Not yet reviewed by Pith; the record is open.

A reliable uncertainty estimator is a key ingredient in the successful use of machine-learning force fields for predictive calculations. Important considerations are correlation with error, overhead during training and inference, and efficient workflows to systematically improve the force field. However, in the case of neural-network force fields, simple committees are often the only option considered due to their easy implementation. Here we present a generalization of the deep-ensemble design, based on multiheaded neural networks and a heteroscedastic loss, that can efficiently deal with uncertainties in both the energy and the forces. We compare uncertainty metrics based on deep ensembles, committees and bootstrap-aggregation ensembles using data for an ionic liquid and a perovskite surface. We demonstrate an adversarial approach to active learning to efficiently and progressively refine the force fields. That active learning workflow is realistically possible thanks to exceptionally fast training enabled by residual learning and a nonlinear learned optimizer.