The results of our trained ML models are shown in Fig. . Reproducibly positive correlation coefficients r 2 on unseen test datasets show that the ML models are capable of identifying meaningful and predictive relations between the target MOF structure and the required synthesis conditions, in particular temperature and time (Figs. ). Given the amount of data that we have currently extracted from literature, we find that the random forest models have the highest performance across all predicted parameters. However, neural networks learn to make better predictions with growing dataset sizes faster (see learning curves in Fig. ) and even exploit correlations between different synthesis parameters (e.g., solvent and temperature) rather than predicting them separately. Hence, we expect that more complex models will outperform random forests in the near future.