A more fundamental challenge that is specific to the G2Ea learning task is that the prediction problem is underdetermined with respect to the manner in which computational E a datasets are currently generated (Fig. ). While experiments measure an effective activation energy based on the Boltzmann average over all accessible conformations and transition states, computationally derived activation energies are typically extracted from a single pair of energies corresponding to a particular reactant and TS conformation that are not always uniquely defined. Available datasets have no guarantee of conformational completeness or of having found a globally minimum energy barrier for the observed reactions. This will lead to an irreducible error for any G2Ea model trained on available quantum-chemistry derived datasets, since predictions are conditioned on the conformational distribution of reactants and TSs used during curation. For example, the Conformer-Rotamer Ensemble Sampling Tool (CREST) algorithm was used during the curation of the Reaction Graph Depth 1 (RGD1) dataset to find the minimum energy conformer of the isolated reactants, and a protocol specific to Yet Another Reaction Program (YARP) was used to select up to three conformations for double-ended TS searches. These choices will show up as inductive biases in models trained on RGD1 and any incomplete conformational sampling will show up as an irreducible error when predicting on unseen reactions.