Dario Baum

Machine Learning for the Acceleration of Quantum Chemical Simulations

The work presented in this thesis shows that efficient ML for quantum chemistry does not rely on a single universally optimal model class, and hybrid strategies that combine explicit physical models with learned components can be as effective as fully data-driven approaches while retaining the robustness and interpretability of the underlying physical description.