In the context of catalyst design using ML methods, the model is more likely transferable to new sets of catalysts and substrates when trained on datasets with larger chemical diversity. If a component is kept constant (e.g., all the reactions in the dataset are applied to the same substrate), then the model will not be trained to understand the impact of this component (e.g., not applicable to the search for ideal substrates for a given catalyst or prediction of the substrate scope of an asymmetric reaction).