Maximilian Tiefenbacher

Supervisor: Leticia González, University of Vienna

Co-Supervisor: Chris Oostenbrink, University of Natural Resources and Life Sciences, Vienna


Start of the project: July 2021

Title of the project: Machine learning for non-adiabatic transition path sampling


Research topic of the student:  The over arching goal of my project is to develop new techniques to enable excited-state simulations of photoreactions on longer time scales than currently feasible. My focus is on machine learning models, which I use to accelerate the evaluation of molecular properties such as energies, forces, and dipole moments with quantum mechanical accuracy. The simulation of a system evolving in time is based on such properties. Since most of the reactions I want to study take place in solution, my aim is to implement one of the aforementioned machine learning methods tailored to such reactions. This new method allows the investigation of photoinduced reactions in solution on the atomic level, leading to a more detailed understanding of these systems.