Advancing atomic electron tomography with neural networks

TL;DR

This paper reviews how neural networks are enhancing atomic electron tomography, improving 3D atomic structure reconstructions, and overcoming limitations caused by artifacts and dose constraints in nanomaterials imaging.

Contribution

It highlights recent advances in neural network-assisted AET, demonstrating significant improvements in reconstruction accuracy and reliability for nanomaterials.

Findings

Neural networks improve reconstruction fidelity in AET.

Enhanced accuracy in surface and bulk atomic structure characterization.

Overcoming artifacts and dose limitations in 3D atomic imaging.

Abstract

Accurate determination of three-dimensional (3D) atomic structures is crucial for understanding and controlling the properties of nanomaterials. Atomic electron tomography (AET) offers non-destructive atomic imaging with picometer-level precision, enabling the resolution of defects, interfaces, and strain fields in 3D, as well as the observation of dynamic structural evolution. However, reconstruction artifacts arising from geometric limitations and electron dose constraints can hinder reliable atomic structure determination. Recent progress has integrated deep learning, especially convolutional neural networks, into AET workflows to improve reconstruction fidelity. This review highlights recent advances in neural network-assisted AET, emphasizing its role in overcoming persistent challenges in 3D atomic imaging. By significantly enhancing the accuracy of both surface and bulk structural characterization, these methods are advancing the frontiers of nanoscience and enabling new opportunities in materials research and technology.