One of the ZIF-8 features yet to be discussed, is its surface area. Structure optimization towards maximizing surface areas of MOFs has been one of the goals of ML approaches., which was also collected for the construction of the data set employed for this work. As shown in the SI file, no correlation was found between the particle's size, morphology, and surface area. This is to say that while synthetic conditions are crucial to determine the operating nucleation and growth mechanisms and the final particle size and morphology, they seem to have little effect on other functional properties, such as BET surface area; once the crystalline particle is formed, its surface area is well-defined, and dispersion on the reported values might be ascribed to the methodology employed to the analysis of gas adsorption isotherms, as recently demonstrated. Determining the impact of synthetic variables on the final particle size through Machine Learning A model that can fit the data and explain a continuous variable, such as the size, is called a regression model. In Machine Learning, a basic and versatile model for making a regression are Decision Trees. Figure , shows an example of how a Decision Tree adjusts data coming from the aqueous-based synthesis of ZIF-8. The algorithm splits the starting data set (root node) based on a question or decision that reduces the variance in each new set and splits the data into two branches. Each of the new sub-sets is going to be split again by a different question, and the process will continue until the remaining data set cannot be split anymore, which constitutes the leaves of the tree. In this case, the minimum number of samples per leaf was set at 2 and max depth at 4 to limit the overfitting . Finally, the average value per leaf was calculated as the outcome of each path. While individual decision trees can be interpreted in a relatively straightforward manner, the results might not be accurate and there is still some risk of overfitting the data. To cope with this, a Random Forest methodology can be employed. Random Forest algorithms generate a final output by combining the individual output of multiple and 50 randomly created decision trees. By these means, overfitting is reduce and a more accurate model is obtained. Once the model is built, it is then possible to know how much a given variable influences the final prediction, through the so-called SHAP value (SHapley Additive exPlanations). Herein, a random forest algorithm was applied to the dataset, considering as relevant variables the concentrations, time, temperature, agitation, and the counterion. For a better contrast between methanol and water as a solvent, each one was modeled separately with a random forest.