The discussion of errors also bears on the relative advantage of formulating E a prediction as a G2Ea task. For example, unintended transition states and biased conformational sampling are not intrinsic problems for some three-dimensional formulations of E a prediction. For example, a model with a three dimensional reactant featurization could plausibly learn the contribution to E a associated with a particular conformer and thus reduce that source of error. Or consider an E a model that performs E a estimation identically to quantum chemical approaches but instead uses ML atomic potentials. Because such a model is formulated to predict reactive potential energy surfaces, it can learn from both intended and unintended TSs and all reactant and product conformations. This formulation may sound much more expensive than directly predicting E a from a pair of chemical graphs, but the advent of GPU-compatible routines for performing TS searches on ML-potentials may render such cost differences moot. These differing formulations of the E a prediction problem can also be expected to affect model tranferability. Better benchmarks will ultimately be required to resolve the accuracy vs cost Pareto front of different E a prediction formulations being gestured toward by this discussion.