Local yield stress statistics in model amorphous solids

Abstract

We develop and extend a method presented by Patinet, Vandembroucq, and Falk [Phys. Rev. Lett. 117, 045501 (2016)PRLTAO0031-900710.1103/PhysRevLett.117.045501] to compute the local yield stresses at the atomic scale in model two-dimensional Lennard-Jones glasses produced via differing quench protocols. This technique allows us to sample the plastic rearrangements in a nonperturbative manner for different loading directions on a well-controlled length scale. Plastic activity upon shearing correlates strongly with the locations of low yield stresses in the quenched states. This correlation is higher in more structurally relaxed systems. The distribution of local yield stresses is also shown to strongly depend on the quench protocol: the more relaxed the glass, the higher the local plastic thresholds. Analysis of the magnitude of local plastic relaxations reveals that stress drops follow exponential distributions, justifying the hypothesis of an average characteristic amplitude often conjectured in mesoscopic or continuum models. The amplitude of the local plastic rearrangements increases on average with the yield stress, regardless of the system preparation. The local yield stress varies with the shear orientation tested and strongly correlates with the plastic rearrangement locations when the system is sheared correspondingly. It is thus argued that plastic rearrangements are the consequence of shear transformation zones encoded in the glass structure that possess weak slip planes along different orientations. Finally, we justify the length scale employed in this work and extract the yield threshold statistics as a function of the size of the probing zones. This method makes it possible to derive physically grounded models of plasticity for amorphous materials by directly revealing the relevant details of the shear transformation zones that mediate this process.

Anisotropic structural predictor in glassy materials

“…Yet, the LHC is a scalar that quantifies the resistance to motion in some unknown direction. That is, like previously proposed structural predictors in glasses (with the exception of [15][16][17]), the LHC misses important tensorial/anisotropic information about the coupling to deformation in a certain direction. For example, an extremely soft spot can be completely decoupled from external forces applied in a certain direction and hence irrelevant for the glass response in this direction.In this Letter, we develop and quantitatively test a the-ory that allows to identify particularly soft glassy structures, explicitly revealing their anisotropic nature and their intrinsic coupling to the direction of externally applied forces.…”

“…We then construct a structural predictor as the product of the local heat capacity and its linear response to external deformation, and show that it offers enhanced predictability of plastic rearrangements under deformation in different directions, compared to the purely scalar predictor.Introduction.-At the heart of resolving the glass mystery resides the need to quantify the disordered structures inherently associated with glasses and to relate them to glass properties and dynamics, most notably spontaneous and driven structural relaxation [1,2]. Numerous attempts to address and meet this grand challenge have been made [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], aiming at defining structural indicators with predictive powers. Achieving this goal would constitute major progress in understanding glassiness and would provide invaluable insight for developing macroscopic theories of deformation and flow of glasses.Recently accumulated evidence suggests that spatially localized soft spots are the loci of glassy relaxation, and hence are highly relevant for glass dynamics.…”

On dissipation in crackling noise systems

“…The tail of the distribution of the energy dissipated during an avalanche corresponds to a power-law decay with exponent ̺ = (τ − ψ h )/(1 − ψ h ) from scaling arguments, a prediction which is exactly satisfied by the BFM, in which case the exponent reduces to the one reported in eq. (19). This result also allows to correctly recover the 4/3 law obtained analytically for the ABBM model.…”

Machine learning characterization of structural defects in amorphous packings of dimers and ellipses

“…Specifically, lowfrequency vibrational modes can identify marginally stable particles that undergo rearrangements [10][11][12]. Recent studies of relaxation events in simulated glassy systems have built on this using similar approaches, including vibrational mean-square displacements [13], local thermal energy [14], and local yield stress [15,16]. In the context of experimental granular systems, one must also contend with the presence of body friction, so a characterization of vibrational modes is generally not possible.…”