Batteries and catalysts fall into the category of energy materials. These materials are used in a wide range of applications and are an important building block for sustainable energy conversion. Powerful and durable batteries and catalysts are the key to responsible use of the resources at our disposal. The complicated atomic structure of these materials is a challenge for their theoretical description and the simulation of the processes taking place. In particular, processes that shorten the lifetime (finite charge cycles or catalyst deactivation) are often difficult to identify with theoretical models.

Using effective cluster-embedding models, we will simulate building blocks of these materials and explore the chemical space of given compositions using grand canonical Monte Carlo methods. Using new, efficient semiempirical methods, we can perform approximate electron structure calculations, giving insight into the structure and reactivity. We then detect the structure-reactivity relationships using statistical methods (machine learning) in an attempt to identify possible lifetime-shortening processes.
 

If you are interested and have any questions, please feel free to contact us at any time.