Unsupervised identification of crystal defects from atomistic potential descriptors
Abstract Identifying crystal defects is vital for unraveling the origins of many physical phenomena. Traditionally used order parameters are system-dependent and can be computationally expensive to calculate for long molecular dynamics simulations. Unsupervised algorithms offer an alternative indepe...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01544-2 |
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| _version_ | 1850045898797613056 |
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| author | Lukáš Kývala Pablo Montero de Hijes Christoph Dellago |
| author_facet | Lukáš Kývala Pablo Montero de Hijes Christoph Dellago |
| author_sort | Lukáš Kývala |
| collection | DOAJ |
| description | Abstract Identifying crystal defects is vital for unraveling the origins of many physical phenomena. Traditionally used order parameters are system-dependent and can be computationally expensive to calculate for long molecular dynamics simulations. Unsupervised algorithms offer an alternative independent of the studied system and can utilize precalculated atomistic potential descriptors from molecular dynamics simulations. We compare the performance of three such algorithms (PCA, UMAP, and PaCMAP) on silicon and water systems. Initially, we evaluate the algorithms for recognizing phases, including crystal polymorphs and the melt, followed by an extension of our analysis to identify interstitials, vacancies, and interfaces. While PCA is found unsuitable for effective classification, it has been shown to be a suitable initialization for UMAP and PaCMAP. Both UMAP and PaCMAP show promising results overall, with PaCMAP proving more robust in classification, except in cases of significant class imbalance, where UMAP performs better. Notably, both algorithms successfully identify nuclei in supercooled water, demonstrating their applicability to ice nucleation in water. |
| format | Article |
| id | doaj-art-c0eb503e8dfe4430bf094f2227a09de7 |
| institution | DOAJ |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-c0eb503e8dfe4430bf094f2227a09de72025-08-20T02:54:36ZengNature Portfolionpj Computational Materials2057-39602025-02-011111810.1038/s41524-025-01544-2Unsupervised identification of crystal defects from atomistic potential descriptorsLukáš Kývala0Pablo Montero de Hijes1Christoph Dellago2Faculty of Physics, University of ViennaFaculty of Physics, University of ViennaFaculty of Physics, University of ViennaAbstract Identifying crystal defects is vital for unraveling the origins of many physical phenomena. Traditionally used order parameters are system-dependent and can be computationally expensive to calculate for long molecular dynamics simulations. Unsupervised algorithms offer an alternative independent of the studied system and can utilize precalculated atomistic potential descriptors from molecular dynamics simulations. We compare the performance of three such algorithms (PCA, UMAP, and PaCMAP) on silicon and water systems. Initially, we evaluate the algorithms for recognizing phases, including crystal polymorphs and the melt, followed by an extension of our analysis to identify interstitials, vacancies, and interfaces. While PCA is found unsuitable for effective classification, it has been shown to be a suitable initialization for UMAP and PaCMAP. Both UMAP and PaCMAP show promising results overall, with PaCMAP proving more robust in classification, except in cases of significant class imbalance, where UMAP performs better. Notably, both algorithms successfully identify nuclei in supercooled water, demonstrating their applicability to ice nucleation in water.https://doi.org/10.1038/s41524-025-01544-2 |
| spellingShingle | Lukáš Kývala Pablo Montero de Hijes Christoph Dellago Unsupervised identification of crystal defects from atomistic potential descriptors npj Computational Materials |
| title | Unsupervised identification of crystal defects from atomistic potential descriptors |
| title_full | Unsupervised identification of crystal defects from atomistic potential descriptors |
| title_fullStr | Unsupervised identification of crystal defects from atomistic potential descriptors |
| title_full_unstemmed | Unsupervised identification of crystal defects from atomistic potential descriptors |
| title_short | Unsupervised identification of crystal defects from atomistic potential descriptors |
| title_sort | unsupervised identification of crystal defects from atomistic potential descriptors |
| url | https://doi.org/10.1038/s41524-025-01544-2 |
| work_keys_str_mv | AT lukaskyvala unsupervisedidentificationofcrystaldefectsfromatomisticpotentialdescriptors AT pablomonterodehijes unsupervisedidentificationofcrystaldefectsfromatomisticpotentialdescriptors AT christophdellago unsupervisedidentificationofcrystaldefectsfromatomisticpotentialdescriptors |