Unsupervised learning for structure detection in plastically deformed crystals
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913349456887808 |
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| author | Barbot, Armand Gatti, Riccardo |
| author_facet | Barbot, Armand Gatti, Riccardo |
| contents | Detecting structures at the particle scale within plastically deformed crystalline materials allows a better understanding of the occurring phenomena. While previous approaches mostly relied on applying hand-chosen criteria on different local parameters, these approaches could only detect already known structures.We introduce an unsupervised learning algorithm to automatically detect structures within a crystal under plastic deformation. This approach is based on a study developed for structural detection on colloidal materials. This algorithm has the advantage of being computationally fast and easy to implement. We show that by using local parameters based on bond-angle distributions, we are able to detect more structures and with a higher degree of precision than traditional hand-made criteria. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2212_14813 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Unsupervised learning for structure detection in plastically deformed crystals Barbot, Armand Gatti, Riccardo Materials Science Machine Learning Detecting structures at the particle scale within plastically deformed crystalline materials allows a better understanding of the occurring phenomena. While previous approaches mostly relied on applying hand-chosen criteria on different local parameters, these approaches could only detect already known structures.We introduce an unsupervised learning algorithm to automatically detect structures within a crystal under plastic deformation. This approach is based on a study developed for structural detection on colloidal materials. This algorithm has the advantage of being computationally fast and easy to implement. We show that by using local parameters based on bond-angle distributions, we are able to detect more structures and with a higher degree of precision than traditional hand-made criteria. |
| title | Unsupervised learning for structure detection in plastically deformed crystals |
| topic | Materials Science Machine Learning |
| url | https://arxiv.org/abs/2212.14813 |