The mission of this lab is to develop ML methods to solve fundamental problems in the sciences and humanities. One exemplary aim is the efficient and accurate inference and simulation of molecular properties, which involves ML methods and ML-driven simulators for quantum mechanics, molecular mechanics and statistical mechanics. A concrete goal is to be able to simulate protein-inhibitor systems in a computationally efficient way with an ML-driven accurate description of quantum effects, long-ranged interactions and within a rigorous statistical mechanics framework.
Director
Research Junior Group Lead
Fellow
Fellow
Fellow
Research Junior Group Lead
Fellow
Fellow
Fellow
Fellow
Fellow
Fellow
Fellow