High‐throughput continuous rotation electron diffraction data acquisition via software automation

• Published in: Journal of applied crystallography 2018-12, Vol.51 (6), p.1652-1661
• Main Authors: Cichocka, Magdalena Ola, Ångström, Jonas, Wang, Bin, Zou, Xiaodong, Smeets, Stef
• Format: Article
• Language: English
• Subjects: Apertures; Artificial neural networks; Automation; Computer programs; crystal screening; Crystal structure; Crystals; Data acquisition; Data collection; Data processing; Defocusing; Diffraction patterns; Electron diffraction; Electrons; high throughput; Human error; Inorganic Chemistry; Neural networks; oorganisk kemi; Research Papers; Rotation; Screening; single-crystal electron diffraction; Software; structure analysis; Zeolites
• ISSN: 1600-5767; 0021-8898; 1600-5767
• DOI: 10.1107/S1600576718015145

Single‐crystal electron diffraction (SCED) is emerging as an effective technique to determine and refine the structures of unknown nano‐sized crystals. In this work, the implementation of the continuous rotation electron diffraction (cRED) method for high‐throughput data collection is described. This is achieved through dedicated software that controls the transmission electron microscope and the camera. Crystal tracking can be performed by defocusing every nth diffraction pattern while the crystal rotates, which addresses the problem of the crystal moving out of view of the selected area aperture during rotation. This has greatly increased the number of successful experiments with larger rotation ranges and turned cRED data collection into a high‐throughput method. The experimental parameters are logged, and input files for data processing software are written automatically. This reduces the risk of human error, and makes data collection more reproducible and accessible for novice and irregular users. In addition, it is demonstrated how data from the recently developed serial electron diffraction technique can be used to supplement the cRED data collection by automatic screening for suitable crystals using a deep convolutional neural network that can identify promising crystals through the corresponding diffraction data. The screening routine and cRED data collection are demonstrated using a sample of the zeolite mordenite, and the quality of the cRED data is assessed on the basis of the refined crystal structure. A semi‐automated routine for continuous rotation electron diffraction has been developed, enabling high‐throughput data collection. Serial electron crystallography combined with a deep convolutional network are used to screen for suitable crystals.