The quality of input data is critical for data driven science. Detailed, high-level (i..e, quantum many-body theory based) simulations, although expensive, can provide immensely valuable data on which other methods can build, if three main issues can be addressed: First, the system size of accurate quantummechanical simulations is often restricted by the computational complexity of the underlying simulation algorithms. Second, the accuracy of the predicted data for new complex materials critically depends on the accuracy of the specific physical model chosen to derive quantum-mechanical simulation data, limiting subsequent data-driven models. Third, the number of atomic-scale configurations that must be covered for a statistically sound description grows dramatically with the complexity of a material, necessitating more and faster high-level calculations to provide input data for subsequent, AI-driven research. Simulations of real-world materials require addressing all three points at the same time.