Comparison of the high-PCE models suggests similar performance among several models. When selecting one for use, Fig. reveals that the random forest has the highest accuracy of the test set, with an R 2 of 0.4 ± 0.07 and RMSE of 1.5 ± 0.21. Although this model performs well with accuracy, the highest PCE it can predict is 15.9%, much lower than the experimental maximum of 18.4%. Examining the PCE prediction ranges (Fig. ) indicates that the NN model predicts the highest PCE. To create a model that has good accuracy similar to random forest and a larger PCE prediction range such as NN, an ensemble method is proposed that provides the benefits of both models. Using a voting regressor, the predictions of both models are averaged to produce a final PCE prediction. The correlation of these predictions with the experimental data (Fig. ) indicates an R 2 of 0.28 ± 0.05 and an RMSE of 1.6 ± 0.14, with the highest PCE prediction of 17.4%. Further analysis of its prediction in multiple classes of NFAs (Fig. ) shows a good correlation among fused ladder-type backbone NFAs, and some of the variance in the predictions lies within the Y-series NFAs, a class not as commonly represented in the data set as fused NFAs.