DP Training Methods. DPs for DSG were trained using the DeePMD [27 formalism with active learning. The method involved in generating training data and training DPs is shown schematically in Figure . An MD based active learning data exploration, inspired by the deep generator (DP-GEN) , was used to improve the quality of our DPs. Embedding and fitting neural networks (NN) composed these DPs, where just the atomic coordinates were used as input to predict the total energy and atomic forces for a given atomic configurations. The total energy was calculated as the sum of the energy contributions from each atom in the system, which was made possible by using sub-networks for each atom i. The forces were calculated by taking the spatial derivative of the predicted energies. These sub-networks are composed of the embedding and fitting NNs, as mentioned above, to map the local environments (defined within a cut off radius) to an embedded feature space. This embedded feature space (D i ) is designed to preserve the translational, rotational, and permutational symmetries of a given system, which were then mapped to "atomic energies" using a fitting NN. The loss function L(p ϵ , p f ) used to train these DPs was defined as