用神经网络提升电子显微镜原子三维成像精度
Advancing atomic electron tomography with neural networks
- 引入卷积神经网络改善原子级重构的失真问题
- 显著提高表面与体相结构表征的准确性
- 适合材料科学与纳米技术研究者参考
精确测定三维原子结构对于理解与调控纳米材料性能至关重要。原子电子断层扫描（AET）可实现皮米级精度的无损原子成像，能够解析缺陷、界面和应变场的三维分布，并观测动态结构演化。然而，几何限制与电子剂量约束导致的重建伪影会影响原子结构确定的可靠性。近期进展将深度学习，尤其是卷积神经网络，融入AET流程，以提升重建保真度。本文综述了神经网络辅助AET的最新进展，强调其在克服三维原子成像长期挑战中的作用。这些方法显著提升了表面与体相结构表征的准确性，推动了纳米科学前沿发展，为材料研究与技术应用开辟新机遇。
原文摘要 · Abstract (English)
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.
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