While interesting it is unlikely that these structures are going to significantly contribute to an ensemble of representative structures, even when accounting for the potential trapping in kinetic wells. Using 1 kT at 300 K as an estimate for the structures likely present we note that only Pt 13 has significant unique structures that will have large presences in the representative ensemble. Pt 13 H 4 has a population of structures at ≈ 2 kT at 300k. It is difficult to prescribe anything physical to the large peaks in in figure . It may be that these are the most common local minima within the restricted PES. However, swarm intelligence is an inherently biased methodology by design and is not designed to find the full PES just the minima (or maxima) within it. For this reason it is hard to determine whether the large peaks are due to abundance of structures at this energy or that pyGlobOpt is particularly good at finding these structures. We can get an estimate for the amount the pyGlobOpt biases towards by comparing the total energy distribution to that of the unique energy distribution plots in figure . We note that in all cases the unique structures' peak occurs at a higher energy than the peak for all structures produced. This indicates that pyGlobOpt is biasing towards lower energy structures, i.e. it is working as intended, and so one should be careful when using algorithms such as this when generating ensembles of structures.