The good performance of RegioML and WLN is in large part due to the large datasets available for this type of reaction. For example, if we retrain the RegioML model on the same 532 reaction we used to develop RegioSQM20, the performance is much worse due to a large increase in the false negative rate leading to a recall (the percentage of positives that are predicted correctly) below 50% compared to 80% for RegioSQM20. Thus, one use of physics-based approaches like RegioSQM20 is to generate synthetic data for ML model for reactions where there is little experimental data. We demonstrate this by showing that the performance of RegioSQM20 can be reproduced by a ML model trained on RegioSQM20generated data.