We study complex chemical processes and reactions with computational methods.
The following topics have been in the center of our research:
Functional Systems and Catalysis:
We aim to improve computational approaches for the study and prediction of functional systems and employ advanced simulation methods to design real-world applications. One focus has been on developing efficient catalysts for sustainable chemistry and unraveling the mechanisms and reaction networks behind important processes such as water splitting.
Photochemistry and Excited States
Our focus is on method development for simulation of excited electronic states and nonadiabatic processes which are key in applications such as photodynamic therapy and light-to-energy conversion. We have been interested in an efficient and accurate modelling of these phenomena, particularly in the condensed phase.
Spectroscopy
Our research delves into the calculation and analysis of electronic and vibrational spectra using various static and dynamics methods. We are particularly focused on modelling spectra for condensed phase systems and developing efficient approaches for a range of spectroscopic signatures, also for chiral compounds.
Machine learning
Revolutionizing chemistry with machine learning: From predicting molecular properties to enhancing dynamic calculations and reaction exploration, our research pioneers machine learning approaches, non-black-box feature engineering, and innovative algorithms, all while developing advanced tools for real-world chemistry challenges.