Research data supporting Unsupervised machine learning applied to scanning precession electron diffraction data
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van Helvoort, ATJ
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Martineau, B., Johnstone, D., Eggeman, A., & van Helvoort, A. (2022). Research data supporting Unsupervised machine learning applied to scanning precession electron diffraction data [Dataset]. https://doi.org/10.17863/CAM.26432
The research presented in the publication is an investigation into the the application of the multivariate analysis methods of linear decomposition and data clustering to scanning precession electron diffraction data. Supporting this are several experimental, simulated, and model datasets. The experimental data comprises several scanning precession electron diffraction (SPED) datasets of a nanowire of gallium arsenide. SPED is a TEM technique where a narrow beam is used to collect a diffraction pattern from every point in a sample scan. Optionally, the beam is precessed - rocked about a double cone above and below the sample. Two samples were studied with various precession angles, detailed in the file names and the metadata associated with each file. The simulated data comprises three multislice simulations performed for the gallium arsenide crystal structure at three precession angles. The model data comprises two simple datasets with basic features mimicking diffraction patterns.
These datasets were prepared using HyperSpy (http://hyperspy.org/) and pyXem (http://pyxem.github.io/pyxem/) and suitable to be analysed using the same.
diffraction, simulation, precession, SPED, gallium arsenide, GaAs, TEM
Related Item: https://doi.org/10.17863/CAM.26444
Publication Reference: https://doi.org/10.1186/s40679-019-0063-3
The Royal Society (uf130286)
European Research Council (291522)
This record's DOI: https://doi.org/10.17863/CAM.26432
GNU General Public License v3.0
Licence URL: https://www.gnu.org/licenses/gpl-3.0.en.html