A Gaussian Parameterization for Direct Atomic Structure Identification in Electron Tomography
This addresses the challenge of accurate 3D atomic structure determination in materials science, offering a more robust method for practical TEM applications, though it appears incremental as it builds on existing tomography frameworks.
The paper tackles the problem of directly identifying atomic structures in electron tomography by reformulating the inverse problem to solve for atom locations and properties using a Gaussian parameterization, showing improved robustness to imaging artifacts in simulated and experimental data.
Atomic electron tomography (AET) enables the determination of 3D atomic structures by acquiring a sequence of 2D tomographic projection measurements of a particle and then computationally solving for its underlying 3D representation. Classical tomography algorithms solve for an intermediate volumetric representation that is post-processed into the atomic structure of interest. In this paper, we reformulate the tomographic inverse problem to solve directly for the locations and properties of individual atoms. We parameterize an atomic structure as a collection of Gaussians, whose positions and properties are learnable. This representation imparts a strong physical prior on the learned structure, which we show yields improved robustness to real-world imaging artifacts. Simulated experiments and a proof-of-concept result on experimentally-acquired data confirm our method's potential for practical applications in materials characterization and analysis with Transmission Electron Microscopy (TEM). Our code is available at https://github.com/nalinimsingh/gaussian-atoms.