Dislocation cartography: Representations and unsupervised classification of dislocation networks with unique fingerprints

TL;DR

This paper introduces a novel, unbiased method using Isomap to classify and compare dislocation network structures in crystalline materials based on their density fields, aiding in understanding plastic deformation.

Contribution

It applies manifold learning to generate unique fingerprints for dislocation structures, enabling systematic quantitative classification and comparison.

Findings

Isomap effectively reveals the intrinsic structure of dislocation density data.

The method provides unique, quantitative fingerprints for different dislocation networks.

The approach facilitates systematic comparison of dislocation structures across samples.

Abstract

Detecting structure in data is the first step to arrive at meaningful representations for systems. This is particularly challenging for dislocation networks evolving as a consequence of plastic deformation of crystalline systems. Our study employs Isomap, a manifold learning technique, to unveil the intrinsic structure of high-dimensional density field data of dislocation structures from different compression axis. The resulting maps provide a systematic framework for quantitatively comparing dislocation structures, offering unique fingerprints based on density fields. Our novel, unbiased approach contributes to the quantitative classification of dislocation structures which can be systematically extended.