The problem with generating QM data is the large amount of CPU time necessary. QM assisted ML as we have presented it, minimizes the number of QM calculations necessary for good-quality predictions. Integrating QM parameters directly into the model requires the users to perform QM calculations for all predictions they want to make. Decoupling these systems gives the user more freedom. The splitting and re-training time necessary for the presented workflow have negligible runtimes in comparison to the QM workflow and the user has full discretion about which samples to process by QM. The system is intentionally designed in such a way that even if samples cannot be processed by QM for whatever reason the ML system is still able to provide a prediction at baseline performance.