Advancing atomic electron tomography with neural networks
This is an incremental review that addresses challenges in atomic structure determination for materials science researchers.
The paper reviews how neural networks, particularly convolutional neural networks, are integrated into atomic electron tomography to improve reconstruction fidelity by overcoming artifacts from geometric limitations and electron dose constraints, enhancing accuracy in 3D atomic imaging for nanomaterials.
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.