Chemical reactions are all around us, forming the basis of life. For example, we digest food and
breathe air, convert energy stored in molecules to muscle movement, or transfer and process
information using reactions. A chemical reaction transforms one or more educts into one or more
products. Using quantum-mechanical calculations, reactions can be studied in detail, for example
how the atoms in the educts iteratively move until the product is formed. However, such
calculations are prohibitively slow to use on a larger scale. The project aims to adapt generative
machine learning models to predict the atomic configurations along reaction pathways in the gas
phase, as well as for organo- and biocatalytic reactions, i.e., reactions that are catalyzed by either
small organic molecules or enzymes. Here, architectures prominent in other fields of research, such
as diffusion models for image generation, will be adapted for chemical research questions. The
trained models can then be used in larger machine learning pipelines to predict reaction properties,
helping to understand, model, and optimize diverse chemical reactions on the computer.