Navigating the vast pool of possible chemical compositions and molecular/crystal structures presents a significant challenge, akin to exploring a multidimensional surface, one step at a time. While databases of known structures and properties provide a valuable foundation, the search space is too expansive for exhaustive exploration using traditional methods alone. Chemical heuristics, grounded in empirical knowledge and informed by data, can play a crucial role in guiding this search to relevant regions, and in defining safe pathways to follow . Accelerated random structure searching and approximate probe structures have also been used to identify fertile regions of chemical space Generative AI now offers a complementary solution to these challenges, providing tools for data-driven optimisation and discovery . By learning from both existing datasets and theoretical models, generative AI can be used to explore uncharted regions by drawing from the distributions of known materials to propose new candidate compounds with targeted properties. Early models based on generative adversarial networks (e.g. CrystalGAN ), and variational autoencoders (e.g. CDVAE ), are now being superseded by generative diffusion models (e.g. DiffCSP and MatterGen ). These can be used as a tool for crystal structure prediction (mapping from chemical formula as input to candidate crystal structures as output) or the compound generation can be conditioned to achieve target property values in an inverse materials design workflow (mapping from property as input to candidate chemical formulas and crystal structures as output).