The ∆H V GNN predictions also benefit substantially from bootstrapped model ensembling to reduce over-fitting and mitigate outliers in test set prediction parity, while no benefit is observed in the ϕ RF models. Consequently, we observed the need to re-calibrate the bootstrapped uncertainty metric derived for the ϕ RF models, but not for the ∆H V GNN models. It should be noted that re-tuning the hyperparameters during model ensembling could further reduce over-fitting but comes at a large computational cost (e.g., tuning 2 hyperparameters with 10 possible values each would already require training 100 times more models). Finally, in both exemplars, we generally observe continued improvement in model performance with more training data for moderately difficult OOD inference (e.g., structure, composition, or chemsys splits). However, for their weakest inference task (Elements for ∆H V GNN and SG# for ϕ RF models), neither is likely to improve further with additional data indicating fundamental limitations of the respective model architectures.