Experimental reconstructions of 3D atomic structures from electron microscopy images using a Bayesian genetic algorithm
Citation:
Annick De Backer, Sandra Van Aert, Christel Faes, Ece Arslan Irmak, Peter D. Nellist, Lewys Jones, Experimental reconstructions of 3D atomic structures from electron microscopy images using a Bayesian genetic algorithm, npj Computational Materials, 2022Download Item:
Abstract:
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.
Sponsor
Grant Number
Science Foundation Ireland (SFI)
URF/RI/191637
Author's Homepage:
http://people.tcd.ie/jonesl1Description:
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Author: Jones, Lewys
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Science Foundation Ireland (SFI)Type of material:
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npj Computational Materials;Availability:
Full text availableSubject (TCD):
Nanoscience & MaterialsDOI:
http://dx.doi.org/10.1038/s41524-022-00900-wMetadata
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