Progress in structure recovery from low dose exposures: Mixed molecular adsorption, exploitation of symmetry and reconstruction from the minimum signal level

Abstract

We investigate the recovery of structures from large-area, low dose exposures that distribute the dose over many identical copies of an object. The reconstruction is done via a maximum likelihood approach that does neither require to identify nor align the individual particles. We also simulate small molecular adsorbates on graphene and demonstrate the retrieval of images with atomic resolution from \add{large area and} extremely low dose raw data. Doses as low as 5 $e^{-}/$Å${}^2$ are sufficient if all symmetries (translations, rotations and mirrors) of the supporting membrane are exploited to retrieve the structure of individual adsorbed molecules. We compare different optimization schemes, consider mixed molecules and adsorption sites, and requirements on the amount of data. We further demonstrate that the maximum likelihood approach is only count limited by requiring at least three independent counts per entity.Finally, we demonstrate that the approach works with real experimental data and in presence of aberrations.

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Cited by in corpus (2)

• A deep learning approach to identify local structures in atomic-resolution transmission electron microscopy images
• Automated image acquisition for low-dose STEM at atomic resolution