Machine learning for scattering data: strategies, perspectives and applications to surface scattering

HIGHLIGHTS

• who: X-ray diffraction and colleagues from the Institute of Applied Physics, University of Tuu0308bingen, Auf der Morgenstelle, Tuu0308bingen, Germany have published the research: Machine learning for scattering data: strategies, perspectives and applications to surface scattering, in the Journal: (JOURNAL)
• what: Some of the existing ML solutions for automated GISAS analysis focus on particular morphological models. Most of the current approaches focus on preliminary filtering of huge amounts of data. For inelastic or quasi-elastic scattering (Grimaldo et_al, 2019), or other forms of addressing the dynamics such as XPCS (Sinha et_al, 2014; Timmermann et_al, 2022 . . .
  

   

  

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