One disadvantage of this approach is that the chemical diversity of the resulting MOFs are limited by the SBUs sampled. For example, only 219 linkers are used in the work of Lee et al., which represents a small fraction of the potential chemical landscape. Other efforts in this domain rely on the use of generative models, which automatically generate new materials by leveraging large datasets of material properties and structures to learn underlying patterns and relationships. Different architectural flavours of generative models have been used for MOF design in the past, such as VAEs, GANs, diffusion models, and reinforcement learning. A recurring theme in these studies is the strategy of pairing a structure-generating module with a property-predicting module, where communication between the two is limited by a reward or loss function. For example, in VAEs, GANs, and diffusion models, the structure generator is developed through extensive self-supervised learning and is directed by the property predictor through an optimisation process that explores a latent chemical space. In reinforcement learning, the agent (structure generator) learns a policy for creating new structures from a reward that is returned from the environment (property predictor), with the objective of maximising the reward. While effective, these optimisation strategies have so far failed to integrate both property prediction and structure optimising tasks into a single module.