Fully optimizing tens of thousands of configurations at the B97-3c level of theory will be too computationally expensive. GFN1-xTB can reduce the computational time of the B97-3c optimization by giving a better start guess. During geometry optimization, the energy of the molecular structure decreases with each iteration as the geometry approaches convergence. Relaxing the convergence criteria can result in a reduction in the number of required iterations and can significantly expedite the optimization process. In the case of large cluster systems, the potential energy surface (PES) can exhibit significant complexity. In such instances, it is often advantageous to conduct a preliminary "pre-optimization" using the same method but with less stringent convergence criteria. This pre-optimization step can aid in identifying and excluding configurations with high energies, thus enabling full optimization to be performed exclusively on conformers that exhibit lower energies and are more proximal to local minima. Figure shows the geometry optimization convergence behavior of the (SA) n (DMA) n systems, with n =1-5. For each system, a total of 1000 full geometry optimizations were performed at the B97-3c level of theory. Each system is presented as the average over the entire ensemble of 1000 cluster configurations. It is evident that the energy rapidly decreases during the first few iterations and subsequently slows. By the 40th iteration, the energy difference had decreased to below 5 kcal/mol.