Speaker
Loubna Kadri
Description
X-ray and neutron powder diffraction are experimental techniques that allow the
determination of structural properties of materials, such as their phase compo-
sition (identification and quantification). Machine learning has the potential
to efficiently replace the traditional procedural paradigm in phase determina-
tion due to its ability to learn data patterns and use them in predictions. In
this study, known structures of different phases were obtained from the Crys-
tallography Open Database and the corresponding powder diffraction patterns
were calculated. Systematic differences between the measured and calculated
diffraction patterns were analysed. A machine learning algorithm was trained
and benchmarked against measured data.
