Associated Projects

The main projects P1-7 are accompanied by a selection of associated projects funded mainly by external sources. The topics range from machine learning and philosophy of computer simulation to method development in computational catalysis and protein research.


Philosophy of Computer Simulation

"In the materials sciences [... the] use of computer simulations raises a series of philosophical questions that are also highly relevant in other fields of scientific research: Can the validation and verification of computer simulations be decoupled? Are computer simulations epistemically opaque? [...] What role do computer simulations play in understanding physical processes on different time and length scales? [...] What epistemic goals are related to the use of computer simulations? [...] What is, e.g., the epistemic role of visualizations?" Read more at

Contact: Julie Schweer


Method Development for Computational Catalysis

Reaction energetics in (ab initio) computational catalysis are typically derived from local harmonic approximations of the potential energy surface. However, this Harmonic Approximation (HA) can become inaccurate for some systems (e.g. at elevated temperatures or for weakly interacting systems during adsorption processes). This project combines molecular dynamics simulation, coordinate transformations and thermodynamic integration to compute anharmonic corrections to the HA.

Contact: Jonas Amsler


Exciton Transfer Simulations in Organic and Biological Systems

Simulations of exciton transfer processes in huge molecular systems such as organic crystals and biological light-harvesting complexes are computationally challenging. The goal of this project is the development and application of a program for direct exciton transfer to study transport mechanisms and to calculate physical observables without any prior assumptions. This is realised utilizing non-adiabatic dynamics methods to simulate the dynamics of the coupled electronic and nuclear degrees of freedom in a QM/MM scheme (DFTB+/GROMACS). The electronic degrees of freedom are treated with a coarse-grained Frenkel Hamiltonian that is parametrized on-the-fly with quantum or machine learning approaches.

Contact: Philipp Dohmen


Name Title Group Contact
M.Sc. Prof. Marcus Elstner, KIT IPC (associated)
M.A. Prof. Rafaela Hillerbrand, KIT ITAS (associated)