To better understand how SPPs change trial structures at these three compositions, we studied the correlation between the SPP scores per atom and their DFT energies per atom for S1 and S2 structures. The coloured circles in Figure show that for S1 structures there is a higher degree of correlation observed for Li2Sn2S3Cl4, and Li3Si3O5Cl5, where the improvement of runtime was greater (Fig. ), than for Li4BrOCl. For these two compositions we observe a wider range of DFT energies per atom and a clear separation between the data for S1 and S2 structures (the coloured and grey circles respectively in Fig. ). In contrast, for Li4BrOCl, spanning a narrower range of DFT energies per atom, the differences between S1 and S2 structures are less pronounced. Li4BrOCl is the least complex out of the three systems from a minimisation perspective, as it has the fewest atoms per formula unit, only 7 compared to 11 and 16, and therefore a simpler energy landscape. By using the SPP pre-optimisation step we improved the quality of the trial structures from S1 to S2 more for Li2Sn2S3Cl4, and Li3Si3O5Cl5, whereas trial structures for Li4BrOCl experienced less change in the SPPoptimisation step and represented a simpler optimisation task. While the SPP score is ~10 4 times faster to calculate than the DFT energy, our examples show that it can be helpfully used to do some of the structure optimisation. For three multi-anion test systems, we applied our workflow to a selected set of reference structures with relevant chemistry and found that a geometry optimisation step using SPPs improved the quality of the trial structures and shortened the runtime of the consequent DFT calculations. Therefore, the biggest impact the SPPs make is for rapid geometry optimisation of the high-energy structures, particularly in large systems with a complex energy landscape.