Identifying structural signatures of shear banding in model polymer nanopillars

Abstract

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 ...

Cavitation in soft matter

“…In addition, it is challenging to obtain insights into the transition from cavity expansion to crack initiation and propagation using continuum mechanics models. Recent efforts in glasses, another class of disordered solids, have shown that the response and failure of the material are heavily dictated by the local packing (91) and that certain regions in the sample are more prone to failure than others (92,93). A similar question can be posed for disordered soft materials, such as polymer networks: is the local network structure important in determining the initial formation and growth of cavity sites, and what must the size of the cavity be before continuum mechanics results can be accurately applied?…”

Characterising soft matter using machine learning

“…The analysis of the simulation results demonstrated the key role of surface defects in leading to pillar failure. 91 The experiments using spheres, dimers and ellipses demonstrated that a naive implementation of the ''softness'' concept worked reasonably for spheres and ellipses but quite poorly for dimers. Harrington and coworkers modified the family of structure functions in order to better match the arrangements of anisotropic particles.…”

“…89 Subsequent studies have applied the learning of ''softness'' to simulations of thin polymer films and pillars and to the analysis of granular experiments using spheres, dimers and ellipsoids. [90][91][92] In the former case, Sussman and coworkers found that the enhanced dynamics close to the surface of a polymer thin film is uncorrelated with the ''softness'' parameter. The SVM approach worked as before for predicting which sites would be likely to move, it just failed to identify any changes close to the free surface (or to the substrate).…”

Deep learning for automated classification and characterization of amorphous materials

“…48 Convolutional neural networks, which use convolutional layers in addition to the fully connected layers that characterize a basic feed forward network, have been shown to excel at computer vision tasks ranging from assessing cancer risk in radiology scans to galaxy morphology classification in telescope images. 29,49,50 As explained in §I, CNNs have also been used to classify a variety of materials, including crystal structures and Ising model configurations.…”

“…There have also been some recent studies that have successfully used machine learning to uncover previously unknown relationships between structure and dynamics in glassy materials. [22][23][24][25][26][27][28][29][30] In these approaches, a supervised machine learning algorithm, called the support vector machine, is used to define a metric, called "softness," that identifies populations of particles that are likely to dynamically rearrange. In this context, "softness" is used to link structure and dynamics, but it is not used to directly identify local structural features or to classify different material structures.…”