However, when we train multiple ML models that have been shown to be capable of predicting the energies of various chemistries, and they are consistently unable to predict the energies of a subset of the data (even when that data is included in the training set), questions may be raised as to whether those energies are consistent, i.e. if there are issues with the underlying data. While the ML model may just be unable to learn those energies, we can think of it also as a diagnostic tool -the ML model may be able to show inconsistencies in data that would otherwise be unnoticeable. We then want to see if these models are able to predict the energies of the entire dataset given energies at a different level of theory that is expected to better capture the chemistry at hand, motivating the generation of tmQM wB97MV. If so, we have strong evidence that the original data was inconsistent, and the new calculations are an improvement. If not, studying those structures whose energies cannot be fit may provide valuable insights into either the DFT or the ML models. Either way, ML acts as a useful tool for identifying the quality of data and offers insights beyond what a human observer would be able to come to.