A Gaussian Parameterization for Direct Atomic Structure Identification in Electron Tomography

TL;DR

This paper introduces a novel method for directly identifying atomic structures in electron tomography by representing atoms as learnable Gaussians, improving robustness and accuracy over traditional volumetric approaches.

Contribution

It reformulates the inverse problem in electron tomography to directly learn atomic positions and properties using a Gaussian parameterization, incorporating physical priors for better results.

Findings

Enhanced robustness to imaging artifacts

Successful simulated and real data experiments

Potential for improved materials characterization

Abstract

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.