Cao Michael C, Chen Zhen, Jiang Yi, Han Yimo
Department of Materials Science and NanoEngineering, Rice University, Houston, TX, 77005, USA.
School of Materials Science and Engineering, Tsinghua University, Beijing, 100084, China.
Sci Rep. 2022 Jul 19;12(1):12284. doi: 10.1038/s41598-022-16041-5.
Electron ptychography provides new opportunities to resolve atomic structures with deep sub-angstrom spatial resolution and to study electron-beam sensitive materials with high dose efficiency. In practice, obtaining accurate ptychography images requires simultaneously optimizing multiple parameters that are often selected based on trial-and-error, resulting in low-throughput experiments and preventing wider adoption. Here, we develop an automatic parameter selection framework to circumvent this problem using Bayesian optimization with Gaussian processes. With minimal prior knowledge, the workflow efficiently produces ptychographic reconstructions that are superior to those processed by experienced experts. The method also facilitates better experimental designs by exploring optimized experimental parameters from simulated data.
电子叠层成像技术为以亚埃级深度空间分辨率解析原子结构以及以高剂量效率研究电子束敏感材料提供了新机遇。在实际操作中，要获得准确的叠层成像图像，需要同时优化多个通常基于反复试验选择的参数，这导致实验通量较低，并阻碍了该技术的更广泛应用。在此，我们开发了一种自动参数选择框架，通过高斯过程贝叶斯优化来解决这一问题。该工作流程只需极少的先验知识，就能高效地生成比经验丰富的专家处理的结果更优的叠层成像重建结果。该方法还通过从模拟数据中探索优化的实验参数，促进了更好的实验设计。
