In general, the QM assisted ML workflow demonstrates on one hand the difference between purely empirical and more physical models, as well as their synergistic potential. The purely empirical model was more successful at precise predictions in the chemical space that was covered in the training set. The QM workflow was more robust in extrapolation and less dependent on a training set. Knowing how and when to combine the two methodologies can lead to a workflow that benefits from the performance of both.