The above diffusion studies used the final DP 10 model. However, the active learning curriculum used for model training allows us to compare the trends in model performance and reliability across different training iterations. As shown in Fig. and the violin plots in S4-11, we observe a general trend where the rate of model convergence is correlated with the chemical complexity of the adsorbate. For instance, Fig. compares the model uncertainty (as measured by ϵ t ) over multiple training iterations for O and OH. While the model learns to describe the dynamics of atomic adsorbates such as O (Fig. ) in a few iterations, more complex behaviour is observed for OH (Fig. ). Specifically, the MD simulation using the iteration 2 model (i.e., DP 2 /MD) explores a wider configuration space resulting in a higher ϵ t than the previous iteration, which is then well-described in the subsequent DP 3 model. This ability to self-correct is also observed for ethyl (Fig. ) and is a major advantage of a curriculum-based training approach. Specifically, ethyl requires the longest number of iterations to converge, which is also reflected in the higher number of sampled configurations across all training iterations (Fig. ). Similar plots and trends for the other adsorbates can be found in the SI (Fig. ). While it is possible that graph-based MLP architectures may require fewer training data, these comparisons are beyond the scope of this work.