is added to the training set whenever an estimate for model uncertainty exceeds a threshold. AL has recently become paramount in the construction of ML potentials [@Reker2015Active-learningDiscovery; @Podryabinkin2017ActivePotentials; @Gastegger2017MachineSpectra; @Gubaev2018MachineLearning; @Bernstein2019DeSurfaces]. Previous work has employed AL to drive nonequilibrium sampling of large datasets through organic chemical space, yielding the highly general ANI-1x potential [@Smith2018LessLearning]. Other recent research by Gubaev et al. [@Gubaev2019AcceleratingPotentials] has explored the use of AL with moment tensor potentials to construct atomistic potentials for materials. Zhang et al. also applied AL to materials using the deep potential model [@Zhang2019ActiveSimulation] for MgAl alloys. AL was used by Deringer, Pickard, and Csányi to build an accurate and general model for elemental Boron [@Deringer2018Data-DrivenBoron]. Although AL is rapidly growing into a standard technique, prior studies have commonly utilized substantial human guidance in selecting the physically relevant search space (e.g., by initializing sampling to the correct crystal structures). With such an approach, one may then question the extent to which the learned models generalize beyond the specific conditions targeted by the AL sampling procedure.

In the present study, we demonstrate that [*it is possible to obtain a broadly accurate potential for aluminum with essentially zero human guidance*]{}. Our AL sampling begins 