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

Materialia, vol. 21, pp. 101314

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.

Authors 3

1. M. Vimal

   TU Bergakademie Freiberg

   Affiliation as printed

   Micromechanical Materials Modelling (MiMM), Institute of Mechanics and Fluid Dynamics, TU Bergakademie Freiberg, Lampadiusstraße 4, Freiberg 09599, Germany

2. Stefan Sandfeld Aachen Chair of Materials Data Science and Materials Informatics Faculty 5 – Georesources and Materials Engineering

   RWTH Aachen University · Forschungszentrum Jülich · TU Bergakademie Freiberg

   Affiliation as printed

   Chair of Materials Data Science and Materials Informatics, Faculty 5 – Georesources and Materials Engineering, RWTH Aachen University, Aachen 52056, Germany

   Institute for Advanced Simulation – IAS-9: Materials Data Science and Informatics, Forschungszentrum Juelich GmbH, Juelich 52425, Germany

   Micromechanical Materials Modelling (MiMM), Institute of Mechanics and Fluid Dynamics, TU Bergakademie Freiberg, Lampadiusstraße 4, Freiberg 09599, Germany

3. Aruna Prakash corresponding

   TU Bergakademie Freiberg

   Affiliation as printed

   Micromechanical Materials Modelling (MiMM), Institute of Mechanics and Fluid Dynamics, TU Bergakademie Freiberg, Lampadiusstraße 4, Freiberg 09599, Germany