A random forest model proposed by Kranthiraja and Saeki , trained on 566 NFA/polymer pairs, was able to predict PCE with an impressive R 2 of 0.72. However, this dataset did not take into account the Y-series acceptors, a new class of materials incorporated in many high-performing OSCs. In a followup paper , they expanded this dataset to 1,318 pairs, the current largest dataset containing NFAs, including multiple classes of NFAs such as A-D-A, subphthalocyanines, perylene bisimides (PBI), fullerene-appended molecules, and a few others. They trained a random forest model on this data set with a predictive performance of R 2 of 0.71. However, experimental data is required as input for the model, which is unrealistic for large screening studies. Another model, developed by Wen et al., trained a voting regressor composed of gradient boosting (GB), support vector regression (SVR), and Ridge models on 351 polymer donor / NFA pairs, had an R 2 of 0.71 for the test set . This model was trained using only polymer donors and requires lengthy DFT calculations for input of the model. For chemical space screening and inverse design applications, a model that is able to balance computational cost and accuracy is needed.