Using electron beam manipulation, we enable deterministic motion of individual Si atoms in graphene along predefined trajectories. Structural evolution during the dopant motion was explored, providing information on changes of the Si atom neighborhood during atomic motion and providing statistical information of possible defect configurations. The combination of a Gaussian mixture model and principal component analysis applied to the deep learning-processed experimental data allowed disentangling of the atomic distortions for two different graphene sublattices. This approach demonstrates the potential of e-beam manipulation to create defect libraries of multiple realizations of the same defect and explore the potential of symmetry breaking physics. The rapid image analytics enabled via a deep learning network further empowers instrumentation for e-beam controlled atom-by-atom fabrication. The analysis described in the paper can be reproduced via an interactive Jupyter notebook at https://git.io/JJ3Bx.

中文翻译：

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通过深度机器学习跟踪定向电子束诱导石墨烯中硅原子运动过程中的原子结构演变

使用电子束操纵，我们可以使石墨烯中的单个 Si 原子沿着预定义的轨迹进行确定性运动。探索了掺杂剂运动过程中的结构演变，提供了原子运动过程中硅原子邻域变化的信息，并提供了可能的缺陷配置的统计信息。应用于深度学习处理的实验数据的高斯混合模型和主成分分析的组合允许解开两种不同石墨烯亚晶格的原子畸变。这种方法展示了电子束操作的潜力，可以创建同一缺陷的多个实现的缺陷库，并探索对称破缺物理学的潜力。通过深度学习网络实现的快速图像分析进一步增强了电子束控制的逐个原子制造的仪器。论文中描述的分析可以通过 https://git.io/JJ3Bx 上的交互式 Jupyter 笔记本进行复制。