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XPEC will integrate together machine-learning data classification and high-dimensional regression with multilevel quantum chemistry methods (DFT and GW) to create the ultimate predictive tool for computational generation of X-ray spectra.

X-ray spectroscopy is a powerful tool to unravel the microscopic structure of materials, that is, how atoms are arranged at the nanoscale. This experimental technique would in principle allow us to precisely establish the nature of chemical bonding in functional materials and make the link between a material's atomic structure and its performance in real-life applications. This knowledge is of vital importance to optimize the new generation of materials needed for environmentally-friendly technologies, such as clean fuel production and sequestration of atmospheric CO₂. Unfortunately, interpretation of X-ray spectra of complex materials is extremely challenging, since signals coming from different atomic environments overlap. The new computational approach developed within the XPEC project will provide an invaluable tool to predict X-ray spectra and interpret existing experimental results.

Picture: Dorothea Golze