In this formulation, Y train represents the training labels (i.e., the DFT reference chemical shieldings), ω denotes the expansion coefficients and λ is the regularization parameter that controls the trade-off between fitting the training data accurately and keeping the model complexity low to avoid overfitting. To optimize the regularization parameter λ , a grid search was conducted over a range of values from 10 -7 to 10 -2 . The model with λ = 5.5 • 10 -6 yields the lowest error. Technical details about the training are described in Section S3 in ESI †. The final KRR model, trained on the database introduced in Section 2.5, achieved a training MAE of 0.2 ppm and a testing MAE of 0.5 ppm, indicating the achievement of the quantitative prediction accuracy. For comparison, this performance surpasses the testing MAE of over 1 ppm reported previously by Chaker et al. for aluminosilicate glasses.