Ever increasing computational power and the rise of AI have boosted the importance of computational approaches to science.

Nowadays, computational simulations can easily predict the outcome and speed of complex chemical reactions both microscopically and on a reactor scale or screen hundreds of materials for optimized performance in a fraction of a second.

In our lab, we focus on the application of such computational techniques to sustainable energy science, which we believe will lead our global energy future. For this, we focus on three main points:

- A detailed quantum-chemical understanding of the stability and activity of electrochemical interfaces, accelerated by machine learning.
- The development of kinetic models and coupling to mass transport effects (multiscale and digital twin based design).
- The derivation of electrocatalytic activity and selectivity descriptors and application for high-throughput computational material screening via machine learning.

Please contact Stefan Ringe directly.

Please contact Stefan Ringe directly.

The exposure to computational simulations and programming happens usually after joining our lab, so there are no specific requirements. We are searching generally for students who are motivated and eager to learn and understand more and have a good background on mathematics, physics and chemistry.

Please contact Stefan Ringe directly.