A01
Modern Monte Carlo Approaches with Machine Learning Potentials for Material Science Applications
Investigating energy-device systems with computational methods requires large timescales and an accurate treatment of all the atomic interactions present, thus this project aims to develop methods that can efficiently yield accurate results for this purpose. In order to overcome the limitations of conventional Ab Initio Molecular Dynamics (AIMD), the project will employ Hamiltonian Monte Carlo (HMC) approaches as well as Machine Learning based Force Fields trained using ab initio data from AIMD simulations. Overall this project aims to unlock new possibilities in investigating highly complex systems and to provide valuable insights into materials used in energy devices.
Barbara Kirchner
Professor of Theoretical Chemistry
Michael Griebel
Professor of Mathematics
Carsten Urbach
Professor of Theoretical Physics
My research focuses on Computational Physics, and in particular Lattice QCD, Lattice Field Theories, algorithm development and statistical data analysis. This naturally includes high performance computing.