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Author Lobato, I.; De Backer, A.; Van Aert, S. pdf  url
doi  openurl
  Title Real-time simulations of ADF STEM probe position-integrated scattering cross-sections for single element fcc crystals in zone axis orientation using a densely connected neural network Type A1 Journal article
  Year (down) 2023 Publication Ultramicroscopy Abbreviated Journal Ultramicroscopy  
  Volume 251 Issue Pages 113769  
  Keywords A1 Journal article; Electron microscopy for materials research (EMAT)  
  Abstract Quantification of annular dark field (ADF) scanning transmission electron microscopy (STEM) images in terms

of composition or thickness often relies on probe-position integrated scattering cross sections (PPISCS). In

order to compare experimental PPISCS with theoretically predicted ones, expensive simulations are needed for

a given specimen, zone axis orientation, and a variety of microscope settings. The computation time of such

simulations can be in the order of hours using a single GPU card. ADF STEM simulations can be efficiently

parallelized using multiple GPUs, as the calculation of each pixel is independent of other pixels. However, most

research groups do not have the necessary hardware, and, in the best-case scenario, the simulation time will

only be reduced proportionally to the number of GPUs used. In this manuscript, we use a learning approach and

present a densely connected neural network that is able to perform real-time ADF STEM PPISCS predictions as

a function of atomic column thickness for most common face-centered cubic (fcc) crystals (i.e., Al, Cu, Pd, Ag,

Pt, Au and Pb) along [100] and [111] zone axis orientations, root-mean-square displacements, and microscope

parameters. The proposed architecture is parameter efficient and yields accurate predictions for the PPISCS

values for a wide range of input parameters that are commonly used for aberration-corrected transmission

electron microscopes.
 
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  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Wos 001011617200001 Publication Date 2023-06-01  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0304-3991 ISBN Additional Links UA library record; WoS full record  
  Impact Factor 2.2 Times cited Open Access OpenAccess  
  Notes This work was supported by the European Research Council (Grant 770887 PICOMETRICS to S. Van Aert). The authors acknowledge financial support from the Research Foundation Flanders (FWO, Belgium) through project fundings (G034621N and G0A7723N) and a postdoctoral grant to A. De Backer. S. Van Aert acknowledges funding from the University of Antwerp Research fund (BOF), Belgium. Approved Most recent IF: 2.2; 2023 IF: 2.843  
  Call Number EMAT @ emat @c:irua:197275 Serial 8812  
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