Several efforts have been made to explore NFA chemical space. The most notable high-throughput virtual screening (HTVS) study, the Harvard Clean Energy project, screened 51,000 NFAs and found promising design strategies . Another HTVS study used random forest and gradient boosting models to screen around 32 million D/A pairs, with predicted high-performing pairs confirmed by experiment . While HTVS can screen a massive subset of chemical space, its highly inefficient and wastes computational resources on examining low-performing molecules. An alternative approach is to use ML to guide molecular generation. Peng and Zhao used a convolutional neural network to generate thousands of NFAs and predict their energy levels and PCE, each evaluation tuning the model to generate better NFAs . While this approach is promising, the training of these models can also be computationally expensive.