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GIXD data and corresponding fits for machine learning

This dataset contains grazing incidence x-ray diffraction (GIXD) maps projected in q-space. It contains different steps of crystallization and annealing of various perovskite thin films prepared by spin coating. This includes various 2D and 3D perovskites with varying cations (cesium,methylammoni

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CreatorStarostin, Vladimir
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Published2026-03-05
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DOI10.5281/zenodo.18385166
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Downloads253
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Licensecc-by-4.0
File Size402.5 MB
Data TypeDataset
Published2026
Licensecc-by-4.0
Total Views232
Total Downloads253

This dataset contains grazing incidence x-ray diffraction (GIXD) maps projected in q-space. It contains different steps of crystallization and annealing of various perovskite thin films prepared by spin coating. This includes various 2D and 3D perovskites with varying cations (cesium,methylammonium, formamidinium, or their mixtures), anions (iodine, bromine), and spacer molecules (Phenethylammonium, phenylenedimethylammonium, pentafluorophenylethylammonium or (1-adamantyl)methylammonium) in 2D perovskites. The selected patterns contain features corresponding to the final perovskite structures as well as different intermediate products (e.g. complexes with the solvents) and precursors (lead iodide, lead bromide, etc.). A glass covered with indium tin oxide (ITO) or fluorine-doped tin oxide (FTO) and (optionally) mesoporous titanium oxide layers was used as a substrate.

The dataset also includes small-molecule organic thin films, both well-oriented samples with sharp Bragg peaks – such as p -quaterphenyl
(4P), dibenzotetrathiafulvalene (DBTTF), diindenoperylene (DIP), pentacene (PEN), and zinc phthalocyanine (ZnPc) – and 3D-powder-like samples, including buckmin-
sterfullerene (C60 ) and hexaazatriphenylenehexacarbonitrile (HATCN), which exhibit Debye-Scherrer rings with almost uniform distributions of scattering intensity along
the azimuthal direction. The thin films were fabricated by thermal evaporation in a vacuum chamber on silicon substrates.

The dataset contains the Q-space maps as well as their corresponding metadata:

  • Angle of incidence
  • Name of the detector
  • Detector pixel size
  • Energy of the beam
  • Facility
  • Horizontal axis (Qxy) range
  • Vertical axis (Qz) range

All Bragg peaks on the Q-space maps are annotated by a Gaussian fit on top of a linear background:

  • Center of the azimuthal angle (‘angle’) [degree]
  • Extent of the azimuthal angle (‘angle_width’) [degree]
  • Level of the linear background, beginning at beam center (‘background_level’) [intensity counts]
  • Slope of the linear background, beginning at beam center (‘background_slope’) [intensity counts]
  • Radial peak position (‘radius’) [Å⁻¹] 
    • beam center starts with radius 0 
    • maximum radius is sqrt((Qxy-pixel-range)²+(Qz-pixel-range)²)
  • Full width half maximum of gaussian fit in radial direction (‘radius_width’) [Å⁻¹]
  • Visibility level (‘visibility’) [integer]
    • 1 low visibility
    • 2 medium visibility
    • 3 high visibility

 

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GIXD data and corresponding fits for machine learning (Full Dataset)402.5 MB
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Files are hosted on the source repository. Click download to access the full dataset.

Starostin, Vladimir (2026). GIXD data and corresponding fits for machine learning. https://doi.org/10.5281/zenodo.18385166