  Grain growth in nanocrystalline Al was studied by means of molecular dynamics
simulations. The novelty of this study results from the utilization of an
algorithm to resolve per-grain kinetics and orientation change from molecular
dynamics data sets. To this aim, a highly efficient algorithm for the
identification and reconstruction of crystallites from molecular dynamics data
sets of FCC materials was developed. This method is capable of calculating
specific attributes of grains, namely, volume, center of mass, average
orientation and orientation spread. In addition, it provides a mapping method
to track grains during time-row data sets. In the present contribution, we
describe and validate the algorithm, which is then used to analyze grain growth
in polycrystalline Al with a weak texture. For the conditions tested, the
algorithm was able to find all of the input orientations and reconstruct the
grains according to their crystallographic orientation. With the help of the
developed algorithms, we studied grain growth kinetics and grain rotation. The
results of the simulations showed slightly slowed-down kinetics in particular
in the initial stages of grain growth and marginal rotation of the grains.
