We showcase the contribution of the different components of SMA-MD by analyzing state populations and potential energies for a molecule in the test set at the two different sampling stages (sampling from the surrogate model and applying postprocessing). To find the slowest transitions between metastable states, we use timelagged independent component analysis (TICA) , a linear dimensionality reduction technique that identifies the linear combinations of molecular features that maximizes the autocorrelation. In Figure (a) we observe that the Boltzmann surrogate captures the relevant states, potentially transitioning to each other in slow processes. Moreover, reweighting and running short simulations adjust the populations and reduce the energy of the ensemble, matching ground-truth long RE simulations 2(b).