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

arXiv: https://arxiv.org/abs/2406.15004 · HTML: https://mlsanity.com/paper/2406.15004/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 72 references that are in the map.
- First posted on arXiv 21 Jun 2024.

## Builds on (most cited first, 4 of 4)

- [DeepVoxels: Learning Persistent 3D Feature Embeddings](https://mlsanity.com/paper/1812.01024/) (2018)
- [Graph Embedding Techniques, Applications, and Performance: A Survey](https://mlsanity.com/paper/1705.02801/) (2017)
- [Improving Sample and Feature Selection with Principal Covariates Regression](https://mlsanity.com/paper/2012.12253/) (2020)
- [DISO: A Domain Ontology for Modeling Dislocations in Crystalline Materials](https://mlsanity.com/paper/2401.02540/) (2024)

## Built on by (most cited first, 1 of 1)

- [Inferring Dislocation Microstructures from X-ray Diffraction via Cross-Modal Contrastive Learning](https://mlsanity.com/paper/2609.12713/) (2026)

## Abstract (arXiv)

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.