Nice to see something like this, using models to get a sense of the space of problems.

What I'd also like to see though is a process where you optimise data-creation not only for your goal, but for learning, finding locations where the model has a high degree of uncertainty, or simply a high degree of sensitivity, and populating that with experimental blends in order to better train both models.