Computational Surface Science

The Max Planck Fellow group "Machine Learning Methods in Chemical Dynamics" uses machine learning (ML) techniques in combination with electronic structure theory and advanced simulation methods to study non-equilibrium processes governing energy conversion, catalysis, and material transformations. These processes, which occur on timescales ranging from femtoseconds to microseconds, are essential for the bottom-up design of systems at the atomic and molecular level. Despite significant methodical progress in both experiment and modeling, many fundamental aspects of these dynamics remain challenging to study due to their inherent complexity and transient nature. For this, we collaborate with groups studying microscopic non-equilibrium phenomena on different time and length scales, specifically in the departments “Surface Dynamics” (Wodtke) and “Ultrafast Dynamics” (Ropers). Our ML/AI methods also contribute to the interpretation and prediction of spectroscopic measurements and to the advancement of modern imaging techniques.

For more details about our research, our group members, and opportunities with us, please visit our website: https://maurergroup.github.io/