标题： 基于Gomb-Net的双层莫尔材料中原子识别 显示英文标题

标题： Atom identification in bilayer moire materials with Gomb-Net

摘要： 在范德华双层材料中，莫尔条纹会复杂化原子分辨率图像的分析，阻碍了通常可通过扫描透射电子显微镜获得的原子尺度洞察。 在这里，我们报告了一种方法，用于检测构成扭曲双层异质结构的每个单独层中的原子位置和种类。 我们开发了一个深度学习模型，Gomb-Net，它可以识别每层中的坐标和原子种类，有效地解卷积莫尔条纹。 这使得可以对诸如应变和掺杂剂分布等量进行层特异性映射，而其他常用分割模型则难以处理由莫尔条纹引起的复杂性。 使用这种方法，我们研究了扭曲的分数型Janus WS2-WS2(1-x)Se2x异质结构中的硒原子替代位点分布，并发现层特异性植入位点不受莫尔条纹局部能量或电子调制的影响。 这一进展使在之前无法实现的材料区域内的原子识别成为可能，为以前无法触及的材料物理提供了新的见解。 显示英文摘要

摘要： 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.