Integration of Deep Learning Methods and Monte Carlo Techniques for Estimating Molecular Observables

In this thesis, we introduce ab-flowMC, an adaptive Markov chain Monte Carlo (MCMC) framework that enables the simultaneous training of one Normalizing Flow (NF) and one Machine Learning Potential (MLP) per state while sampling a Boltzmann weighted molecular ensemble. This approach allows for the ra...