Eike Eberhard

Eike Eberhard is a Researcher at the Technical University of Munich, affiliated with the TUM School of Computation, Information and Technology and the Department of Computer Science. His work focuses on the intersection of machine learning and quantum chemistry, particularly in developing advanced models for electronic structure modeling and AI4Science.

• Education
  • M.Sc. in Computational Science and Engineering (2022–2024), TUM, with a thesis on GNN-Based Global Exchange-Correlation Functionals in Kohn-Sham DFT
  • M.Sc. in Biophysics (2021–2023), TUM, with a thesis on Force Generation by Enhanced Diffusion in Enzyme-Loaded Vesicles
  • B.Sc. in Physics (2017–2021), TUM, thesis on Extending Dispersive Bounds to Include Sub-threshold Branch Cuts

Eike’s research spans Machine Learning applications in Quantum Chemistry, Graph Neural Networks, and Density Functional Theory. He contributes to open-source projects like eg-xc and mess, focusing on differentiable Hamiltonian evaluation and molecular dynamics. His Google Scholar profile highlights his preprint on equivariant, non-local electron density functionals.