  We introduce a Bayesian genetic algorithm for reconstructing atomic models of
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 (ADF STEM) images serves as an input for the
initial three-dimensional (3D) model. The novel 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 excellent prospects for obtaining reliable reconstructions of
beam-sensitive nanoparticles during dynamical processes from images acquired
with sufficiently low incident electron doses.
