There have been a few attempts to address this gap, mostly within the realm of small drug-like molecules. One intriguing example is the work of Shen et al., who introduce the concept of "deep dreaming" for the optimisation of organic molecules. This method uses inceptionism -a technique originally developed to visualise the patterns and features learned by convolutional neural networks from images -to modify the input of a property prediction model that has been trained to predict real-valued properties from molecular strings. Shen et al.'s findings reveal two keys points: first, that a model's learned representations can be used to directly optimise molecular structures towards desired functionalities using gradientbased algorithms (rather than optimising molecules indirectly in a latent space or through a reinforcement policy), thereby generating distributions of molecules with systematically shifted properties; second, that inceptionism allows one to probe what the model has learned of the structure-property relationship by observing the sequence of molecular modifications executed to achieve a particular design objective. Deep dreaming therefore offers a novel way to expand the pool of possible candidate materials for a given application, consolidating the tasks of property prediction and structure optimisation, while also providing interpretable insights into the model's understanding.