We split the data into training, validation, and test sets using a 0.8/0.2 train/val split as well as via LOCO-CV. We report two types of validation tests as summarized in Table . One of the validation methods uses a weighted root-mean-square error (RMSE) of various multi-objective Pareto front properties (target vs. chemical uniqueness proxy). The target is weighted against the proxy property (Eq. ( )):