To prove broader applicability, external test sets were also compiled. 1143 molecules from Wilbraham et al.'s paper on Mapping the optoelectronic property space of small aromatic molecules (MOPSSAM) was used, 143 from their calibration training set and 1000 randomly selected from the remaining 250k molecules. 10k molecules from Fallon et al.'s paper on indolonaphthyridine thiophene (INDT) derivatives were also used as they are promising candidates for singlet fission. 1000 molecules from Abreha et al.'s VERDE database (Verd-eDB) were used, as the classes of molecules identified (porphyrins, quinones, dibenzoperylenes) are relevant for various green chemistry excited state applications. Finally, to truly test the broader applicability of the model, another active learning cycle was run on PCQC. Using a training set composed of the 10k molecules from SCOP-PCQC plus the 200k molecules from SCOP-AL-Exp, an ensemble ML model was generated and used to evaluate uncertainty on the remaining PCQC molecules. 100k of the highest uncertainty molecules for each of S 1 and T 1 were chosen as the last test set, labeled PCQC-AL. Additional information about active learning is in SI Section V). To visualize the training and test datasets, we plotted the locations of the datasets in chemical space. We used uniform manifold approximation and projection (UMAP), a dimensionality reduction technique that reduces the high-dimensional space of chemical structure into 2 dimensions for ease of visualization. We use the Jaccard-Tanimoto similarity between Morgan fingerprints of molecules as a measure of proximity in chemical space. We first generated a global UMAP based on all molecules, then categorized them into (a) training and (b) test, and colored them based on their dataset, shown in Figure .