Therefore, DM21's failure to converge for some systems using D suggests that the functional is highly nonsmooth (i.e. nearly discontinuous) for combinations of input features that are 'close' enough to the minima to be sampled during optimization. The presence of (near) discontinuities is not surprising in a machine-learned DFA -the exact density functional is very complicated and the DFA needs to capture that complexity by fitting to training data, so will inherit a bias toward its training data. What is surprising is that even simple systems, like TM atoms, can have combinations of features that are outside the training data. Section IV will therefore explore this point in more detail.