Metal-organic framework (MOF) chemistry has flourished through the creation of a vast chemical space where more than 100,000 MOFs have been discovered. The number is increasing rapidly with a wide and continuously expanding variety of structural types, building units, linkage chemistry, and functional groups. In fact, the chemical space of possible MOF structures is so huge that it is impossible to fully explore it experimentally. , Simulation and machine learning (ML) have evolved as important tools for guiding researchers to computationally identify regions of interest. However, in order to synthesize the novel MOF structures, the researchers still have to rely on their experience, employing a trial-and-error approach (Fig. ). This is a very challenging process that is highly time-consuming, labor-intensive and requires a lot of resources. Therefore, the search for an efficient way to find the optimal MOF synthesis conditions represents the current bottleneck in speeding up MOF exploration.