To showcase the framework's potential on more systematically sampled data, reaction yield of Buchwald-Hartwig cross couplings generated by Ahneman et al. was modeled. Unlike the Suzuki coupling data, reaction yields were determined solely through high throughput experimentation (HTE) from a single laboratory and 26% of the dataset consisted of "negative reactions" (Figure ). For this dataset, a single amine is reacted against 16 aryl halides, 3 bases, 4 ligands, and 24 additives (Figure ). Prior modeling revealed that testing on unseen additives can lead to much higher error rates. Notably, additives are not the only critical factor in yield determination, thus, I probed the performance of Crystal-Yield when predicting on not just unseen additives, but unseen aryl halides, bases, and catalysts. Baseline models of Random Forest, Adaboost, and Gaussian Process were used. As with each previous test set, unseen molecules (halides, bases, ligands, additives) were used. Model performance for the Buchwald-Hartwig reactions was the average of k-fold validation, where each fold consisted of several unseen halides/bases/ligands/additives. Once again, similar performance of Yield-BERT and the baseline models was observed, however, GraphRXN clearly showcased its specialization in modelling HTE data. Notably, when the base Crystal-Yield model (~130,000 parameters) was used to predict Buchwald-Hartwig yields on unseen ligands, GraphRXN outperformed with an MAE of 13.8 compared to Crystal-Yield's MAE of