Before we can begin the simulations we first need to determine which structures to sample with the MC trajectories. Ideally we want to select structures that are diverse and well-separated on the energy surface such that the simulations cover as much of the energy surface as possible with the fewest number of trajectories. Redundant structures that occupy similar regions of the energy surface will add little to improving the MLP while increasing the computational cost. The area of the energy surface that will be covered by each trajectory is difficult to determine a priori, however, using farthest point sampling we can ensure that our selected structures represent a diverse set. Thus, we selected 10 structures from the set of 300 oxalic acid structures by FPS in the descriptor space starting from the lowest energy structure, which also is the match to the β polymorph. On-the-fly training from these structures with an uncertainty threshold of 2.0 kJ mol -1 yielded 1636 structures from the MC trajectories that were added to the training set.