Eike Eberhard

Technical University of Munich
Department of Computer Science - I26
Boltzmannstr. 3
85748 Garching b. München
Germany

Room: 00.11.051
E-Mail: e.eberhard@tum.de

GitHub: ESEberhard
Google scholar: Google scholar

Research Interests

• Machine learning for graphs and molecules
• Machine learning for quantum chemistry
  • Density functional theory
  • Electronic structure modelling
  • Force-fields
• AI4Science
• Graph neural networks

Selected Publications

• Learning Equivariant Non-Local Electron Density Functionals
  Eike Eberhard, Nicholas Gao, Stephan Günnemann
  Preprint
   

For a full list, see Google scholar.

Education

• 2022 - 2024: M.Sc. Computational Science and Engineering (CSE), Technical University of Munich
  • Thesis: GNN-Based Global Exchange-Correlation Functionals in Kohn-Sham DFT
  • best of class, passed with high disctinction
• 2021 - 2023: M.Sc. Biophysics, Technical University of Munich
  • Paper originating from Thesis: Force Generation by Enhanced Diffusion in Enzyme-Loaded Vesicles
    (Preprint) Eike Eberhard, Ludwig Burger, César L. Pastrana, Hamid Seyed-Allaei, Giovanni Giunta, Ulrich Gerland
  • passed with high distinction
• 2017 - 2021: B.Sc. Physics, Technical University of Munich
  • Thesis: Extending Dispersive Bounds to Include Sub-threshold Branch Cuts