Manifold learning of four-dimensional scanning transmission electron microscopy

@article{Li2019ManifoldLO,
  title={Manifold learning of four-dimensional scanning transmission electron microscopy},
  author={X. Li and O. Dyck and M. Oxley and A. Lupini and Leland McInnes and John Healy and S. Jesse and S. Kalinin},
  journal={npj Computational Materials},
  year={2019},
  volume={5},
  pages={1-8}
}
  • X. Li, O. Dyck, +5 authors S. Kalinin
  • Published 2019
  • Computer Science, Materials Science, Engineering, Physics, Mathematics
  • npj Computational Materials
Four-dimensional scanning transmission electron microscopy (4D-STEM) of local atomic diffraction patterns is emerging as a powerful technique for probing intricate details of atomic structure and atomic electric fields. However, efficient processing and interpretation of large volumes of data remain challenging, especially for two-dimensional or light materials because the diffraction signal recorded on the pixelated arrays is weak. Here we employ data-driven manifold leaning approaches for… Expand

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