We have recently developed a way to accelerate the genetic algorithm using machine-learned interatomic potentials trained on-the-fly using active learning .The machine-learned interatomic potentials are used to quickly identify likely low-energy clusters, which are further relaxed locally by DFT to refine the energies. This method has been used to identify low-energy structures for S clusters of size 51, 52, and . Additional details about this method can be found in reference .