Scientific AI
We develop principled AI methods to solve hard problems from the natural sciences. Our expertise are fundamental algorithms and their application to complex problems with spatial structure.
Highlights
Geometric Machine Learning in Quantum Chemistry
Predicting molecular properties is important for a huge range of problems, from biochemistry to drug development.
The most universal approach to such predictions relies on quantum mechanics. We aim to dramatically speed up these quantum calculations using machine learning methods that respect the fundamental symmetries of the problem.
Our Network
We learn a lot from our colleagues! We are proud members of, and help shape the future of, our excellence cluster, our ellis unit and our department.