We were motivated to generate a synthetic dataset for this study due to the lack of noise-free dataset options. Datasets generated by density functional theory (DFT) calculation are often considered for the role of a low-noise chemical dataset as they are not subject to experimental uncertainty in data collection like most datasets would be. Indeed, DFT datasets are available and with large dataset sizes in the case of QM9 49 or PCQM4Mv2 . The properties calculated in these datasets depend on the 3D atomic coordinates used in the calculation. The choice of a different molecular conformer to be used in calculation or a different optimization process to find the optimized atomic coordinates would result in different property targets. In our study of error types, we are using the connectivity graph representations of molecules, commonly referred to as 2D representations. The models using 2D graph representations do not distinguish between variations in optimized 3D coordinates or choice of molecular conformer, meaning that the models would not have access to all the features necessary to calculate the property and therefore would have some level of irreducible error, manifesting as noise. By creating a group-additivity dataset, the features necessary to calculate the modeled property are explicitly available with a 2D model representation, allowing us to avoid this source of irreducible error.