In this work, we present Surrogate Model-Assisted Molecular Dynamics (SMA-MD) as an approach to efficiently sample from the Boltzmann distribution. The overall workflow of SMA-MD is summarized in Figure and consists of two main steps. In the first step, we use a generative model trained to emulate the Boltzmann distribution of molecules (the Boltzmann surrogate) to generate conformations. These conformations are constructed in two sub-steps: starting by generating local geometries with a deterministic algorithm and then sampling the torsion angles using a DGM to mix across the molecules' 'slow degrees of freedom'. Next, in the second step, we use short parallel MD simulations to thermalize and mix the fast degrees of freedom. Combining simulations with an exact reweighting scheme enables us to generate unbiased samples from the Boltzmann distribution of the molecule. These ensembles can then be used to compute thermodynamic properties of interest.