The UFP is trained using the active learning procedure shown in the workflow in Figure . The initial dataset is an existing set of LPS structures which was used to train a GAP for LPS . The UFP is trained and iteratively improved by adding structures of -and am-LPS to the training set. Structures are drawn from UFP-MD simulations, where the UFP used for each iteration is the most recent UFP obtained during the training workflow. This active learning cycle of training, UFP-MD simulation, and model evaluation is repeated iteratively until convergence of the UFP energy and force errors is achieved. Finally, the converged energy and force errors over a withheld test set are displayed in Figure 1 meV/atom and 109.9 meV/Å, respectively). These are comparable with the corresponding errors for the GAP for LPS . In addition to the iterative training procedure used to create a robust dataset, the hyperparameters specific to the UFP model were also optimized. Details are given in the Supporting Information Table .