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
• Subproject of: SFB 1639: NuMeriQS: Numerical Methods for Dynamics and Structure Formation in Quantum Systems
• Applicant Institution: Rheinische Friedrich-Wilhelms-Universität Bonn
• Project Heads: Professor Dr. Michael Griebel; Professorin Dr. Barbara Kirchner; Professor Dr. Carsten Urbach