Fig. 2 Evaluation of accuracy in finding the atomic positions via a deep learning model for different levels of noise. Evaluation of accuracy in finding.

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Fig. 2 Evaluation of accuracy in finding the atomic positions via a deep learning model for different levels of noise. Evaluation of accuracy in finding the atomic positions via a deep learning model for different levels of noise. (A) The deviation of the predicted positions (average value for all atoms in the image) from the ground truth positions of atoms for different combinations of noise (λ′) and blurring (σ′). The λ′ and σ′ are the scaled parameters of Poisson and Gaussian distributions, respectively. See the Supplementary Materials for the details and code of how the noisy data were created and analyzed. (B) Simulated image of graphene lattice associated with the red cross in (A). (C) Same image overlaid with atomic coordinates. Different colors of circular markers show different degrees of atomic positions displacement from ground truth coordinates. (D) The histogram showing atomic displacements for every atomic position displayed in (C). Most of the deviations are below 10 pm, which is within instrumental uncertainty for atomic position extraction. Maxim Ziatdinov et al. Sci Adv 2019;5:eaaw8989 Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).