Hands-on DFT and Beyond: Accuracy, Efficiency, and Reproducibility in Computational Materials Science

The discovery of novel materials is key on the route to face global challenges like the quest for efficient and sustainable use of energy resources. Computational approaches play a central role here as they allow us to explore uncharted territory in chemical and materials space, for example in order to develop novel batteries, highly efficient solar cells, stable biocatalysts, or carbon dioxide fixation strategies. 

Novel Materials Discovery by Learning from Electronic-Structure Theory is the central theme of this summer school, we will teach the basics and recent advances of electronic-structure theory. 

The focus is in particular on density-functional theory (DFT), but also topics beyond DFT will be covered: ab initio thermodynamics and statistical mechanics, excited-state properties, nuclear quantum effects, multi-scale modeling, and machine learning approaches to potential parametrization, Big-Data dimensionality reduction, and property prediction.