Next, evaluation of machine learning algorithms is required to ensure high accuracy for new data. Eight regression machine learning models implemented with Scikit-learn are examined: random forest (RF), LASSO, gradient boosting (GB), extreme gradient boosting (XGB), support vector machine (SVM), knearest neighbors (KNN), lightGBM, and a neural network (NN). The input descriptors contained electronic properties (i.e., frontier orbital energies, fundamental bandgap, etc.), optical properties (i.e., optical bandgap, integral of absorption spectrum, etc.), and 2048-digit extended connectivity fingerprint (ECFP4) counts, with more details in ESI Note S1. The input data underwent pre-processing to scale the values to unit variance, ensuring all data is of the same magnitude, a common requirement for many ML models.The dataset was split into 80% training and 20% testing and the resulting R 2 and RMSE are 5-fold cross validated. Two sets of models were examined, the first trained on the entire dataset, with PCE ranging from 0.01% to 18.77% (999 pairs), and the other on a subset with PCEs greater than 10% (503 pairs). Since the resulting model will be used to find high-efficiency materials, model selection was based on the performance of the high-PCE subset.