  Atomistic simulations have become a powerful tool in materials research due
to the extremely fine spatial and temporal resolution provided by such
techniques. In order to understand the fundamental principles which govern
material behavior at the atomic scale and directly connect to experimental
works, it is necessary to quantify the microstructure of materials simulated
with atomistics. Specifically, quantitative tools for identifying crystallites,
their crystallographic orientation, and overall sample texture do not currently
exist. Here, we develop a post-processing algorithm capable of characterizing
such features, while also documenting their evolution during a simulation. In
addition, the data is presented in a way that parallels the visualization
methods used in traditional experimental techniques. The utility of this
algorithm is illustrated by analyzing several types of simulation cells which
are commonly found in the atomistic modeling literature, but could also be
applied to a variety of other atomistic studies which require precise
identification and tracking of microstructure.
