0.7 Å Resolution Electron Tomography Enabled by Deep‐Learning‐Aided Information Recovery
Chunyang Wang, Guanglei Ding, Yitong Liu, Huolin L. Xin
Abstract
The 3D determination of a nanomaterial's atomic structure is crucial for understanding their physical, chemical, and electronic properties. Electron tomography, as an important 3D imaging method, offers a powerful method to probe the 3D structure of materials from nanoscale to atomic scale. However, the grand challenge—the missing‐wedge‐induced information loss and artifacts—has greatly hindered them from obtaining 3D atomic structures with high contrast, high precision, and high fidelity. Herein, for the first time, by combining atomic electron tomography with an artificially intelligent “deepfake” neural network, this work demonstrates that the resolution of 3D imaging can be improved down to 0.71 Å, which is a record high resolution achieved by electron tomography. It is also shown that the lost information in reconstructed tomograms can be effectively recovered by only acquiring data from −50 to +50 ° (44% reduction of dosage compared with −90 to +90 ° full tilt series). In contrast to conventional methods, the deep‐learning model shows outstanding performance for both macroscopic objects and atomic features solving the long‐standing dosage and missing‐wedge problems in electron tomography. This work provides important guidance for the application of machine learning methods to tomographic imaging atomic‐scale features in nanomaterials.