  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.
