As done usually in NN-training for DFT functionals, we use a post-SCF approach for the n-LMF (see above). SCF-based training is done rarely so far, in spite of its possible advantages for training dynamics and regularization, due to computational challenges, in particular for larger datasets (we note recent efforts for the DM21 functional regarding SCFloss to mitigate such issues). As transferability of a post-SCF training is of course a possible issue, we have performed SCF calculations with the post-SCF n-LMF parameters every 400 epochs to monitor the effects of selfconsistency. Ultimately, the n-LMF and functional exhibiting the best performance in SCF calculations was selected for final analysis, to ensure good applicability in practice. That is, we selected the lowest MAE of the SCF solutions and avoided further iterations that increase complexity without improving accuracy in a routine workflow.