2026-08-13 –, Room 2
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.
NQCDynamics.jl² is a non-adiabatic molecular dynamics package our research group has built to facilitate development and large-scale simulations using new mixed quantum classical dynamics methods which go beyond the Born-Oppenheimer approximation.
These methods are necessary to describe the coupling between light, electrons and phonons at interfaces, where ultrafast laser pulses can drive chemical reactivity by inducing mode-selective energy transfer more efficiently than comparable thermal heating.¹
However, computationally simulating the multitude of simultaneous processes occurring at different time scales ab-initio remains a challenge.³
Using interfaces to Julia- and Python-based ML models working in tandem⁴, I am able to simulate the effect of ultrafast laser pulses on hydrogen evolution from copper surfaces efficiently at a large scale.
NQCDynamics.jl provides a framework for initialising dynamics simulations and propagating the equations of motion using DifferentialEquations.jl, with a number of interfaces to the wider Julia- and Python-based molecular simulation ecosystem.
After sampling rare reactive dynamics from thousands of simulations, I can compare energy partitioning in desorbed hydrogen molecules, concluding that the choice of electronic friction approximation only determines the rate of energy transfer, while the energy distributions of desorbing molecules are governed by the potential energy surface. This suggests that thermal and laser-driven desorption may yield similar outcomes at low coverage.
[1]: S. W. Lee, Appl. Surf. Sci. Adv. 16, 100428 (2023).
[2]: J. Gardner et al., J. Chem. Phys. 156, 174801 (2022).
[3]: C. Frischkorn and M. Wolf, Chem. Rev. 106, 4207 (2006).
[4]: W. G. Stark et al., Phys. Rev. B. 112, 2 (2025).
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.