Digital representation and quantification of discrete dislocation networks

TL;DR

This paper introduces a computational framework combining 2-point spatial correlations and PCA to efficiently quantify and compare dislocation networks, aiding the rational design of metal materials.

Contribution

It presents a novel statistical and low-dimensional representation method for dislocation networks, addressing previous computational and statistical limitations.

Findings

Framework effectively quantifies dislocation networks

Enables comparison of different dislocation configurations

Facilitates material design optimization

Abstract

Dislocation networks and their evolution are known to control the mechanical properties of metal samples. However, the lack of computationally efficient and statistically rigorous descriptors for such defect systems has hindered the development and adoption of rational protocols for the optimal design of these material systems. This study presents a framework for the rigorous statistical quantification and low dimensional representation of dislocation networks using the formalism of 2-point spatial correlations (also called 2-point statistics) along with Principle Component Analysis (PCA). The usefulness of this basic framework for comparing and observing dislocation networks is exemplified and discussed with suitable examples.