Dislocation cartography: Representations and unsupervised classification of dislocation networks with unique fingerprints
This work addresses the problem of quantitatively classifying dislocation structures in materials science, offering an unbiased approach that can be systematically extended, though it appears incremental in applying existing techniques to a specific domain.
The study tackled the challenge of detecting structure in high-dimensional density field data of dislocation networks from plastic deformation in crystalline systems by using Isomap manifold learning, resulting in maps that provide unique fingerprints for quantitative comparison and classification of dislocation structures.
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.