Procedure for 3D atomic resolution reconstructions using atom-counting and a Bayesian genetic algorithm

TL;DR

This paper presents a Bayesian genetic algorithm that reconstructs 3D atomic models of nanoparticles from single projections, leveraging atom-counting and prior information to improve accuracy in low-dose imaging conditions.

Contribution

The novel algorithm combines Bayesian inference with genetic algorithms to enhance 3D atomic reconstructions from limited projection data, incorporating atom-counting and neighbor-mass relations.

Findings

Accurate 3D reconstructions from single projections.

Effective in low electron dose conditions.

Potential for real-time analysis of beam-sensitive nanoparticles.

Abstract

We introduce a Bayesian genetic algorithm for reconstructing atomic models of nanoparticles from a single projection using Z-contrast imaging. The number of atoms in a projected atomic column obtained from annular dark field scanning transmission electron microscopy (ADF STEM) images serves as an input for the initial three-dimensional (3D) model. The novel algorithm minimizes the energy of the structure while utilizing a priori information about the finite precision of the atom-counting results and neighbor-mass relations. The results show excellent prospects for obtaining reliable reconstructions of beam-sensitive nanoparticles during dynamical processes from images acquired with sufficiently low incident electron doses.