  For molecular dynamics simulations of hard particles, we define dynamic
neighbors as the distinct particles that collide with a given reference one
during a specific time interval. This definition allows us to determine the
distribution of the number of dynamic neighbors, its average, and its standard
deviation. We will show that regardless of the time window used to identify
dynamic neighbors, their distribution is correlated with diffusion
coefficients, structure, and configurational entropy. Thus, it is likely that
the distribution of the number of dynamic neighbors may be employed as another
tool to gain insights into the dynamic behavior of hard systems. We tested this
approach on 2D and 3D systems consisting of monodisperse and binary mixtures of
hard disks and spheres. Results show that implementing dynamic neighbors to
define order parameters can sharpen the signals where transitions take place.
