Our guiding principles for developing performance metrics are directly geared to measurable observables and using them in the ML optimization are readily transferable to multiple other applications where tensor interactions matter, e.g. dielectric interactions, atomic forces, stress and strain, or chemical shielding. By explicitly demonstrating its effectiveness over scalar learning, and providing a database suitable for benchmarking, we thus aim to present this work as a general guideline for further tensorial learning methods.