Lectures

Asymmetric Catalysis from the Theoretical Perspective

Analytical methods for emerging contaminants: advanced tools to understand environmental and technical processes

Expanding the toolbox for integrated structural biology of nucleic acids

Singlet Fission in organic materials

Insight-Driven Strategies in Catalysis for Selective Transformations and Late-Stage Functionalizations

Behavior of Molecules: From Catalysis to Biological Functions

Microbial symbioses and the evolution of novel organelles

Toward Intelligent Design of Molecular Catalysts of Electrochemical Reactions

Einführungsvorlesung: Using data to solve problems: The new role of pharmacoepidemiology in patient care

Molecular Design of Organic Ions for Asymmetric Catalysis

The Development of Peptide- and Peptoid-Based Treatments for Neglected Tropical Diseases

Excited-state Dynamics Simulations with Machine Learning

Light can induce a wealth of processes in electronically excited states but corresponding simulations are limited by the costly computations of potential energy surfaces. A solution to this problem will be presented, where machine learning potentials are used to carry out excited-state molecular dynamics. The dynamics is simulated with our surface hopping approach called SHARC (surface hopping including arbitrary couplings), which is able to treat not only kinetic dynamical couplings but also any other arbitrary coupling on an equal footing. Consequently, machine learning is employed not only for potentials but also for nonadiabatic couplings. These developments open up the possibility to simulate time scales in the nanosecond regime compared to a few picoseconds in conventional approaches.

Molecular simulations of flexible enzymes: binding affinity prediction and insights into   biocatalyst and antidote design

Behavior of molecules: from catalysis to biological functions

Single Metal Atoms as Game-Changers in Heterogeneous Catalysis