Grain segmentation in atomistic simulations using orientation-based iterative self-organizing data analysis
This work addresses the need for accurate grain segmentation in polycrystalline materials at the nanoscale for researchers in materials science, though it is incremental as it modifies an existing technique for a specific domain.
The authors tackled the problem of grain segmentation in atomistic simulations by proposing the Orisodata algorithm, an unsupervised machine learning method that clusters atoms into grains based on orientation, and demonstrated its effectiveness on a 122-grain nanocrystalline thin film sample in undeformed and deformed states, correctly identifying deformation twins and low-angle grain boundaries.
Atomistic simulations have now established themselves as an indispensable tool in understanding deformation mechanisms of materials at the atomic scale. Large scale simulations are regularly used to study the behavior of polycrystalline materials at the nanoscale. In this work, we propose a method for grain segmentation of an atomistic configuration using an unsupervised machine learning algorithm that clusters atoms into individual grains based on their orientation. The proposed method, called the Orisodata algorithm, is based on the iterative self-organizing data analysis technique and is modified to work in the orientation space. The working of the algorithm is demonstrated on a 122 grain nanocrystalline thin film sample in both undeformed and deformed states. The Orisodata algorithm is also compared with two other grain segmentation algorithms available in the open-source visualization tool Ovito. The results show that the Orisodata algorithm is able to correctly identify deformation twins as well as regions separated by low angle grain boundaries. The model parameters have intuitive physical meaning and relate to similar thresholds used in experiments, which not only helps obtain optimal values but also facilitates easy interpretation and validation of results.