For the last two decades, metal-organic frameworks (MOFs) have been an extensive object of study thanks to their high porosity and their extensive spectrum of applications, including gas storage and separation, sensing, catalysis and drug delivery. MOF synthesis consists of the self-assembly of the organic ligand and metal component into a periodic network. Several methods for MOF synthesis have been developed including solvothermal, electrochemical, mechanochemical, microwave, and ultrasound. In all these, one usually tries to find the optimal conditions at which crystals can form. Often this requires finding a sweet spot where the binding of the ligand and metal node is sufficiently strong that a stable crystal can form, but not too strong that the system quickly forms an amorphous structure which cannot be crystallized. In addition, different topologies may form depending on the synthesis conditions. There are a considerable number of parameters that can influence the reaction and its outcome (i.e., solvents, pH, reagents concentration, reaction time, temperature, pressure, etc.), and the optimization of these conditions for new or established MOFs is often laborious, expensive and time-consuming. While conventionally, the optimization of these parameters rest on the chemical intuition of individuals, novel approaches are needed to tackle the extensive diversity in chemistry of MOFs. Therefore, data-driven approaches have been developed to accelerate such optimization processes. Moosavi et al. combined a genetic algorithm (GA) with machine learning (ML) to optimise the synthesis of the MOFs. They illustrated their approach with the synthesis of HKUST-1 39 using a microwave-based robotic platform, to find the synthesis conditions of HKUST-1 that yielded high quality crystals. This approach not only aims to find the optimal reaction conditions, but also aims to learn the most important experimental variables from analysing both successful, partly successful, and failed experiments.