Alternative methods included making perturbations to existing crystal structures and choosing to accept or reject the new candidate using a Monte Carlo scheme as well as the generative adversarial network of Group 10. To manage computational cost, many participants chose to terminate their program's execution after a certain number of structures or generation attempts were made. Some participants included convergence criteria by monitoring repeated generation of the lowest energy structures or by monitoring the number of generation attempts without finding new low-energy candidates. In contrast, the method presented here efficiently covers the search space for molecular crystals using a divide-and-conquer strategy that partitions the parameter space into subspaces based on space group symmetries, eliminating the need for the convergence checks required by stochastic approaches.