Figure first validates the capabilities of Equation (2) to capture the configurational energy landscape of hydrogen absorption in PdH x , Pd 0.91 Rh 0.09 H x , and Pd 0.91 Ag 0.09 H x alloys. Training configurations for 0 < x < 1, where x is the hydrogen to metal ratio (H/M), were generated by sampling random alloy configurations in the 32 atom FCC supercell and random hydrogen occupation of the octahedral interstitials, with 80 DFT-relaxed configurations per x (Supplementary Section 1). Figure shows train and test set predictions for Pd 0.91 Rh 0.09 H 0.5 alongside several minimum and maximum energies predicted by ML screening 10,000 random configurations, which again demonstrates successful out-of-train-distribution predictions. Finally, Figure shows both histograms of the signed test set errors for Pd 0.91 Ag 0.09 H x and the overall test MAE when only 80%, 8%, 4% or 1% of the train split is used for model training. The ML still achieves high performance with a test set MAE of ∼1.5 meV/atom when only ∼100 configurations are used for training, although the addition of 20x more training data still contributes to significant error reduction.