The estimation of rare-event probabilities is a central challenge in computational physics and biophysics, where transitions between metastable states (such as protein conformational rearrangements or escapes from disordered energy basins) occur on timescales far beyond standard simulation trajectories. Overcoming these barriers requires enhanced sampling frameworks. This project focuses on developing, implementing, and evaluating adaptive Markov chain Monte Carlo (MCMC) algorithms paired with molecular dynamics engines.