Improving Quantum-Chemical Prediction of 19F NMR Chemical Shifts via Machine Learning

19F nuclear magnetic resonance (NMR) spectroscopy is widely used for structural elucidation of fluorinated molecules, but reliable prediction of 19F chemical shifts remains challenging because quantum-chemical calculations are computationally demanding and their accuracy can vary across diverse molecular environments. In this work, we develop a machine-learning-assisted framework to improve quantum-chemical prediction of 19F NMR chemical shifts. A data set of 2605 experimental shifts was compiled from the literature, and isotropic shielding constants were calculated using a density functional theory (DFT)/gauge-including atomic orbital (GIAO) calculation protocol. Machine learning was then used to analyze the relationship between calculated shielding values and experimental chemical shifts. The analysis indicates that the data set can be partitioned into operationally defined, structure-associated regimes in which the mapping between calculated shielding and experimental shift differs systematically. By identifying the structural characteristics of these regimes and constructing prediction models separately for each region, the overall predictive accuracy of the quantum-chemical framework is significantly improved. The resulting models achieve mean absolute errors below 4 ppm and show practical promise under the tested benchtop 60 MHz conditions after simple linear calibration. Application to a fluorinated reaction mixture further demonstrates the utility of the approach for assisting spectral interpretation and prioritizing candidate structures. These results show that the main contribution of the present work is not simply applying machine learning to 19F NMR prediction, but using machine learning to diagnose and correct subset-dependent limitations in the shielding-shift relationship within a practical quantum-chemical workflow.