For the sake of neural network training efficiency, it is advantageous to use a machine learning architecture that understands and exploits properties of symmetry such as equivariance. We use the e3nn machine learning framework, which employs a graph convolutional neural network that has equivariance in three dimensions built in . The e3nn framework implements equivariance by representing learned features in the hidden layers of the network as combinations of irreducible representations of 3D space. 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 . The network we use for this study is built on the gate points 2101 model, which can be found in the e3nn model library .