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

TL;DR

This paper introduces a novel post-processing algorithm for atomistic simulations that accurately identifies and tracks microstructural features like crystallites and orientations, bridging simulation data with experimental visualization methods.

Contribution

The paper presents a new algorithm for quantifying and visualizing microstructure evolution in atomistic simulations, filling a gap in existing tools.

Findings

Successfully characterizes crystallites and orientations in simulation data.

Tracks microstructure evolution over simulation time.

Applicable to various atomistic modeling scenarios.

Abstract

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.