Research at the Interface of Atomistic Simulations, Quantum Mechanics, and Machine Learning
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At the interface of Chemistry, Simulation and AI
Our work brings together quantum chemistry, atomistic simulations, and machine learning to study complex materials and chemical processes. Our main research interests include the global geometry optimization of solids, surfaces, and molecular systems, as well as catalytic reaction mechanisms and dynamic processes of matter.
Find out more about our research combining active machine learning with atomistic simulations based on quantum chemical calculations and analysis of diffraction experiments.
New Software and modern Hardware
The field of computational quantum chemistry is experiencing a paradigm change, moving beyond purely classical quantum chemical approaches toward new methods based on machine learning and emerging hardware platforms such as NISQ quantum devices.
Our group contributes to this development by exploring and shaping these new computational strategies.
