The main metric to evaluate the potential of an OSC is the PCE. Although there are multiple models to predict PCE, such as Scharber's model, they fail to accurately make predictions for nonfullerene based devices . To overcome this, our previous work took a data-driven approach to create an empirical linear model known as the Organic Photovoltaic Efficiency Predictor (OPEP) model based on approximately 110 non-fullerene acceptor/polymer donor pairs, of which 77 have PCE greater than 9%. Although the model showed good performance in predicting the high-PCE range above 9% (R 2 = 0.55, RMSE = 1.6%), a larger dataset is needed to generalize the model. In the first part of this work, an updated OPEP2 model is developed. To expand the training data, a new dataset containing 1,225 donor/NFA pairs, of which 1,001 are unique, is prepared from hundreds of publications, containing photovoltaic performance such as opencircuit voltage (V OC ), short-circuit current (J SC ), fill factor (FF), and PCE, and optical and electronic data such as ionization en-ergy, electron affinity, optical bandgap, and wavelength of maximum absorption. Unlike other datasets , the donors and NFAs include both polymers and small molecules. A distribution of the experimental PCE in this data set (ESI Fig. ) displays an even distribution among high and low PCE.