The similarity in performance of the models is likely due to the inherently noisy nature of experimental chemistry: where different chemists, different reagent lots, and different environments can cause shifts within the experimental value. By its very nature, the USPTO Suzuki dataset is comprised of reactions from multiple chemists across the country and across decades of research. Indeed, historical bias is often present in this style of collated data, which can translate to poor machine understanding. Additionally, negative data is rarely observed in this dataset, with only 0.9% of all reactions reporting a yield under 5% (Figure ). Prior research have noted the importance of this "negative data" in predictive modelling. Crystal-Yield's average MAE of ~18% is a step forward towards accurate modeling of noisier data.