Reaction Yields: Reaction yield prediction, both qualitative and quantitative, has been a rich area for machine learning in chemistry. Driven by its importance to synthetic chemists and by the relative abundance of data through patent literature curation (USPTO) and high throughput experimentation (HTE), several notable ML models have been generated with the intention of accurate yield prediction. It has been noted that a model with good yield prediction accuracy could be utilized for a number of valued fields including reagent ranking and retrosynthesis design, a challenging field often considered by synthetic chemists to showcase the "artform" of synthetic chemistry. The link between solid state molecular structure and reaction outcomes, which are typically performed in solution, is more tenuous than for toxicity, thus yield prediction is a challenge use case for foundational chemistry knowledge extracted from structural data. Two reactions which have seen enormous utility in the synthetic community are the Suzuki and Buchwald-Hartwig coupling reactions, highly versatile palladium-catalyzed carbon-X bond formation reactions. Indeed, Suzuki and Buchwald-Hartwig couplings made up nearly a third of all reactions performed in medicinal chemistry and natural product total synthesis in 2016, with its prevalence only increasing in the following years. Attention was first directed to Suzuki coupling reactions, whose data was sourced from the US patent literature (USPTO). Prior deep learning models have noted excellent performance, sometimes only a 5% discrepancy between experimental and predicted values. However, when a leave-molecule-out approach was taken on Buchwald-Hartwig coupling data, whereby the tested molecules have an unseen molecules, model performance drops precipitously. The model is therefore challenged on this style of splitting, where the top reagents/reactants are left out for the test set. This Suzuki dataset comprised of 5,143 electrophiles, 1,122 nucleophiles, 10 catalysts, and 90 ligands (Figure ). Similar to toxicity predictions, yield prediction baselines of Random Forest and Adaboost with MAE as the metric. Initial trials with Gaussian Process regression models yielded far lower accuracies, thus they were omitted from the baseline measure.