Figure (c) summarizes the MAEs for the parity plots displayed in (a) and (b). Unlike the defect dataset, the MAEs and their standard deviations for the work function dataset are very similar between the non-nested and nested splitting strategies. This likely stems from the GNN-based model being more prone to overfitting compared to the 15-feature RF model. Hence, the GNN model benefits more from statistical averaging during nested splitting. Like the defect dataset, the MAEs increase in the order Random < Structure < Composition < Chemsys < Elements. However, an interesting difference is that the SG# split exhibits the highest MAE, less than the MAE for the Elements split (219 and 149 meV, respectively for nested splits). Compared to the MAE of the random split this is an increase of 133% and 59%, respectively. This agrees well with the RF model features being largely comprised of elemental properties (e.g., electron affinity) while containing little structural information. The work function model generalizes better outside the elemental training distribution and worse outside the structural training distribution. Among all splits that leave one element out, the MAEs are significantly larger for holding out F, H, O, or Cl (1178, 959, 708, 657 meV, respectively; cf. periodic table heat map in Supplementary Figure ). These elements typically exhibit complex chemical behavior that may not be well captured in other chemistries. Compared to random splitting the MAE (94 meV) increases by only ∼ 17% for structural, compositional, and chemical systems splitting (all three have an MAE of ∼ 110 meV for nested splits). This surprisingly small increase in MAE may be explained by the work function strongly depending on the element present in the topmost surface layer -hence, as long as an element is present in any chemical system (or composition) in the train set, the RF model is able to learn the elemental trend for the work function and can then extrapolate well for an unseen chemical system. The average MAE increases (218 meV) by holding out groups of the periodic table compared to holding out just Elements (149 meV). Similarly, the MAEs increase by holding out point groups (227 meV) and crystal systems (272 meV) compared to just holding out space groups (219 meV). Supplementary Figure displays the parity plots, MAE trends, and residuals for these additional hold-out strategies. Similar to Figure (d), the residuals |ϕ DFT -⟨{ϕ ML } L ⟩| are plotted against the standard deviation of the work function predictions over model ensembles in Figure . Interestingly, the averages of the residuals within a given bin of σ({MAE} K ) (red markers) tend to have a slightly greater slope than the x = y parity line (cyan). The overconfidence of this bootstrapped uncertainty metric appears to be typical of tree-based models using hand-engineered features and therefore requires re-calibration 8 such that the expectation value of the residual for a given σ bin is closer to parity.