This makes the use of MRF features highly appealing for ML of XC approximations (or alternatively using ML to improve MRF). However, certain challenges have to be addressed before we can get to get to that stage. First, due to its radically different mathematical structure, one cannot follow standard routes used for improving semilocal functionals to refine MRF. For example, incorporation of the exact constraints is a standard 'Perdew-school' route for refining XC functional approximations . Making a semilocal approximation exact for the uniform electron gas is trivial for semilocal approximations , but a different mathematical structure of MRF makes the imposition of this and other constraints more involved (see e.g., Refs. for in-depth discussions on the relevance of the uniform electron gas constraint for both solids and molecules). Second, despite its affordable scaling (N 3 ), the full nonlocality of MRF and its underlying features require more implementation effort to make it readily available in quantum-chemical codes.