Download Statistical Machine Learning for Electron Microscopic

Statistical Machine Learning for Electron Microscopic Spectral Imaging
Motoki Shiga1, 2
1Gifu
2PRESTO,
University, 1-1 Yanagido, Gifu, 501-1193 Japan,
Japan Science and Technology Agency, 4-1-8, Honcho, Kawaguchi, Saitama, 332-0012
Japan
E-mail: shiga_m @gifu-u.ac.jp
With the development of microscopy technologies and the first principles calculations, the size of
datasets to be analyzed in materials science has been rapidly increasing. Against such a large size of
datasets, informatics technologies such as machine learning and data mining have become more important.
This talk introduces our recently developed machine learning methods for scanning transmission electron
microscopy (STEM) with comprehensive electron energy-loss (EELS) and energy-dispersive X-ray (EDX)
spectral data analysis. The spectrometers in the equipment collect a set of spectra called spectral imaging
(SI), each from the sub-nanometer area of the sample by the fine incident electron probe consecutively
scanning over the two-dimensional region. An important task on SI data analysis is to automatically
identify basis spectra and spatial distributions of chemical components on a specimen. This automatic
analysis can be performed by a matrix factorizations approach such as Principal Component Analysis
(PCA) and Nonnegative Matrix Factorization (NMF) [1]. Because of the natural assumption of NMF, in
which both spectra and intensities of chemical components are non-negative, we took a NMF approach and
developed a new method with two features: 1) a spatial orthogonal penalty to clearly resolve EELS/EDX-SI
into each component and 2) an automatic determination of the number of components using only a given SI
dataset, without any experts’ knowledge [2]. This talk also provides example results of SI data analysis by
our developed method.
[1] M. Shiga, K. Tatsumi, S. Muto, K. Tsuda, Y. Yamamoto, T. Mori and T. Tanji, "Sparse Modeling of
EELS and EDX Spectral Imaging Data by Nonnegative Matrix Factorization," Ultramicroscopy,
170, 43-59 (2016).
[2] M. Shiga, S. Muto, K. Tatsumi and K. Tsuda, "Matrix Factorization for Automatic Chemical
Mapping from Electron Microscopic Spectral Imaging Datasets," Transactions of the Materials
Research Society of Japan, 41 (4), 333-336 (2016).