Building up a 19 F NMR chemical shift prediction model from the ground up is a difficult task. Public data sets are a good starting point. Alternatively, a different starting point could use the presented QM workflow or a similar one to bootstrap such a model from minimal or no experimental data. While a model trained on QM results would not have the same precision as a model trained on experimental data, it could be a way to start predicting and screening mixtures. The 19 F NMR chemical shifts measured as a consequence could then be fed back into the model starting a continuous improvement cycle. Another option would be to train a model on QM generated data and then fine-tune on available experimental data in a transfer learning workflow .