Quantum mechanics (QM) calculations are a largely training set independent collection of methods that can be used to predict chemical shifts. Their performance in predicting specifically 19 F NMR chemical shifts has been increasing steadily. Several density functional theory (DFT) methods have shown a favorable balance of speed and accuracy, especially after a correction by linear regression. In this work, we will further formalize our usage of ML methods to predict 19 F NMR chemical shift and then extend it with QM methodology. To that end we will first describe the limits of the ML method's applicability domain. Those compounds that are outside this will then be split off and handled orthogonally by QM. This will involve a more physical description of the molecules that may synergize with the purely empirical ML workflow to increase overall performance, but more importantly to prevent strong outliers.