A detailed description of e3nn can be found in the literature or online: . A brief, non-technical explanation of the model is given here (Fig. ). The e3nn model is initialized with a structure's atomic coordinates. The coordinates are converted into a three-dimensional graph that gets passed as the input layer to the neural network. Each node in the graph represents an atomic position that marks the center of a gated radial convolution that learns geometric features (such as bonding interactions) about its environment. The learned features are stored as combinations of irreducible representations in the hidden layers of the network. After passing through the hidden layers, the model produces as output the coefficients C iklm that make up the 3D charge density. Because e3nn has equivariance built in, a molecule that is translated, rotated, or reflected in space will produce the same charge density-in fact, a translated, rotated, or reflected form of the original charge density. Prior to training the e3nn model, network hyperparameters were tuned using a 2-mer validation set independent from the training and test sets. The network hyperparameters can be found in the ESI † .