ML2 was trained on eight small closed-shell atoms/ions (H -, He, Be, Mg, Ne, Ar, Ca, and Kr). The architecture of the neural network for ML2 has three hidden layers, each consisting of 16 neurons. They were connected via a hyperbolic tangent activation function, including the output layer, constraining the ML2 weights to a real-space zero-centered mapping. We employed for the optimization either the GES or LES based loss function. The learning rate follows thereby an exponential decay that continues over 5000 epochs including the warm-up phase.