We can roughly distinguish a) approaches that use ML based on KS-DFT data to parameterize cheaper approaches, such as force fields or orbital-free DFT, 33 b) ∆-ML to improve upon DFT accuracy with respect to high-level ab initio data, and c) learning the entire XC functional or aspects of it. This work belongs to the latter category, as we will use an NN to train only one aspect of the XC functional, namely the LMF of a LH. We draw some inspiration from but contrast our work to the recent DM21 functional. DM21 is a complete deep-NN rung 4 functional. It achieves remarkably low delocalization and strong-correlation errors and also is relatively competitive for test sets like the GMTKN55 suite we will examine here. But it is a completely black-box functional that allows almost no analysis or understanding (it also appears to suffer from some numerical instabilities affecting SCF convergence ).