 | Maximilian Xaver Tiefenbacher University of Vienna Institute of Theoretical Chemistry Währinger Str. 17 1090 Vienna Austria maximilian.tiefenbacher@univie.ac.at +43 1 4277 52760 / 73290 |
| Focus Area |
| 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. My project is realized within the framework of ViRAPID, which is an interdisciplinary Research platform with members from the Math, Physics and Chemistry departments. |
| Publications |
Excited-state Nonadiabatic Dynamics in Explicit Solvent Using Machine Learned Interatomic Potentials Digital Discovery 4, 1478-1491, (2025), DOI: 10.1039/D5DD00044K M. Tiefenbacher, B. Bachmair, C. Cheng-Giuseppe, J. Westermayr, P. Marquetand, J. Dietschreit, L. González Nonadiabatic Forward Flux Sampling for Excited-State Rare Events J. Chem. Theory Comput. 19, 1657–1671, (2023), DOI: 10.1021/acs.jctc.2c01088 M. M. Reiner, B. Bachmair, M. Tiefenbacher, S. Mai, L. González, P. Marquetand, C. Dellago
Recent Advances in Machine Learning for Electronic Excited State Molecular Dynamics Simulations SPR Chemical Modelling 17, 178-200, (2023), DOI: 10.1039/9781839169342 1. N. K. Singer, M. Tiefenbacher, G. Ecker (and all other MolTag Students) Celebration of 10 years of MolTag MolTag Doctoral Program (Youtube) , , (2022) |