Grain-resolved kinetics and rotation during grain growth of nanocrystalline Aluminium by molecular dynamics

TL;DR

This study uses molecular dynamics simulations with a novel algorithm to analyze grain growth, kinetics, and rotation in nanocrystalline aluminum, providing detailed insights into grain behavior during growth.

Contribution

A new efficient algorithm was developed to identify and track grains and their orientations in molecular dynamics data of FCC materials, enabling detailed kinetic and rotational analysis.

Findings

Grain growth kinetics are slightly slowed in initial stages.

Grain rotation is marginal during growth.

The algorithm accurately reconstructs grain orientations and attributes.

Abstract

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.