Modern Monte Carlo Approaches with Machine Learning Potentials for Material Science Applications
(A01)
Subject Area
Theoretical Chemistry: Electronic Structure, Dynamics, Simulation
Mathematics
Theoretical Chemistry: Molecules, Materials, Surfaces
Term
since 2024
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 511713970
Investigating energy-device systems with computational methods requires large timescales and an accurate treatment of all the atomic interactions present. Therefore, 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.
DFG Programme
Collaborative Research Centres
