Graph Theory Based Approach to Characterize Self Interstitial Defect Morphology

TL;DR

This paper introduces a graph theory-based algorithm for detailed and efficient characterization of defect morphologies in crystal microstructures, improving upon existing methods by providing comprehensive, fast, and quantitative analysis.

Contribution

The paper presents a novel graph theoretical approach to classify defect morphologies, capable of analyzing complex defect clusters with high accuracy and speed.

Findings

Successfully classified over a thousand defect clusters

Demonstrated superior speed and completeness over existing methods

Provided detailed quantitative defect morphology data

Abstract

The defect morphology is an essential aspect of the evolution of crystals' microstructure and its response to stress. Existing methods either only report defect concentration or characterize only some of the defect morphologies. 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.