  In polycrystalline materials, grain boundaries are sites of enhanced atomic
motion, but the complexity of the atomic structures within a grain boundary
network makes it difficult to link the structure and atomic dynamics. Here we
use a machine learning technique to establish a connection between local
structure and dynamics of these materials. Following previous work on bulk
glassy materials, we define a purely structural quantity, softness, that
captures the propensity of an atom to rearrange. This approach correctly
identifies crystalline regions, stacking faults, and twin boundaries as having
low likelihood of atomic rearrangements, while finding a large variability
within high-energy grain boundaries. As has been found in glasses [9,19,26],
the probability that atoms of a given softness will rearrange is nearly
Arrhenius. This indicates a well-defined energy barrier as well as a
well-defined prefactor for the Arrhenius form for atoms of a given softness.
The decrease in the prefactor for low-softness atoms indicates that variations
in entropy exhibit a dominant influence on the atomic dynamics in grain
boundaries.
