For instance, O 2 dissociation, as shown in Figure , exhibits decreasing uncertainties until iteration three, after which the uncertainty worsens across all samplers (Figure ). As a consequence, the DP 3 model successfully identified an MLP/TS that was successfully refined using DFT. In contrast, the DP 7 model did not yield a valid MLP/TS for O 2 dissociation. This observation underscores a key limitation of the parallel training approach when combined with an adaptive sampling scheme. Specifically, while the DP 3 model successfully captures the relevant portion of the reaction coordinate for O 2 dissociation, the adaptive sampling scheme induces an on-the-fly data bias as configurations relevant to this reaction are sampled less as the uncertainty decreased (see Figure ). This issue is further exacerbated for O 2 dissociation as the reaction network does not contain any other O-O bond formation reactions. While not discussed here, similar trends are observed for reaction 19 (Figure ).