Machine Learning. We developed a code to extract the MOF linker from the CIF. The RDKit library was further used to evaluate the molecular fingerprint of the extracted linker. The MOF metal nodes were represented by their full electronic configuration. The molecular fingerprint of the linker and the full electronic configuration of the metal node, accounting for its oxidation state, were combined to form the input of the ML model. This input representation was compared to the MOF representation developed by Kulik and coworkers, relying on autocorrelation features of the metal cores and the linkers. The output of the ML model was the MOF synthesis conditions, namely temperature, synthesis time, solvent properties and additive type. Depending on the specific synthesis conditions, we evaluated several regression models, in particular random forest regression and neural networks. The scikit-learn library in Python was used for the implementation of the ML models. 70% of the full dataset was used to train the ML model, while the remaining data was used to test the model. In the case of solvent property prediction, we limited the data to MOFs with single-solvent synthesis. To quantify the accuracy of the trained ML model, we calculated the mean absolute error and the correlation coefficient r 2 of the training and test dataset for the regression tasks. The accuracy of the ML model for the classification tasks were quantified by calculating the normalized confusion matrix.