Toward fully automated UED operation using two-stage machine learning model

• Published in: Scientific reports Vol. 12; no. 1; pp. 4240 - 12
• Main Authors: Zhang, Zhe, Yang, Xi, Huang, Xiaobiao, Shaftan, Timur, Smaluk, Victor, Song, Minghao, Wan, Weishi, Wu, Lijun, Zhu, Yimei
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 10.03.2022; Nature Publishing Group UK; Nature Portfolio
• Subjects: Automation; Learning algorithms; Machine learning; MATERIALS SCIENCE; MATHEMATICS AND COMPUTING; Nanostructured materials
• ISSN: 2045-2322; 2045-2322
• 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.