The problem is exacerbated by active learning because many active learning algorithms are designed to seek low formation energies, so structures with surprisingly low formation energies are sampled at a better-than-chance rate. If the correction value is negative, as is the case with many species in the standard correction method, structures near the corner of the phase diagram for a corrected species are predicted to be stable. Therefore, active learning algorithms trained on data corrected with the standard method will oversample the faulty region near the corner of the phase diagram. For examples of active learning exhibiting this behavior, refer to the phase diagrams in the supplemental information. In turn, this leads to false "discoveries" of new stable materials and encourages further exploration of this portion of the search space, with both DFT calculations and physical experiments, for an unfounded reason.