Supplementary Material

The supplementary material contains details on the network architecture and explains how to handle symmetries during training and inference, the training dataset, protocols and metrics and additional results obtained with different hyperparameters for training. It also contains a qualitative analysis of the relation between high-weight generated configurations and the difference in energy and positions between source and generated configurations.