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Related Experiment Video

Updated: Sep 19, 2025

Array Tomography Workflow for the Targeted Acquisition of Volume Information using Scanning Electron Microscopy

Array Tomography Workflow for the Targeted Acquisition of Volume Information using Scanning Electron Microscopy

Published on: July 15, 2021

Advancing atomic electron tomography with neural networks.

Juhyeok Lee^1,2, Yongsoo Yang^3,4

¹Energy Geosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA. jhlee0667@lbl.gov.

Applied Microscopy

|June 18, 2025

Summary

This summary is machine-generated.

Neural networks enhance atomic electron tomography (AET) for precise 3D nanomaterial imaging. This advancement overcomes reconstruction challenges, improving accuracy for materials research and nanoscience applications.

Keywords:

3D structural analysis Atomic electron tomography Neural network

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Last Updated: Sep 19, 2025

Array Tomography Workflow for the Targeted Acquisition of Volume Information using Scanning Electron Microscopy

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Picometer-Precision Atomic Position Tracking through Electron Microscopy

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

Area of Science:

Materials Science
Nanotechnology
Computational Imaging

Background:

Accurate 3D atomic structure determination is vital for understanding nanomaterial properties.
Atomic electron tomography (AET) provides high-precision, non-destructive 3D atomic imaging.
Reconstruction artifacts in AET limit reliable atomic structure determination.

Purpose of the Study:

To review recent advances in neural network-assisted atomic electron tomography.
To highlight how deep learning overcomes AET reconstruction challenges.
To showcase the impact of improved 3D atomic imaging on nanoscience and materials research.

Main Methods:

Integration of deep learning, specifically convolutional neural networks, into AET workflows.
Utilizing neural networks to improve the fidelity of 3D atomic reconstructions.
Reviewing and synthesizing recent literature on neural network-assisted AET.

Main Results:

Neural network assistance significantly enhances the accuracy of AET reconstructions.
Improved surface and bulk structural characterization of nanomaterials.
Overcoming persistent challenges in 3D atomic imaging fidelity.

Conclusions:

Neural network-assisted AET is a powerful tool for advancing nanoscience.
These methods enable more accurate 3D atomic structure determination.
New opportunities in materials research and technology are emerging due to improved imaging capabilities.