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Materials property mapping from atomic scale imaging via machine learning based sub-pixel processing

Engineering and Technology

Materials property mapping from atomic scale imaging via machine learning based sub-pixel processing

J. Han, K. Go, et al.

Discover a groundbreaking machine learning-based method by Junghun Han, Kyoung-June Go, Jinhyuk Jang, Sejung Yang, and Si-Young Choi for enhancing the accuracy of material property mapping from atomic-scale STEM images. This innovative approach combines advanced segmentation, denoising processes, and clustering techniques to achieve sub-pixel precision.

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