Zhang Zhe, Yang Xi, Huang Xiaobiao, Shaftan Timur, Smaluk Victor, Song Minghao, Wan Weishi, Wu Lijun, Zhu Yimei
SLAC National Accelerator Laboratory, Menlo Park, CA, 94025, USA.
National Synchrotron Light Source II, Brookhaven National Laboratory, Upton, NY, 11973, USA.
Sci Rep. 2022 Mar 10;12(1):4240. doi: 10.1038/s41598-022-08260-7.
To demonstrate the feasibility of automating UED operation and diagnosing the machine performance in real time, a two-stage machine learning (ML) model based on self-consistent start-to-end simulations has been implemented. This model will not only provide the machine parameters with adequate precision, toward the full automation of the UED instrument, but also make real-time electron beam information available as single-shot nondestructive diagnostics. Furthermore, based on a deep understanding of the root connection between the electron beam properties and the features of Bragg-diffraction patterns, we have applied the hidden symmetry as model constraints, successfully improving the accuracy of energy spread prediction by a factor of five and making the beam divergence prediction two times faster. The capability enabled by the global optimization via ML provides us with better opportunities for discoveries using near-parallel, bright, and ultrafast electron beams for single-shot imaging. It also enables directly visualizing the dynamics of defects and nanostructured materials, which is impossible using present electron-beam technologies.
为了证明实现UED操作自动化并实时诊断机器性能的可行性，已经实施了一种基于自洽端到端模拟的两阶段机器学习（ML）模型。该模型不仅将以足够的精度提供机器参数，以实现UED仪器的完全自动化，还将使电子束信息作为单次无损诊断可用。此外，基于对电子束特性与布拉格衍射图案特征之间根本联系的深入理解，我们应用了隐藏对称性作为模型约束，成功地将能量扩散预测的准确性提高了五倍，并使束发散预测速度提高了两倍。通过ML进行全局优化所实现的能力为我们提供了更好的机会，利用近平行、明亮和超快电子束进行单次成像来发现新现象。它还能够直接可视化缺陷和纳米结构材料的动力学，而这是目前的电子束技术无法做到的。
