We found the HGB model, a gradient-boosted decision tree-based algorithm, to be best at predicting PCE from molecular structure, and from molecular structure and device fabrication features. Despite modest R 2 scores of ca. 0.6, this reflects the state-of-the-art predictive performance for PCE. The same HGB algorithm outperformed models trained on a dataset almost twice the size (ca. 1000 data points) of ours, and matched the performance of models when trained on a dataset more than double the size of ours (ca. 1300 data points). The performance of the HGB algorithm may be in part attributed to its ability to extract maximal information by incorporating missing valuesof which there are manywith its binning strategy. There are a number of ways in which model performance could be improved. Firstly, the "big p, small n" problemwhere the number of variables or features (p) is much larger than the number of observations or data points (n)might be overcome by simply adding additional data points. The quality of the dataset could also be improved by including more varied device fabrication procedures for each material combination. The current iteration of the dataset primarily includes only the top performing device from each donor:acceptor combination found in a paper. However, there are often others that may provide additional information despite lower PCE values. Furthermore, most reported PCE values correspond to the best single device, and not an average of multiple replicates, due to current reporting practices.