Flat histogram sampling is a powerful Monte Carlo approach used to overcome the limitations of traditional MC simulations, particularly when treating complex energy landscapes that feature rare events. Unlike standard simulations that rely on random sampling, flat histogram methods aim to ensure an equal exploration of all possible states or configurations within a system's phase space. For example, the Wang-Landau algorithm performs a non-Markovian random walk to build the density of states by quickly visiting all the available energy spectrum. This resulting sampling distribution leads to a simulation where the energy barriers are invisible, meaning that the algorithm visits all accessible states (favor-able and less favorable) much faster than a Metropolis algorithm. Flat histogram sampling has been applied to drive simulations to calculate density of states, explore the flexibility of nanoporous framework, and explore water adsorption. We took inspiration from these approaches to develop sampling strategies to extract frames from the large amount of AIMD trajectories that represent the reference data.