Chemical reactivity is a fundamental aspect of chemistry that determines how substances interact with each other to form new compounds. Understanding chemical reactivity allows scientists to predict and control chemical processes, design new materials, synthesize drugs, and develop sustainable technologies for energy conversion and storage. We are particularly interested in real-time reactivity prediction to support synthesis planning and uncertainty quantification to ensure high-quality predictions. To this end, we apply a combination of quantum chemistry and machine learning.
Lead Researcher: Maike Eckhoff
Active learning enhances the efficiency of machine learning workflows by selectively querying the most informative training data points generated by experiment or computation. In this way, the amount of training data needed can be significantly reduced, which saves resources and boosts efficiency. This approach is particularly important when training data is scarce or expensive to obtain—such as in chemistry or the natural sciences in general.
Lead researcher: Elizaveta (Liza) Surzhikova
Drug design is a critical field in medicinal chemistry that focuses on creating new therapeutic compounds that can effectively interact with biological targets to treat diseases. Synthesizability ensures that the designed molecules can be efficiently and economically produced in the lab or on an industrial scale, which is crucial for practical application. By integrating computer-aided drug design, quantum chemistry, and machine learning, we aim to accelerate the discovery of new drugs on different scales.
Lead Researcher: Kerrin Janssen