To that end, from an initial seed distribution of 1,000 MOFs, we optimise the c p by setting the target value high for property maximisation and low for property minimisation. Here, high and low targets refer to the upper and lower limit values encountered in the training set, respectively. Taking only the best predicted structure from an aggregate of 10 local optimisations (c.f. the Optimising individual MOF structures section), we recompute the "ground truth" c p using the ML model of Moosavi et al. and display the results in Figure This achievement is made possible through an optimisation strategy that closely reflects the physics of c p itself: in crystalline materials such as MOFs, atoms are arranged in a lattice structure and can only vibrate about their fixed positions. The c p , which measures the energy required to increase the temperature of the solid by one degree, directly correlates to the energy required to alter these vibrations. Notably, these vibrational changes require more energy for heavier atoms, resulting in a higher c p per atom (expressed in J mol -1 K -1 ). However, experimental measurements of c p are often reported per gram of material, so expressing c p in units of J g -1 K -1 offers more practical insights. In this case, linker molecules with a greater molecular weight tend to produce MOFs with a higher atomic c p but a lower per-gram c p . Figure (a) reveals that, when tasked with minimising the c p on a per-gram basis, our deep dreaming model favours the addition of heavy atoms like bromine, sulphur, or phosphorous to the linker backbone, leading to MOFs with a higher average atomic mass and, consequently, to lower c p values. This is evident when we inspect the attention weights of the model, whereby Group SELFIES tokens such as [Br] and, in the example shown in While our results illustrate the model's nuanced approach to optimising MOF properties through strategic linker modifications, a natural question emerges on the quality of the linkers generated in the reverse process. As we utilise pretrained weights and biases of the deep dreaming model to try and explore different, potentially unexplored areas of the linker phase space, it becomes necessary to confirm that the optimised materials are sufficiently diverse, novel, and unique. We therefore compute the MOSES 40 metrics described in Table to address these concerns. It is recommended within this framework to benchmark the performance of generative models using 10,000 samples, and so we extend our analysis from Figure (a) to a distribution of 10,000 seed MOFs and report the results in Table .