However, this 10k molecule subset of PCQC may be too small for ML model training. In addition, a model trained on only molecules relevant to spectral conversion may have poor out-of-domain performance. Therefore, we turn to active learning to add diverse molecules to the training set with further sampling of PCQC. Here, we use active learning techniques to evaluate regions of chemical space where the ML model is uncertain. Active learning is an ML technique often used to sample unexplored regions of state space. Our implementation uses a trained ensemble of ML models to measure uncertainty of the remaining chemical space. Specifically: first, a 10-ensemble ML model was trained on the 10k SCOP-PCQC molecules to directly predict S 1 and T 1 energies. Then, the ensemble was used to predict S 1 and T 1 energies on the remaining 3.5M molecules in PCQC. The 100k molecules with the highest uncertainty (variance in ensemble prediction) were chosen as an expansion to SCOP-PCQC, labeled SCOP-AL-Exp, for each of S 1 and T 1 . This process helps ensure broad applicability of the ML model. More details about the active learning process are available in SI Section V.