These enhanced sampling or extended ensemble methods rely on two broad strategies: finding a surrogate of the Boltzmann ensemble that rapidly mixes between metastable states or exchanging the state variable with one or multiple ensembles that mix faster ; ; ; ; ; ; ; . To ensure that the modified ensembles sufficiently overlap with the true Boltzmann ensemble, statistical reweighing techniques such as importance sampling, the weighted histogram analysis method , the multistate Bennett acceptance ratio , or transition-based methods ; ; are used. These techniques allow for an effective reweighting of the equilibrium statistics but are disadvantaged by the intrinsic need to identify collective variables, perturb macroscopic thermodynamic variables, or alter Hamiltonians, which often rely on manual trial-and-error of numerous potential candidates . The identification of useful collective variables is a field of its own, with multiple application-specific approaches, including for ligand binding and slow dynamics ; ; . In particular, collective variable estimation has seen a surge in interest in recent years due to the broad accessibility of deep representation learning methods ; ; .