Alexander Spears
I am a third-year Physics PhD student at the University of Vienna in the research group of Prof. Reinhard Maurer.
My research focuses on the simulation of light-driven hydrogen evolution. A number of experiments using ultra-fast laser pulses have indicated that the transfer of energy from light into molecular degrees of freedom is more selective than under purely thermal conditions, potentially enabling more efficient catalysis.
The complex interactions between electrons, light and adsorbate molecules at metal surfaces are not well understood, both due to the computational power required to simulate molecular dynamics outside the Born-Oppenheimer approximation, as well as the different time scales of electronic, lattice and electromagnetic responses.
A variety of methods has been developed to include the effects of electron-nuclear coupling in classical molecular dynamics, such as electronic friction or surface hopping.
In combination with machine-learning methods to calculate interatomic potentials and other parameters, I hope to simulate non-thermal hydrogen surface chemistry in adequate detail at reduced computational cost.
To better capture light-matter interactions at a sub-picosecond scale, I will attempt to use and improve methods to describe light excitations beyond the methods currently used to verify experimental results.
Session
I would like to show you how I combine Python- and Julia-based machine learning models into a scalable molecular dynamics (MD) simulation workflow using the NQCDynamics.jl package.
My research focuses on simulating light-driven chemistry on metal surfaces, where non-adiabatic coupling between electronic and nuclear motion requires custom MD methods.
If you are in the field of non-adiabatic / excited state dynamics, I hope I can show you why this might be interesting for your own simulations.