Once the model is trained to predict a given material KPI, the same architecture is used to optimise MOF linkers (Figure ). Our optimisation strategy is similar but distinct from the original work of Shen et al. First, instead of modifying the entire input representation to minimise the target error, we only modify the edge SBU encoding. Second, while every transmutation is chemically valid by virtue of the SELFIES algorithm, not every decoded molecule represents a valid linker. We consider a linker to be valid if it has two connection points (which we represent using francium pseudoatoms), and these connection points must be sufficiently far away from each other to encourage the formation of a porous crystalline structure. We therefore penalise the formation of molecules with chemical compositions that do not contain exactly two francium atoms, and only retain structures with connection points that are appropriately distanced. Additional details on these constraints can be found in Supplementary Note 4. Finally, the quality of generated linker molecules are evaluated using several metrics, mainly from the MOSES 40 framework, as described in Table Dataset curation