As discussed in more detail below, we observed good transferability of cluster-based MLPs to systems with PBC. Similar transferability has already been reported for an NNP applied to bulk water, where NNPs trained using both PBC and cluster training data demonstrate similar performance in the prediction of bulk properties, such as radial distribution functions (RDF), self-diffusion coefficients, and equilibrium densities. After the initial MLP training, one structure from the initial training set is selected as the starting point to propagate the molecular dynamics (MD) using the first version of the trained MLP. Several rounds of short MD simulations are then performed to assess the stability of the potential and generate new training structures. The simulation time is set to (n 3 + 2) fs where n corresponds to the index of the MD run, starting from 0. The time step of the dynamics is 0.5 fs. More details can be found in ref. and SI § S6.1. From each MD trajectory, the last frame is evaluated by the selector to determine whether to add or not this structure to the training set. If the structure is not selected, n is incremented, and MD runs are repeated until the maximum simulation time or a maximum number of MD iterations is achieved.