Identifying structural signatures of shear banding in model polymer nanopillars

TL;DR

This study uses machine learning to identify mesoscale defects in polymer nanopillars that predict shear banding, revealing that small diameter fluctuations and softness are key indicators of failure in amorphous solids at the nanoscale.

Contribution

It introduces a machine learning approach to link microscopic structural features with shear banding in polymer nanopillars, highlighting the role of diameter fluctuations and softness.

Findings

Diameter fluctuations are less than half a particle in size.

Softness correlates with shear banding propensity.

Fluctuations concentrate along shear band planes at certain diameters.

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 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.