-A few trends become apparent from this visualization. As seen in Figure (a), SCOP-PCQC is primarily localized to two regions, while the active learning expansions have broader coverage of chemical space. Many molecules in the SCOP-AL-Exp set are localized around the SCOP-PCQC molecules, suggesting that despite the chemical similarity of structures, their excited state energies may be significantly different. We further observe that the QM-symex-10k dataset provides a uniform sampling of the overall QM-symex dataset, and that the QM-symex datasets are significantly different from PCQC. Turning to test datasets, we note that the INDT dataset is significantly different from both the PCQC and QM-symex based training datasets. VerdeDB has some molecules outside but near the PCQC training sets, while others are within the PCQC space. Finally, both MOPSSAM datasets seem to lie within the PCQC training set space, implying good predictive performance is expected.