A general data-driven framework for scalable electron tomography

Abstract Electron tomography (ET) is crucial for determining the three-dimensional (3D) structure of materials in real space but challenging due to the inherent missing-wedge, intensive-dose, and limited depth-of-field. Although deep learning can address these challenges in principle, the scarcity of ET data severely limits its application. In this study, we propose a general data-driven ET reconstruction framework that uses extensive and readily available random high-entropy projections to construct large-scale datasets. By integrating both real structural priors and depth-dependent imaging physics, the framework enables high-quality 3D reconstruction independent of specific materials or resolutions. Using this strategy, we successfully determine the 3D atomic structure of a 13-nm Pt nanoparticle containing 52 138 atoms, achieving a root-mean-square displacement of 22.6 pm; and the projection consistency error is significantly reduced, effectively expanding the depth-of-field limit of atomic-scale ET.