However, building a large dataset should be done with care as adding data may also results in loss of diversity and introduction of biases. For example, overrepresenting a class of catalysts may result in the model learning (or even memorizing) mostly about this chemical series (e.g., phenyl better than methyl at a given position on a ring), rather than learning general rules (e.g., steric effects). This can result in significant biases of the model and poor accuracy in the search for novel catalysts. At this point, it is important to note that information following all these criteria is rarely available in a form that can be used immediately (such as a text files or formatted tables). For QM and MM methods, the size of the set is not as relevant, but the diversity should still be a focus of the data collection, as these methods must be able to capture various effects experienced by various catalysts (e.g., hydrogen bonds, cation-π interactions). In practice, should all this information be available (several catalysts from different classes already developed), one may question the need for a model to design novel catalysts for this reaction. Thus, the transferability of the method to other reactions may be investigated (see Applications section). In the case several catalysts are already available but none providing the level of stereoselectivity targeted, an ML model may be required. However, one may question the ability of a model trained on poor to good catalysts to identify excellent catalysts (e.g., based on a different mechanism, on an interaction not experienced by other catalysts). An important aspect is that the model would be as general as the data set is: if the data set contains little variability, the model would likely fail to predict an out of set example.