  Granular materials are a ubiquitous yet ill-understood class of media. Many
different approaches and techniques have been developed to understand the many
complex behaviors they exhibit but none have been completely successful. We
present a novel means to understand granular materials, the Hydra String Method
(HSM). This is an efficient and autonomous way to trawl an arbitrary potential
energy surface (or any similarly high dimensional function) that enumerates the
saddle points, minima, and minimum energy paths between them. In doing so, it
creates a reduced dimensional network representation of this function. We also
present a series of tests to choose optimized parameters for the application of
the HSM. We apply this to the potential energy function of a granular system
consisting of a bidisperse configuration of frictionless soft spheres. Future
work will make use of the found ensemble of transition pathways to
statistically predict the dynamics of a system of grains.
