To evaluate ML-based solvent prediction, we focused on a subset of MOFs which are synthesized using only one solvent. We compared the accuracy of the top 6 ML predictions with multiple random baseline methods (Fig. ), including selection of a random solvent out of all solvents as well as out of the six most frequent solvents that are used in 96% of the single-solvent SynMOF database. We found that the ML model outperforms the random selection, in particular for the top 1 -3 solvent predictions, where the ML model reaches an accuracy of > 90%. In the case of additive predictions (Fig. ), the task of the ML model is to classify required additives as acidic, basic, and no additive. While performing well on the training set, the generalization to unseen test data suffers from an imbalanced dataset (most database entries do not use an additive). We use balance correcting weights of the training data points, leading to predictions which distinguish very well between synthesis procedures involving basic and acidic additives. However, the differentiation between acidic and no additive or basic and no additive is less pronounced. One of the reasons might be related to the hidden variables such as type and function of additives: Some of them (inorganic acids and bases) have only the role of pH regulation, while others (organic acids and bases) are also involved in modulation of the MOF growth. Besides, concentration and strength of additives are additional important parameters, influencing the role of additive.