  The sudden arrest of flow by formation of a stable arch over an outlet is a
unique and characteristic feature of granular materials. Previous work suggests
that grains near the outlet randomly sample configurational flow microstates
until a clog-causing flow microstate is reached. However, factors that lead to
clogging remain elusive. Here we experimentally observe over 50,000 clogging
events for a tridisperse mixture of quasi-2D circular grains, and utilize a
variety of machine learning (ML) methods to search for predictive signatures of
clogging microstates. This approach fares just modestly better than chance.
Nevertheless, our analysis using linear Support Vector Machines (SVMs)
highlights the position of potential arch cornerstones as a key factor in
clogging likelihood. We verify this experimentally by varying the position of a
fixed (cornerstone) grain, and show that such a grain dictates the size of
feasible flow-ending arches, and thus the time and mass of each flow.
Positioning this grain correctly can even increase the ejected mass by over
50%. Our findings demonstrate that interpretable ML algorithms like SVMs can
uncover meaningful physics even when their predictive power is below the
standards of conventional ML practice.
