By removing outliers more than 1.5 interquartile ranges (IQRs) below the first quartile or above the third quartile, corresponding to 5% of the experimental data, the RMSE drops from 3.8 ppm to 2.26 ppm, which is close to the expected accuracy of our DFT methodology (2.4 ppm). 11 Some of these discrepancies may reflect severe failings of DFT rather than errors in experimental assignments, however, the final performance of our model supports the use of this conservative strategy. Ultimately, this data-processing pipeline (SI Fig. ) produced a "cleaned" dataset containing around 5,000 structures and 50,000 experimental C chemical shifts, which we refer to as Exp5K. We then used transfer learning (TL) with the Exp5K dataset to retrain DFTNN. With TL, a pre-trained network model can be improved by learning from a new, higher accuracy dataset even when data is sparsely available. The optimizable parameters in our GNN model can be categorized into two groups: updating We compared the above approach against training a model whose parameters are randomly initialized (i.e., from scratch). Fig. illustrates the efficiency of TL in the present work, and also highlights the fact that the performance of ExpNN-dft is superior to the DFTNN model and DFT computations, even though the experimental training set is relatively sparse. The success of this approach arises from the strong correlation between DFT chemical shifts and experimental shifts, the molecular structures shared by DFT8K and Exp5K, and the strategy of freezing 94% of GNN hyperparameters during TL.