While there are other multiobjective design settings to be explored for DAC (see Supplementary Note 8), here we seek to optimise both Q CO 2 and S CO 2 /N 2 simultaneously from a seed distribution of 1,000 MOFs. For this, we train a single model to predict both properties and invert the architecture for deep dreaming. Similar to the single objective case studies, Figure (a) and (b) reveal shifts in the property distributions towards higher Q CO 2 and S CO 2 /N 2 values. However, these shifts are less pronounced than those observed for c p (Figure (a)) or, for example, the bandgap (Supplementary Fig. ). We expect this arises for two related reasons: first, the mapping between MOF strings and the adsorption properties are less accurate with the sample size used to train our model (Table ). Second, we do not posit any 3D coordinate information into our MOF string representation. As the adsorption properties are highly dependent on the spatial configuration of the atoms (albeit less so at the limit of infinite dilution, where they are primarily determined by the