Christoph Dellago

GenAIMMD is introduced, an iterative algorithm that actively and self-consistently learns the ideal reaction coordinate (the committor) and trains a conditioned Boltzmann Generator to sample from arbitrary bias windows along it, providing a correlation-free and fully parallelizable path sampling scheme that does not require prior knowledge of the system's transition mechanism.

This work introduces NS-Flows: a single conditional normalizing flow, conditioned on the NS energy bound and trained on a sliding window of recent live sets, that replaces MCMC by direct parallel draws corrected by importance-weighted rejection resampling, allowing flow training without structured priors or a pre-existing dataset.

NAFFS can reduce simulation time for rare molecular events by up to three orders of magnitude while maintaining statistical rigor and mechanistic insight.

DFT-trained neural network potentials can accurately model hydration and exchange kinetics of magnesium ions, but struggle with solvation free energy predictions.

NATPS offers a computationally cheaper route to simulating rare photochemical events by combining time-reversible nonadiabatic dynamics with transition path sampling.