The philosophy of molecular design driven by framework design principles outlined for MOFs has been used to facilitate new materials discovery with great success. A number of crystalline MOFs with preferred topologies are formed by "reticular synthesis", a conceptual approach assembling judiciously designed molecular building blocks into predetermined rigid and ordered frameworks, which creates strong coordination bonds between inorganic and organic units. However, the successful synthesis of a wider range of MOFs with distinct crystal and pore structures that conform to desired design specifications and exhibit expected physical and chemical characteristics remains a process of "trial-and-error" informed by chemical understanding and expert knowledge. Developments in high-throughput experimental techniques and computational screening approaches have enabled the synthesis and evaluation of a much larger number of materials. The integration of data science techniques, particularly those based on ML workflows, with experimental discovery offers a new direction in material research. It allows systematic material data evaluation at scale to offer statistical advice and concurrently assist chemists in discerning and prioritizing certain chemistries when designing new materials. ML can numerically represent real chemical problems and address complex issues, particularly those involving large combinatorial spaces, non-linear processes, or problems that cannot be precisely modelled by existing theoretical methods. For instance, it can be used to analyse potential relationships between the high-temperature/solvent-removal stability of MOFs and their chemical/geometric structures , and also to accelerate the discovery of low thermal conductivity oxides or high-performance inorganic crystalline solid-state lithium ion (Li + ) electrolytes. To date, over 100,000 experimental MOF structures have been synthesised and deposited into the Cambridge Structural Database (CSD). Alongside these experimentally synthesised MOFs, a substantially larger number of hypothetical MOFs have been generated computationally to identify the most promising materials for a desired application by systematically traversing feasible combinations of building units in diverse topologies.