We took several GNNs that have shown promise on OC20 and OC22, and trained them on tmQM. Surprisingly, these models were consistently unable to predict the energies of a subset of tmQM, even if that data was included in the training set. To address these inconsistencies, we recomputed the energies of structures in tmQM with a higher quality level of electronic structure theory (i.e. single-point calculations), using ωB97M-V/def2-SVPD instead of TPSSh/def2-SVP. Our chosen basis set includes diffuse functions, which should improve energy predictions, particularly given the nature of the electronic structure of the systems studied in tmQM. The ωB97M-V functional is also expected to perform better than TPSSh based on studies of DFT performance on transition metal complexes. The new dataset is presented as tmQM wB97MV, and is given alongside a number of ML model benchmarks, learning curves to assess model performances, and a chemical space representation illustrating its improvements. Models trained on tmQM wB97MV yielded predictions that were consistent with training data, and error metrics improved in all cases.