Atom identification in bilayer moire materials with Gomb-Net
This method addresses the challenge of atomic-scale analysis in complex bilayer materials for materials science, enabling new insights into previously inaccessible physics.
The researchers tackled the problem of identifying atomic positions and species in twisted bilayer materials, which are obscured by moire patterns, by developing Gomb-Net, a deep learning model that successfully deconvolutes these patterns to enable layer-specific mapping.
Moire patterns in van der Waals bilayer materials complicate the analysis of atomic-resolution images, hindering the atomic-scale insight typically attainable with scanning transmission electron microscopy. Here, we report a method to detect the positions and identities of atoms in each of the individual layers that compose twisted bilayer heterostructures. We developed a deep learning model, Gomb-Net, which identifies the coordinates and atomic species in each layer, effectively deconvoluting the moire pattern. This enables layer-specific mapping of quantities like strain and dopant distributions, unlike other commonly used segmentation models which struggle with moire-induced complexity. Using this approach, we explored the Se atom substitutional site distribution in a twisted fractional Janus WS2-WS2(1-x)Se2x heterostructure and found that layer-specific implantation sites are unaffected by the moire pattern's local energetic or electronic modulation. This advancement enables atom identification within material regimes where it was not possible before, opening new insights into previously inaccessible material physics.