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Atomistic simulations have become a powerful tool in materials research dueto the extremely fine spatial and temporal resolution provided by suchtechniques. In order to understand the fundamental principles which governmaterial behavior at the atomic scale and directly connect to experimentalworks, it is necessary to quantify the microstructure of materials simulatedwith atomistics. Specifically, quantitative tools for identifying crystallites,their crystallographic orientation, and overall sample texture do not currentlyexist. Here, we develop a post-processing algorithm capable of characterizingsuch features, while also documenting their evolution during a simulation. Inaddition, the data is presented in a way that parallels the visualizationmethods used in traditional experimental techniques. The utility of thisalgorithm is illustrated by analyzing several types of simulation cells whichare commonly found in the atomistic modeling literature, but could also beapplied to a variety of other atomistic studies which require preciseidentification and tracking of microstructure.

Tracking Microstructure of Crystalline Materials: A Post-Processing Algorithm for Atomistic Simulations