A key property of molecular systems is that they are equivariant with respect to Euclidean symmetry. Intuitively, what this means is that when a molecule is translated, rotated, or reflected in 3D space, it behaves as an equivalent molecule. The symmetries are thus extended to properties of the molecule. For the sake of neural network training efficiency, it is advantageous to use an architecture that can understand and exploit these symmetries. As such, we use the e3nn machine learning framework, which is structured as a graph convolutional neural network that has equivariance in three dimensions built in. Because e3nn understands that a rotated form of a molecule is the same molecule, it learns much faster than a model that does not account for this symmetry. In fact, it has been shown that e3nn can reduce the amount of training data needed by a factor of 1000 compared to models without built-in equivariance. For the task of learning electron densities represented in an auxiliary basis, equivariance is an essential property. Each C iklm coefficient corresponds to a different spherical harmonic function of degree l and order m. This means that the coefficients for all functions with l > 0 will change under rotation or reflection of the molecule. Equivariance is required to perform this transformation correctly. The e3nn framework implements equivariance by representing learned features in the hidden layers of the network as combinations of irreducible representations of 3D space. These features can themselves be interpreted as spherical harmonics. The networks trained in this work use learned features in the hidden layers up to l max = 3.