University of Basel

Do we really need nanoparticulate drug carrier systems?

Expanding the toolbox for integrated structural biology of nucleic acids

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

Unsaturated metal isocyanides: molecules to materials

Engineered metalloprotein catalysts for selective group transfer reactions

Quantum-state-controlled Reactive Atom-atom Scattering

On discovery in catalysis

Mapping the Interaction Potential of Chemical Reactions Directly from Scattering Data

Structural, theoretical and spectroscopic studies of LPMOs--copper oxidases which oxidise cellulose

Development of direct aromatic coupling reactions by transition metal catalysis

Discovery, mechanism and application of chemoselective cross-coupling reactions

Necessity is the mother of invention: Natural products and the chemistry they inspire

Electrifying Organic Synthesis – Going Metal- and Reagent-free!