Three separate GNNs were trained, referred to as DFTNN, ExpNN-dft, and ExpNN-ff. Architectures and hyper-parameters for these networks are the same, but they are trained against different targets or using different input structures. The DFTNN is trained against DFT calculated chemical shifts using the optimized geometries from the DFT8K dataset with randomly initiated parameters. This model is then retrained against experimental chemical shifts from the Exp5K dataset while retaining the DFT geometries, with partially fixed parameters to generate the ExpNN-dft model. Finally, the model is again retrained using experimental chemical shifts from Exp5K while geometries are replaced by MMFF structures, with partially fixed parameters to produce the ExpNN-ff model. Further details on transfer-learning and frozen parameters are given in the Supplementary Methods section 3.