Grain segmentation in atomistic simulations using orientation-based iterative self-organizing data analysis

TL;DR

This paper introduces the Orisodata algorithm, an unsupervised machine learning method for grain segmentation in atomistic simulations based on atomic orientations, demonstrating improved accuracy in identifying grains and deformation features.

Contribution

The paper presents a novel orientation-based iterative self-organizing data analysis algorithm tailored for atomistic grain segmentation, enhancing interpretation and validation of simulation results.

Findings

Successfully segments grains in nanocrystalline samples

Identifies deformation twins and low angle boundaries

Parameters relate to experimental thresholds

Abstract

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.