Moreover, most of the erroneous predictions belong to the Training set which was unexpected. Therefore, we investigated the reason behind these outlier molecules by analyzing the related molecule pairs. First, we selected 39 molecule pairs that have an error over 0.05 Hartree predicted by the ensemble model. Then, for every selected molecule pair, we searched the most similar pairs from RedDB as described in the Methods section. Using the similar molecule pairs found by the similarity search, we compared their DFT computed reaction energies. We calculated the deviation of the reaction energies of the selected pair and the similar molecule pairs from their mean reaction energy. As a result of the comparison, we found that the reaction energies of selected pairs were much different than their similar pairs while the reaction energy values of similar pairs used for comparison were close to each other. This result pointed out a possible error that is coming from the DFT computations for the selected molecule pairs. We further investigated these molecule pairs by inspecting their 3D geometries. We confirmed that 25 molecules from the inspected pairs had significant structural distortions in their DFT-optimized geometries, which is a sign of chemical instability. Therefore, these pairs of molecules have been removed from RedDB in its next version.