Dislocation cartography: Representations and unsupervised classification of dislocation networks with unique fingerprints
Detecting structure in data is the first step to arrive at meaningful representations for systems. This is particularly challenging for evolving dislocation networks evolving as a consequence of plastic deformation of crystalline materials. Our study employs Isomap, a manifold learning technique, to...
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2025-03-01
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| Series: | APL Machine Learning |
| Online Access: | http://dx.doi.org/10.1063/5.0224710 |
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| _version_ | 1849744878453391360 |
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| author | Benjamin Udofia Tushar Jogi Markus Stricker |
| author_facet | Benjamin Udofia Tushar Jogi Markus Stricker |
| author_sort | Benjamin Udofia |
| collection | DOAJ |
| description | Detecting structure in data is the first step to arrive at meaningful representations for systems. This is particularly challenging for evolving dislocation networks evolving as a consequence of plastic deformation of crystalline materials. Our study employs Isomap, a manifold learning technique, to show the intrinsic structure of high-dimensional dislocation density field data of dislocation structures resulting from different compression axes. Our maps provide a systematic framework for quantitatively comparing dislocation structures and offer unique fingerprints based on dislocation density fields. It represents a novel, unbiased approach that contributes to the quantitative classification of dislocation structures, which can be systematically extended using different representations of dislocation systems. |
| format | Article |
| id | doaj-art-ec7f5e56d44f422fa1c6bd51a14a7637 |
| institution | DOAJ |
| issn | 2770-9019 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | APL Machine Learning |
| spelling | doaj-art-ec7f5e56d44f422fa1c6bd51a14a76372025-08-20T03:06:24ZengAIP Publishing LLCAPL Machine Learning2770-90192025-03-0131016103016103-1110.1063/5.0224710Dislocation cartography: Representations and unsupervised classification of dislocation networks with unique fingerprintsBenjamin Udofia0Tushar Jogi1Markus Stricker2Interdisciplinary Centre for Advanced Materials Simulation (ICAMS), Materials Informatics and Data Science, Ruhr-Universität Bochum, Universitätsstr. 150, 44801 Bochum, GermanyInterdisciplinary Centre for Advanced Materials Simulation (ICAMS), Materials Informatics and Data Science, Ruhr-Universität Bochum, Universitätsstr. 150, 44801 Bochum, GermanyInterdisciplinary Centre for Advanced Materials Simulation (ICAMS), Materials Informatics and Data Science, Ruhr-Universität Bochum, Universitätsstr. 150, 44801 Bochum, GermanyDetecting structure in data is the first step to arrive at meaningful representations for systems. This is particularly challenging for evolving dislocation networks evolving as a consequence of plastic deformation of crystalline materials. Our study employs Isomap, a manifold learning technique, to show the intrinsic structure of high-dimensional dislocation density field data of dislocation structures resulting from different compression axes. Our maps provide a systematic framework for quantitatively comparing dislocation structures and offer unique fingerprints based on dislocation density fields. It represents a novel, unbiased approach that contributes to the quantitative classification of dislocation structures, which can be systematically extended using different representations of dislocation systems.http://dx.doi.org/10.1063/5.0224710 |
| spellingShingle | Benjamin Udofia Tushar Jogi Markus Stricker Dislocation cartography: Representations and unsupervised classification of dislocation networks with unique fingerprints APL Machine Learning |
| title | Dislocation cartography: Representations and unsupervised classification of dislocation networks with unique fingerprints |
| title_full | Dislocation cartography: Representations and unsupervised classification of dislocation networks with unique fingerprints |
| title_fullStr | Dislocation cartography: Representations and unsupervised classification of dislocation networks with unique fingerprints |
| title_full_unstemmed | Dislocation cartography: Representations and unsupervised classification of dislocation networks with unique fingerprints |
| title_short | Dislocation cartography: Representations and unsupervised classification of dislocation networks with unique fingerprints |
| title_sort | dislocation cartography representations and unsupervised classification of dislocation networks with unique fingerprints |
| url | http://dx.doi.org/10.1063/5.0224710 |
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