Empirically, device fabrication parameters are known to be important factors in OPV device performance. The lack of improvement in model performance when including these features suggests that it may be due to the quality of the training data. The top-performing model is HGB trained on Mordred descriptors and any of the device fabrication feature subsets (R 2 = 0.61±0.03). This may be due to the fact that HGB can handle missing data points, and therefore is capable of learning input feature-target relationships even from data points with missing features (e.g. total solids concentration and spin coating speed). The top ML model architecture, HGB, using Mordred descriptors for molecular structure and trained on all available features achieves an R 2 score of 0.61±0.03. The model predictions are poor for data points where the true PCE is below 5%. Predictions in this range are as high as ca. 12%. This is to be expected because this range is under-represented in the dataset and therefore the model sees fewer examples on average. A similar phenomenon can be observed in the high-PCE tail of the distribution. In both cases, predictive errors tend toward the points with the highest density in the dataset (ca. 10% PCE). Additionally, there are no predictions near the maximum PCE (15.71%). The same phenomena are observed for models with different molecular representations and data (Figure ).