A connection between microscopic structure and macroscopic properties is expected for almost all material systems. High-resolution transmission electron microscopy is a technique offering insight into the atomic structure, but the analysis of large image series can be time consuming. The present work describes a method to automatically estimate the atomic structure in two-dimensional materials. As an example @entity15583 is chosen, in which the positions of the @entity505 atoms are reconstructed. Lattice parameters are extracted in the frequency domain and an initial atom positioning is estimated. Next, a plausible neighborhood structure is estimated. Finally, atom positions are adjusted by simulation of a Markov random field model, integrating image evidence and the strong geometric prior. A pristine sample with high regularity and a sample with an induced hole are analyzed. False discovery rate-controlled large-scale simultaneous hypothesis testing is used as a statistical framework for interpretation of results. The first sample yields, as expected, a homogeneous distribution of @entity505 - @entity505 (C-C) bond lengths. The second sample exhibits regions of shorter C-C bond lengths with a preferred orientation, suggesting either strain in the structure or a buckling of the @entity15583 sheet. The precision of the method is demonstrated on simulated model structures and by its application to multiple exposures of the two @entity15583 samples.

Structure identification in high-resolution transmission electron microscopic images: an example on XXXX .

@entity505 :: ('MESH:D002244', 'Chemical') :: ['carbon'] @entity15583 :: ('MESH:D006108', 'Chemical') :: ['graphene']
@entity15583 :: (MESH:D006108,Chemical) :: ['graphene']
