Fig. 4: Robustness and generalizability of the inference model for slip stacking analysis.
From: Auto-resolving the atomic structure at van der Waals interfaces using a generative model
a Schematics of the electron beam (EB) irradiation on 2D vdW materials. b Inference accuracy as a function of the defect ratio. The inset shows the DRIT-generated image with 10% defect ratio. c Inference accuracy as a function of the peak signal-to-noise ratio (PSNR) of the STEM image. Insets display DRIT-generated images with different PSNR levels. d Experimental ADF-STEM image of a slip-stacked bilayer ReS2 having a hole in one layer (white dashed box). e Da mapping of (d) inferred by the ML model. Inset is the atomic model of the bilayer region reconstructed by the inferred slip coordinate. f Difference of the inferred slip vectors between experimental images with low and high signal-to-noise ratios (SNR). 10 pairs of experimental images are chosen (insets). g Example verifying the model’s reliability when analyzing low SNR experimental images. A pair of experimental STEM images in (f) is displayed in the left panels. The atomic model of the low SNR image is inferred with the corresponding noise-free simulated image shown in the top right corner. Three intensity line profiles taken along the same locations (white arrows) in the high SNR experimental image, the low SNR experimental image, and the noise-free simulated image are shown in the lower right corner. h Generalizability of the ML model to the structural inference of slip-stacked ReS2 trilayers, where the superiority of the end-to-end ML algorithm over the trial-and-error model of human inference is displayed. An experimental ADF-STEM image of a trilayer ReS2 is shown on the left. Scale bars in (b), (c), (g), and (h): 0.5 nm. Scale bars in (d) and (e): 1 nm.
