用深度学习破解双层莫尔材料原子识别难题，实现分层精准定位。
Atom identification in bilayer moire materials with Gomb-Net
- 基于Gomb-Net深度学习模型，分离双层材料中各层的原子坐标与种类。
- 成功在扭曲双层WS2-Se杂化结构中识别硒原子取代位点分布。
- 适用于莫尔调制复杂区域，为材料物理研究提供新视角。
范德瓦尔斯双层材料中的莫尔图案使原子分辨成像分析复杂化，阻碍了扫描透射电子显微镜通常能获得的原子级洞察。本文提出一种方法，可检测扭曲双层异质结构中每一层的原子位置与种类。我们开发了深度学习模型Gomb-Net，能够识别各层的坐标与原子物种，有效解耦莫尔图案。该方法实现了应变、掺杂分布等物理量的分层映射，而传统分割模型在莫尔效应干扰下难以实现。利用此方法，我们在扭曲的分数型朱纳斯WS2-WS2(1-x)Se2x异质结构中研究了硒原子的替代位点分布，发现分层植入位点不受局部能量或电子调制影响。该进展使此前无法实现的原子识别成为可能，为探索不可及材料物理提供了新路径。
原文摘要 · Abstract (English)
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.
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