Out of the properties that can be predicted, we focus on the electron density. The central problem to be solved by any machine learning electronic structure method is the ability to train on small fragments without losing ac-curacy when evaluating the model on larger structures. Recent work has shown that learning the electron density offers a promising way to solve this conundrum. Moreover, the electron density is a fundamental property from which useful quantities such as energies, forces, and electrostatic potentials can be derived.