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 obtai...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.展开更多
基金This work was supported by the European Research Council(Grant 770887 PICOMETRICS to S.V.A.and Grant 823717 ESTEEM3)The authors acknowledge financial support from the Research Foundation Flanders(FWO,Belgium)through project fundings(G.0267.18N,G.0502.18N,G.0346.21N)+1 种基金a postdoctoral grant to A.D.B.L.J.acknowledges Science Foundation Ireland(SFI–grant number URF/RI/191637)the Royal Society,and the AMBER Centre.The authors acknowledge Aakash Varambhia for his assistance and expertise with the experimental recording and use of characterization facilities within the David Cockayne Centre for Electron Microscopy,Department of Materials,University of Oxford,and in particular the EPSRC(EP/K040375/1 South of England Analytical Electron Microscope).
文摘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.
