When deformed beyond their elastic limits, crystalline solids flow plastically via particle rearrangements localized around structural defects. Disordered solids also flow, but without obvious structural defects. We link structure to plasticity in disordered solids via a microscopic structural quantity, “softness,” designed by machine learning to be maximally predictive of rearrangements. Experimental results and computations enabled us to measure the spatial correlations and strain response of softness, as well as two measures of plasticity: the size of rearrangements and the yield strain. All four quantities maintained remarkable commonality in their values for disordered packings of objects ranging from atoms to grains, spanning seven orders of magnitude in diameter and 13 orders of magnitude in elastic modulus. These commonalities link the spatial correlations and strain response of softness to rearrangement size and yield strain, respectively.
We experimentally characterize heterogeneous nonexponential relaxation in bidisperse supercooled colloidal liquids utilizing a recent concept called "softness" [Phys. Rev. Lett. 114, 108001(2015)]. Particle trajectory and structure data enable classification of particles into subgroups with different local environments and propensities to hop. We determine residence times, tR, between particle hops and show that tR derived from particles in the same softness subgroup are exponentially distributed. Using the mean residence timetR for each softness subgroup, and a Kramers' reaction rate model, we estimate the activation energy barriers, E b , for particle hops, and show that bothtR and E b are monotonic functions of softness. Finally, we derive information about the combinations of large and small particle neighbors that determine particle softness, and we explicitly show that multiple exponential relaxation channels in the supercooled liquid give rise to its non-exponential behavior.
Amorphous solids are critical in the design and production of nanoscale devices, but under strong confinement these materials exhibit changes in their mechanical properties which are not well understood. Phenomenological models explain these properties by postulating an underlying defect structure in these materials but do not detail the microscopic properties of these defects. Using machine learning methods, we identify mesoscale defects that lead to shear banding in polymer nanopillars well below the glass transition temperature as a function of pillar diameter. Our results show that the primary structural features responsible for shear banding on this scale are fluctuations in the diameter of the pillar. Surprisingly, these fluctuations are quite small compared to the diameter of the pillar, less than half of a particle diameter in size. At intermediate pillar diameters, we find that these fluctuations tend to concentrate along the minor axis of shear band planes. We also see the importance of mean "softness" as a classifier of shear banding grow as a function of pillar diameter. Softness is a new field that characterizes local structure and is highly correlated with particle-level dynamics such 1 arXiv:1809.06922v1 [cond-mat.soft] 18 Sep 2018 that softer particles are more likely to rearrange. This demonstrates that softness, a quantity that relates particle-level structure to dynamics on short time and length scales, can predict large time and length scale phenomena related to material failure.Keywords fracture, shear banding, confinement, polymer, disordered materials, softness, machine learning There are numerous applications where amorphous organic materials are used in highly confined geometries, including as polymer photoresists in semiconductor manufacturing, 1 the active layers in organic light-emitting diodes, 2,3 and in polymer nanocomposites at high loadings of nanoparticles. 4,5 In many of these applications, in particular semiconductor manufacturing, the mechanical properties of the confined material are of utmost importance.Generally speaking, amorphous materials have many unique mechanical properties including high strength, high stiffness, and low mechanical dissipation. 6-12 These properties make them desirable in a number of engineering applications; however, their use is hindered by their tendency to fail in a brittle manner. 13-17 A hallmark of these catastrophic failure modes is shear banding, the localization of shear strain to a narrow region which develops during deformation. 18,19 Shear banding has been experimentally observed in many types of amorphous materials including: granular materials, 20,21 bubble rafts, 22,23 complex fluids, 24,25 and metallic glasses. 26,27 Although shear banding has been extensively studied in the bulk using phenomenological models, a microscopic theory of shear banding has proven elusive. The phenomenological models that describe shear banding can broadly be classified into two types. Solid mechanics models postulate some constitutive relations about how ...
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