Dislocation cartography: Representations and unsupervised classification of dislocation networks with unique fingerprints
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909228864634880 |
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| author | Udofia, Benjamin Jogi, Tushar Stricker, Markus |
| author_facet | Udofia, Benjamin Jogi, Tushar Stricker, Markus |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_15004 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dislocation cartography: Representations and unsupervised classification of dislocation networks with unique fingerprints Udofia, Benjamin Jogi, Tushar Stricker, Markus Materials Science Machine Learning 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. |
| title | Dislocation cartography: Representations and unsupervised classification of dislocation networks with unique fingerprints |
| topic | Materials Science Machine Learning |
| url | https://arxiv.org/abs/2406.15004 |