Alessandro Coretti

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.