Unsupervised machine learning combined with 4D scanning transmission electron microscopy for bimodal nanostructural analysis.

ABSTRACT: Unsupervised machine learning techniques have been combined with scanning transmission electron microscopy (STEM) to enable comprehensive crystal structure analysis with nanometer spatial resolution. In this study, we investigated large-scale data obtained by four-dimensional (4D) STEM using dimensionality reduction techniques such as non-negative matrix factorization (NMF) and hierarchical clustering with various optimization methods. We developed software scripts incorporating knowledge of electron diffraction and STEM imaging for data preprocessing, NMF, and hierarchical clustering. Hierarchical clustering was performed using cross-correlation instead of conventional Euclidean distances, resulting in rotation-corrected diffractions and shift-corrected maps of major components. An experi

SUBMITTER: Kimoto K 

PROVIDER: S-EPMC11303778 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature