Probing the transition from dislocation jamming to pinning by machine learning
Materials Theory - 2020
Tóm tắt
Collective motion of dislocations is governed by the obstacles they encounter. In pure crystals, dislocations form complex structures as they become jammed by their anisotropic shear stress fields. On the other hand, introducing disorder to the crystal causes dislocations to pin to these impeding elements and, thus, leads to a competition between dislocation-dislocation and dislocation-disorder interactions. Previous studies have shown that, depending on the dominating interaction, the mechanical response and the way the crystal yields change.Here we employ three-dimensional discrete dislocation dynamics simulations with varying density of fully coherent precipitates to study this phase transition − from jamming to pinning − using unsupervised machine learning. By constructing descriptors characterizing the evolving dislocation configurations during constant loading, a confusion algorithm is shown to be able to distinguish the systems into two separate phases. These phases agree well with the observed changes in the relaxation rate during the loading. Our results also give insights on the structure of the dislocation networks in the two phases.
Từ khóa
Tài liệu tham khảo
A. Ardell, Precipitation hardening. Metall. Trans. A. 16(12), 2131–2165 (1985).
A. Arsenlis, W. Cai, M. Tang, M. Rhee, T. Oppelstrup, G. Hommes, T. G. Pierce, V. V. Bulatov, Enabling strain hardening simulations with dislocation dynamics. Model. Simul. Mater. Sci. Eng.15(6), 553 (2007).
A. Arsenlis, D. Parks, Crystallographic aspects of geometrically-necessary and statistically-stored dislocation density. Acta Mater.47(5), 1597–1611 (1999).
C. M. Bishop, Pattern recognition and machine learning (Springer, New York, 2006).
V. V. Bulatov, M. Rhee, W. Cai, Periodic boundary conditions for dislocation dynamics simulations in three dimensions. MRS Online Proc. Libr. Arch.653:, Z1.3 (2000).
J. Carrasquilla, R. G. Melko, Machine learning phases of matter. Nat. Phys.13(5), 431 (2017).
F. F. Csikor, I. Groma, T. Hochrainer, D. Weygand, M. Zaiser, in Proceedings of the 11th International Symposium on Continuum Models and Discrete Systems. On the range of 3d dislocations pair correlations (Mines ParisTech Les PressesParis, 2007).
W. Hu, R. R. Singh, R. T. Scalettar, Discovering phases, phase transitions, and crossovers through unsupervised machine learning: A critical examination. Phys. Rep. E. 95(6), 062122 (2017).
P. D. Ispánovity, L. Laurson, M. Zaiser, I. Groma, S. Zapperi, M. J. Alava, Avalanches in 2d dislocation systems: Plastic yielding is not depinning. Phys. Rev. Lett.112(23), 235501 (2014).
A. Lehtinen, G. Costantini, M. J. Alava, S. Zapperi, L. Laurson, Glassy features of crystal plasticity. Phys. Rep. B. 94(6), 064101 (2016).
A. Lehtinen, F. Granberg, L. Laurson, K. Nordlund, M. J. Alava, Multiscale modeling of dislocation-precipitate interactions in fe: From molecular dynamics to discrete dislocations. Phys. Rep. E. 93(1), 013309 (2016).
P. Mehta, M. Bukov, C. -H. Wang, A. G. Day, C. Richardson, C. K. Fisher, D. J. Schwab, A high-bias, low-variance introduction to machine learning for physicists. Phys. Rep.810:, 1–124 (2019).
M. -C. Miguel, A. Vespignani, M. Zaiser, S. Zapperi, Dislocation jamming and andrade creep. Phys. Rev. Lett.89(16), 165501 (2002).
M. Ovaska, L. Laurson, M. J. Alava, Quenched pinning and collective dislocation dynamics. Sci. Rep.5:, 10580 (2015).
M. Ovaska, T. Paananen, L. Laurson, M. J. Alava, Collective dynamics of dislocations interacting with mobile solute atoms. J. Stat. Mech. Theory Exp.2016(4), 043204 (2016).
Y. Pan, H. Wu, X. Wang, Q. Sun, L. Xiao, X. Ding, J. Sun, E. K. Salje, Rotatable precipitates change the scale-free to scale dependent statistics in compressed ti nano-pillars. Sci. Rep.9(1), 3778 (2019).
S. Papanikolaou, Learning local, quenched disorder in plasticity and other crackling noise phenomena. NPJ Comput. Mater.4(1), 1–7 (2018).
S. Papanikolaou, Y. Cui, N. Ghoniem, Avalanches and plastic flow in crystal plasticity: an overview. Model. Simul. Mater. Sci. Eng.26(1), 013001 (2017).
S. Papanikolaou, M. Tzimas, A. C. Reid, S. A. Langer, Spatial strain correlations, machine learning, and deformation history in crystal plasticity. Phys. Rep. E. 99(5), 053003 (2019).
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay, Scikit-learn: machine learning in python. J. Mach. Learn. Res.12:, 2825–2830 (2011).
H. Salmenjoki, A. Lehtinen, L. Laurson, M. J. Alava, Plastic yielding and deformation bursts in the presence of disorder from coherent precipitates. Phys. Rev. Mater.4(8), 083602 (2020).
A. A. Shirinyan, V. K. Kozin, J. Hellsvik, M. Pereiro, O. Eriksson, D. Yudin, Self-organizing maps as a method for detecting phase transitions and phase identification. Phys. Rep. B. 99(4), 041108 (2019).
R. B. Sills, N. Bertin, A. Aghaei, W. Cai, Dislocation networks and the microstructural origin of strain hardening. Phys. Rep. Lett.121(8), 085501 (2018).
G. Sparks, R. Maaß, Nontrivial scaling exponents of dislocation avalanches in microplasticity. Phys. Rev. Mater.2(12), 120601 (2018).
D. Steinberger, H. Song, S. Sandfeld, Machine learning-based classification of dislocation microstructures. Front. Mater.6:, 141 (2019).
E. P. Van Nieuwenburg, Y. -H. Liu, S. D. Huber, Learning phase transitions by confusion. Nat. Phys.13(5), 435 (2017).
Z. Yang, S. Papanikolaou, A. C. Reid, W. -k. Liao, A. N. Choudhary, C. Campbell, A. Agrawal, Learning to predict crystal plasticity at the nanoscale: Deep residual networks and size effects in uniaxial compression discrete dislocation simulations. Sci. Rep.10(1), 1–14 (2020).
M. Zaiser, Scale invariance in plastic flow of crystalline solids. Adv. Phys.55:, 185–245 (2006).
L. Zdeborová, Machine learning: New tool in the box. Nat. Phys.13(5), 420 (2017).
Y. Zhang, A. H. Ngan, Extracting dislocation microstructures by deep learning. Int. J. Plast.115:, 18–28 (2019).
P. Zhang, O. U. Salman, J. -Y. Zhang, G. Liu, J. Weiss, L. Truskinovsky, J. Sun, Taming intermittent plasticity at small scales. Acta Mater.128:, 351–364 (2017).