as implemented in scikit-learn is used to classify pairs of components as possible cocrystals. We set the hyperparameter, number of estimators, to 300 and randomly split the dataset into a training set (80%) and a validation set (20%). The RF model achieved an accuracy of 0.91 and an F1-score of 0.91 (Supplementary Fig. ). The RF model outputs a number, named Pcc, which interprets the percentage of decision trees in the forest voting for a positive outcome. Therefore, this number is no longer binary (i.e., 0 or 1) but continuous, ranging from 0 to 1, and can be used to represent the likelihood that two components can form a cocrystal. The closer the number is to 1, the more likely the pairs of components are to form cocrystals. The stoichiometry of each individual cocrystal was not incorporated as a model parameter and will instead be explored experimentally. Detailed information was provided in Supplementary Information Section 1 and 7.