Graph theory based approach to characterize self interstitial defect morphology

The defect morphology is an essential aspect of the evolution of crystal microstructure and its response to stress. While reliable and efficient standard computational algorithms exist for finding defect concentration and size distribution in a crystal, defect morphology identification is still nascent. The need for an efficient and comprehensive algorithm to study defects is becoming more evident with the increase in the amount of simulation data and improvements in data-driven algorithms. We present a method to characterize a defect's morphology precisely by reducing the problem into graph theoretical concepts of finding connected components and cycles. The algorithm can identify the different homogenous components within a defect cluster having mixed morphology. We apply the method to classify morphologies of over a thousand point defect clusters formed in high energy W collision cascades. We highlight our method's comparative advantage for its completeness, computational speed, and quantitative details.

Automated summary

The characterization of defect morphology is crucial in understanding the evolution of crystal microstructure in response to stress. While existing computational algorithms can efficiently find defect concentration and size distribution, the identification of defect morphology remains limited. Our proposed algorithm, based on graph theoretical concepts, offers a comprehensive and efficient solution for precisely characterizing defect morphology, particularly in identifying different homogeneous components within defect clusters with mixed morphology. Applying the method to classify morphologies of point defect clusters produced in high energy W collision cascades showcases its advantages in completeness, computational speed, and detailed quantitative analysis.