Mimicking complex dislocation dynamics by interaction networks

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.

This is a preview of subscription content, log in to check access.

References

  1. 1.

    L. Zdeborová, Nat. Phys. 13, 420 (2017)

    Article  Google Scholar 

  2. 2.

    J. Behler, J. Chem. Phys. 145, 170901 (2016)

    ADS  Article  Google Scholar 

  3. 3.

    V. Botu, R. Ramprasad, Phys. Rev. B 92, 094306 (2015)

    ADS  Article  Google Scholar 

  4. 4.

    T.D. Huan, R. Batra, J. Chapman, S. Krishnan, L. Chen, R. Ramprasad, NPJ Comput. Mater. 3, 37 (2017)

    ADS  Article  Google 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)

    ADS  Article  Google Scholar 

  7. 7.

    M. Koch-Janusz, Z. Ringel, Nat. Phys. 14, 578 (2018)

    Article  Google Scholar 

  8. 8.

    J. Carrasquilla, R.G. Melko, Nat. Phys. 13, 431 (2017)

    Article  Google Scholar 

  9. 9.

    E.P.L. van Nieuwenburg, Y.H. Liu, S.D. Huber, Nat. Phys. 13, 435 (2017)

    Article  Google Scholar 

  10. 10.

    S.J. Wetzel, Phys. Rev. E, 96, 022140 (2017)

    ADS  Article  Google Scholar 

  11. 11.

    S. Papanikolaou NPJ Comput. Mater. 4, 27 (2018)

    ADS  Article  Google Scholar 

  12. 12.

    P. Battaglia, R. Pascanu, M. Lai, D.J. Rezende, inAdvances in Neural Information Processing Systems (NIPS, 2016), Vol. 29, p. 4502

  13. 13.

    M.C. Miguel, A. Vespignani, S. Zapperi, J. Weiss, J.R. Grasso, Nature 410, 667 (2001)

    ADS  Article  Google Scholar 

  14. 14.

    M. Zaiser, Adv. Phys. 55, 185 (2006)

    ADS  Article  Google Scholar 

  15. 15.

    J. Rosti, J. Koivisto, L. Laurson, M.J. Alava, Phys. Rev. Lett. 105, 100601 (2010)

    ADS  Article  Google Scholar 

  16. 16.

    P.D. Ispánovity, L. Laurson, M. Zaiser, I. Groma, S. Zapperi, M.J. Alava, Phys. Rev. Lett. 112, 235501 (2014)

    ADS  Article  Google Scholar 

  17. 17.

    S. Janićević, M. Ovaska, M.J. Alava, L. Laurson, J. Stat. Mech. Theory Exp. 2015, P07016 (2015)

    Article  Google Scholar 

  18. 18.

    S. Papanikolaou, Y. Cui, N. Ghoniem, Model. Simul. Mater. Sci. Eng. 26, 013001 (2017)

    ADS  Article  Google Scholar 

  19. 19.

    J.P. Hirth, J. Lothe,Theory of dislocations (Krieger, Malabar, FL, 1982)

  20. 20.

    P. Moretti, M.-C. Miguel, M. Zaiser, S. Zapperi, Phys. Rev. B 69, 214103 (2004)

    ADS  Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Henri Salmenjoki.

Additional information

Contribution to the Topical Issue “Complex Systems Science meets Matter and Materials”, edited by Stefano Zapperi.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Salmenjoki, H., Alava, M.J. & Laurson, L. Mimicking complex dislocation dynamics by interaction networks. Eur. Phys. J. B 91, 275 (2018). https://doi.org/10.1140/epjb/e2018-90419-7

Download citation