At CMP, Alexander’s research focuses on the simulation of light-driven chemistry on metal surfaces using machine learning methods and mixed quantum-classical molecular dynamics. 

Experiments using ultra-fast laser pulses have shown that energy transfer from light into molecular degrees of freedom on metal surfaces is more selective than under purely thermal conditions, potentially enabling more efficient catalysis. However, the complex interactions between light and matter are challenging to simulate at chemically relevant time scales, where his research attempts to bridge a gap between efficiency and accuracy. 

We’re excited to have Alexander on board and look forward to his contributions to the group!