The number of configurations sampled for each adsorbate during the training curriculum is shown in Fig. . The first few iterations of our active learning protocol result in uniform sampling across different adsorbates. However, as expected, we observe that the number of configurations sampled per iteration decreases during model training. Ethyl requires the largest number of iterations and sampled configurations to converge. Taken together, the final DP 10 model is trained on ∼27,000 gas-phase, ∼27,000 1-layer and ∼6,000 4-layer DFT data points. Although not used here, we note that analogous workflows can now be implemented through the DPGen interface. 56