The best DL model (MLP trained on ECFP, R 2 = 0.46±0.04) performs well because the ECFP representation extracts relevant features from the molecular structure, making the inherently challenging task of learning the most relevant features from sparse and low quantity data less difficult for the MLP algorithm. In addition, DL models are biased towards smooth functions which make it challenging for the model to learn from tabular data and discrete data, as is the case with this dataset. Interestingly, while the GNN regressor struggled due to the size of the dataset, the graph embeddings extracted from the GNN embedder combined with a tree-based or kernelbased prediction model outperformed the direct prediction of PCE from the graph. Independent of model choice, the best representations are found to be material properties, ECFP, Mordred descriptors, and graph embeddings (model performance in Table ). OHE is a good baseline representation in that if the same model performs worse with a new representation, it is likely that the new representation is uninformative. OHE performs poorly across all of the models because none of the chemical, substructural, or atomic characteristics are captured. We find that most representations perform significantly better than OHE. However, when only including fabrication conditions, the nature of which will be detailed in Section 4, the difference is very small. This suggests that fabrication conditions alone are not very informative. None of the models that were evaluated were able to take advantage of the sequence information in SMILES and SELFIES, explaining their poor performance. Models such as long short-term memory (LSTM) networks may be useful. However, the dataset is likely too small for the LSTM network to accurately learn the syntax and grammar of string-based representations in addition to accurately learning the complex relationships between the representation and properties. Despite the modest R 2 scores, we find that our top-performing models (RF and HGB) either match or surpass the state-of-the-art for other similar OPV datasets (Figure ). In particular, we achieve better predictive performance (R 2 = 0.5±0.03) using only ECFP and the material properties available in the dataset in comparison to the performance (R 2 = 0.4) achieved when using computed descriptors. We also explored the ability of multi-output models for predicting PCE. While the most common approach is to predict PCE as the single output, PCE can also be predicted as the product of its components -JSC, VOC, and FF (Equation ). We selected three models: two that natively support multi-output regression in scikit-learn (RF, HGB), and an artificial neural network (ANN) made in PyTorch, which is similar to the MLP described above (further details available in Methods). The models were trained concurrently on PCE, JSC, VOC, and FF. Model scores for PCE prediction were calculated using Equation 1 from the predicted JSC, VOC, and FF (Figure ). Ultimately, model performance was not significantly different between directly predicting PCE (Figure ) and predicting PCE from JSC, VOC, and FF. However, we observed that all models were able to predict JSC much better than VOC, despite heuristics suggesting a strong correlation between VOC and LUMOA -HOMOD, which suggests that this problem is worth further investigation.