Advertisement

Mimicking complex dislocation dynamics by interaction networks

  • Henri Salmenjoki
  • Mikko J. Alava
  • Lasse Laurson
Regular Article
  • 11 Downloads
Part of the following topical collections:
  1. Topical issue: Complex Systems Science meets Matter and Materials

Abstract

Two-dimensional discrete dislocation models exhibit complex dynamics in relaxation and under external loading. This is manifested both in the time-dependent velocities of individual dislocations and in the ensemble response, the strain rate. Here we study how well this complexity may be reproduced using so-called Interaction Networks, an artificial intelligence method for learning the dynamics of complex interacting systems. We test how to learn such networks using creep data, and show results on reproducing individual and collective dislocation velocities. The quality of reproducing the interaction kernel is discussed.

References

  1. 1.
    L. Zdeborová, Nat. Phys. 13, 420 (2017) CrossRefGoogle Scholar
  2. 2.
    J. Behler, J. Chem. Phys. 145, 170901 (2016) ADSCrossRefGoogle Scholar
  3. 3.
    V. Botu, R. Ramprasad, Phys. Rev. B 92, 094306 (2015) ADSCrossRefGoogle Scholar
  4. 4.
    T.D. Huan, R. Batra, J. Chapman, S. Krishnan, L. Chen, R. Ramprasad, NPJ Comput. Mater. 3, 37 (2017) ADSCrossRefGoogle Scholar
  5. 5.
    S. Wiewel, M. Becher, N. Thuerey, https://doi.org/arXiv:1802.10123 (2018)
  6. 6.
    J. Pathak, B. Hunt, M. Girvan, Z. Lu, E. Ott, Phys. Rev. Lett. 120, 024102 (2018) ADSCrossRefGoogle Scholar
  7. 7.
    M. Koch-Janusz, Z. Ringel, Nat. Phys. 14, 578 (2018) CrossRefGoogle Scholar
  8. 8.
    J. Carrasquilla, R.G. Melko, Nat. Phys. 13, 431 (2017) CrossRefGoogle Scholar
  9. 9.
    E.P.L. van Nieuwenburg, Y.H. Liu, S.D. Huber, Nat. Phys. 13, 435 (2017) CrossRefGoogle Scholar
  10. 10.
    S.J. Wetzel, Phys. Rev. E, 96, 022140 (2017) ADSCrossRefGoogle Scholar
  11. 11.
    S. Papanikolaou NPJ Comput. Mater. 4, 27 (2018) ADSCrossRefGoogle Scholar
  12. 12.
    P. Battaglia, R. Pascanu, M. Lai, D.J. Rezende, in Advances in Neural Information Processing Systems (NIPS, 2016), Vol. 29, p. 4502 Google Scholar
  13. 13.
    M.C. Miguel, A. Vespignani, S. Zapperi, J. Weiss, J.R. Grasso, Nature 410, 667 (2001) ADSCrossRefGoogle Scholar
  14. 14.
    M. Zaiser, Adv. Phys. 55, 185 (2006) ADSCrossRefGoogle Scholar
  15. 15.
    J. Rosti, J. Koivisto, L. Laurson, M.J. Alava, Phys. Rev. Lett. 105, 100601 (2010) ADSCrossRefGoogle Scholar
  16. 16.
    P.D. Ispánovity, L. Laurson, M. Zaiser, I. Groma, S. Zapperi, M.J. Alava, Phys. Rev. Lett. 112, 235501 (2014) ADSCrossRefGoogle Scholar
  17. 17.
    S. Janićević, M. Ovaska, M.J. Alava, L. Laurson, J. Stat. Mech. Theory Exp. 2015, P07016 (2015) CrossRefGoogle Scholar
  18. 18.
    S. Papanikolaou, Y. Cui, N. Ghoniem, Model. Simul. Mater. Sci. Eng. 26, 013001 (2017) ADSCrossRefGoogle Scholar
  19. 19.
    J.P. Hirth, J. Lothe, Theory of dislocations (Krieger, Malabar, FL, 1982) Google Scholar
  20. 20.
    P. Moretti, M.-C. Miguel, M. Zaiser, S. Zapperi, Phys. Rev. B 69, 214103 (2004) ADSCrossRefGoogle Scholar

Copyright information

© EDP Sciences, SIF, Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Henri Salmenjoki
    • 1
  • Mikko J. Alava
    • 1
  • Lasse Laurson
    • 1
    • 2
  1. 1.Department of Applied PhysicsAalto UniversityAaltoFinland
  2. 2.Laboratory of Physics, Tampere University of TechnologyTampereFinland

Personalised recommendations