Unravelling the microscopic structure of materials
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
Read more news
Samu Taulu develops next-generation brain imaging methods
Associate Professor of neurostimulation and neuroimaging Samu Taulu develops more precise methods for understanding the brain
EAA Best Paper and Presentation Award for Young Researchers for postdoctoral researcher Thomas Deppisch
The awarded work shows how speech can be made clearer in noisy environments while still preserving directional cues
Professor Hanna Maylett: 鈥淲ithout time spent together, a shared vision cannot emerge鈥
According to Hanna Maylett, associate professor of film directing, a director doesn鈥檛 need to know all the answers; rather, their role is to create a process through which those answers can be discovered together.