Experimental reconstructions of 3D atomic structures from electron microscopy images using a Bayesian genetic algorithm
We introduce a Bayesian genetic algorithm for reconstructing atomic models of monotype crystalline nanoparticles from a single projection using Z-contrast imaging. The number of atoms in a projected atomic column obtained from annular dark field scanning transmission electron microscopy images serve...
| Main Authors: | , , , , , |
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| Format: | Journal article |
| Language: | English |
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Springer Nature
2022
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| _version_ | 1797109121896415232 |
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| author | De Backer, A Van Aert, S Faes, C Irmak, EA Nellist, PD Jones, L |
| author_facet | De Backer, A Van Aert, S Faes, C Irmak, EA Nellist, PD Jones, L |
| author_sort | De Backer, A |
| collection | OXFORD |
| description | We introduce a Bayesian genetic algorithm for reconstructing atomic models of monotype crystalline nanoparticles from a single projection using Z-contrast imaging. The number of atoms in a projected atomic column obtained from annular dark field scanning transmission electron microscopy images serves as an input for the initial three-dimensional model. The algorithm minimizes the energy of the structure while utilizing a priori information about the finite precision of the atom-counting results and neighbor-mass relations. The results show promising prospects for obtaining reliable reconstructions of beam-sensitive nanoparticles during dynamical processes from images acquired with sufficiently low incident electron doses. |
| first_indexed | 2024-03-07T07:36:00Z |
| format | Journal article |
| id | oxford-uuid:20712154-27de-4a49-b65b-08ea0d495472 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:36:00Z |
| publishDate | 2022 |
| publisher | Springer Nature |
| record_format | dspace |
| spelling | oxford-uuid:20712154-27de-4a49-b65b-08ea0d4954722023-03-17T14:41:56ZExperimental reconstructions of 3D atomic structures from electron microscopy images using a Bayesian genetic algorithmJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:20712154-27de-4a49-b65b-08ea0d495472EnglishSymplectic ElementsSpringer Nature2022De Backer, AVan Aert, SFaes, CIrmak, EANellist, PDJones, LWe introduce a Bayesian genetic algorithm for reconstructing atomic models of monotype crystalline nanoparticles from a single projection using Z-contrast imaging. The number of atoms in a projected atomic column obtained from annular dark field scanning transmission electron microscopy images serves as an input for the initial three-dimensional model. The algorithm minimizes the energy of the structure while utilizing a priori information about the finite precision of the atom-counting results and neighbor-mass relations. The results show promising prospects for obtaining reliable reconstructions of beam-sensitive nanoparticles during dynamical processes from images acquired with sufficiently low incident electron doses. |
| spellingShingle | De Backer, A Van Aert, S Faes, C Irmak, EA Nellist, PD Jones, L Experimental reconstructions of 3D atomic structures from electron microscopy images using a Bayesian genetic algorithm |
| title | Experimental reconstructions of 3D atomic structures from electron microscopy images using a Bayesian genetic algorithm |
| title_full | Experimental reconstructions of 3D atomic structures from electron microscopy images using a Bayesian genetic algorithm |
| title_fullStr | Experimental reconstructions of 3D atomic structures from electron microscopy images using a Bayesian genetic algorithm |
| title_full_unstemmed | Experimental reconstructions of 3D atomic structures from electron microscopy images using a Bayesian genetic algorithm |
| title_short | Experimental reconstructions of 3D atomic structures from electron microscopy images using a Bayesian genetic algorithm |
| title_sort | experimental reconstructions of 3d atomic structures from electron microscopy images using a bayesian genetic algorithm |
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