor Ageing, No. 792 in TECDOC Series, INTERNATIONAL ATOMIC ENERGY AGENCY, Vienna, 1995.
  12. G. Was, R. Averback, Radiation Damage Using Ion Beams 1, Comprehensive Nuclear Materials, 2012, pp. 195-221.
  13. T.C. O'Connor, J. Andzelm, M.O. Robbins, Airebo-m: a reactive model for hydrocarbons at extreme pressures, J. Chem. Phys. 142 (2) (2015), 024903. https://doi.org/10.1063/1.4905549
  14. A. Gulans, A.V. Krasheninnikov, M.J. Puska, R.M. Nieminen, Bound and free self-interstitial defects in graphite and bilayer graphene: a computational study, Phys. Rev. B 84 (2011), 024114. https://doi.org/10.1103/physrevb.84.024114
  15. J. Graser, S.K. Kauwe, T.D. Sparks, Machine learning and energy minimization approaches for crystal structure predictions: a review and new horizons, Chem. Mater. 30 (11) (2018) 3601-3612. https://doi.org/10.1021/acs.chemmater.7b05304
  16. J. Schmidt, M.R.G. Marques, S. Botti, M.A. L Marques, Recent advances and applications of machine learning in solid-state materials science, npj Computational Materials 5 (1) (2019) 83. https://doi.org/10.1038/s41524-019-0221-0
  17. J. Behler, M. Parrinello, Generalized neural-network representation of highdimensional potential energy surfaces, Phys. Rev. Lett. 98 (2007) 146401. https://doi.org/10.1103/PhysRevLett.98.146401
  18. M. Babar, H.L. Parks, G. Houchins, V. Viswanathan, An accurate machine learning calculator for the lithium-graphite system, J. Phys.: Energy 3 (1) (2020), 014005. https://doi.org/10.1088/2515-7655/abc96f
  19. P. Rowe, V.L. Deringer, P. Gasparotto, G. Csanyi, A. Michaelides, An accurate and transferable machine learning potential for carbon, J. Chem. Phys. 153 (3) (2020), 034702. https://doi.org/10.1063/5.0005084
  20. G.H. Kinchin, R.S. Pease, The displacement of atoms in solids by radiation, Rep. Prog. Phys. 18 (1) (1955) 1-51. https://doi.org/10.1088/0034-4885/18/1/301
  21. Tokio Fukahori, Yosuke Iwamoto, A Calculation Method of PKA, KERMA and Dpa from Evaluated Nuclear Data with an Effective Single-Particle Emission Approximation (ESPEA) and Introduction of Event Generator Mode in Phits Code, 2012.
  22. S. Signetti, K. Kang, N.M. Pugno, S. Ryu, Atomistic modelling of the hypervelocity dynamics of shockcompressed graphite and impacted graphene armours, Comput. Mater. Sci. 170 (2019) 109152. https://doi.org/10.1016/j.commatsci.2019.109152
  23. N.D. Orekhov, V.V. Stegailov, Molecular-dynamics based insights into the problem of graphite melting, J. Phys. Conf. 653 (2015), 012090. https://doi.org/10.1088/1742-6596/653/1/012090
  24. A. David, A.D. Nicola, U. Tartaglino, G. Milano, G. Raos, Viscoelasticity of short polymer liquids from atomistic simulations, J. Electrochem. Soc. 166 (9) (2019). B3246-B3256. https://doi.org/10.1149/2.0371909jes
  25. S. Plimpton, Fast parallel algorithms for short-range molecular dynamics, J. Comput. Phys. 117 (1) (1995) 1-19. https://doi.org/10.1006/jcph.1995.1039
  26. L. Li, S. Reich, J. Robertson, Defect energies of graphite: density-functional calculations, Phys. Rev. B 72 (2005) 184109. https://doi.org/10.1103/physrevb.72.184109
  27. A. Stone, D. Wales, Theoretical studies of icosahedral c60 and some related species, Chem. Phys. Lett. 128 (5) (1986) 501-503. https://doi.org/10.1016/0009-2614(86)80661-3
  28. A. Stukowski, Visualization and analysis of atomistic simulation data with OVITO-the Open Visualization Tool, Model. Simulat. Mater. Sci. Eng. 18 (1) (2010).
  29. P.-L. Tu, J.-Y. Chung, A new decision-tree classification algorithm for machine learning, in: TAI'92 Proceedings Fourth International Conference on Tools with Artificial Intelligence, IEEE Computer Society, 1992, pp. 370-371.
  30. A. Priyam, G. Abhijeeta, A. Rathee, S. Srivastava, Comparative analysis of decision tree classification algorithms, International Journal of current engineering and technology 3 (2) (2013) 334-337.
  31. H.H. Patel, P. Prajapati, Study and analysis of decision tree based classification algorithms, Int. J. Comput. Sci. Eng. 6 (10) (2018) 74-78.
  32. M. Brijain, R. Patel, M. Kushik, K. Rana, A Survey on Decision Tree Algorithm for Classification, Computer Science, 2014.
  33. L. Rokach, O. Maimon, Top-down induction of decision trees classifiers-a survey, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 35 (4) (2005) 476-487. https://doi.org/10.1109/TSMCC.2004.843247
  34. S. Islam, S.S.Z. Ashraf, Point and space groups of graphene, Resonance 24 (4) (2019) 445-457. https://doi.org/10.1007/s12045-019-0797-1