To put the ML performance into perspective, we performed tests with 11 human MOF synthesis experts. We developed an online quiz based on 50 MOFs randomly selected from the SynMOF database which will be publicly available. The participants were given the 3D structures of MOFs, chemical structures of the linkers and information on the metal ion, and asked to estimate synthesis conditions such as temperature, time, solvents and additives without any help from literature or other external sources (SI Section 3.7). After each MOF synthesis prediction, we also asked the participants to estimate how certain they are in the answer. The correlation coefficients r 2 between the experts' temperature and time predictions and the reported synthesis conditions are close to zero, even after averaging over 11 estimates by different researchers (Fig. ) and after sorting only by predictions with high certainty. This rather surprising result shows that even small correlations learned and exploited by the ML model will help to estimate better synthesis conditions. In summary, we showed that the ML models are able to learn generalized patterns and correlations in the SynMOF database, which exceed the experts' general intuition, and thus, could be used to identify good first guesses for experimental synthesis attempts of new MOFs.