The example shown in Figure is the result of a single execution of the deep dreaming model. However, since the edge SBU is initialised with random noise before reverse differentiation, the optimisation trajectory (and therefore the final decoded structure) is entirely stochastic. For instance, in Figure , the same seed edge SBU returns different decoded structures across several optimisation runs. In each iteration, the MOF seed is transformed into a structure with an expanded pore space. This is achieved by adding aromatic Group SELFIES tokens into the edge SBU encoding in some cases (i.e., Run 1 -3), or by unfurling the 3-membered ring of the seed linker and extending the alkane backbone between connection points (i.e., Run 40). It is therefore evident that, while the trajectory may change across multiple repeat trials, the design strategy remains consistent. We can interpret these results as a collection of local optimisations, whereby the ease of transforming the initial structure to achieve the desired functionality may vary based on the chosen optimisation path. Given that the decoded structure obtained by a single run can be highly variable due to the formation of local minima in the optimisation landscape, a more reliable outcome can be achieved by selecting the best predicted structure from an ensemble of local optimisations. Moving forward, we embrace this methodology, choosing the best structure from an aggregate of local optimisations for any given MOF.