orders of magnitude faster than well optimized ML potentials, whereas reference QM calculations are typically many orders of magnitude slower. As with classical potentials, one can use ML potentials to perform large-scale molecular dynamics simulations by employing spatial domain decomposition. Scalability is partly limited by the fact that, for typical ML codes, upwards of 1,000 atoms should be assigned to each GPU for greatest efficiency.

Building a transferable ML potential requires a large set of QM calculations [@Yao2017ThePhysics]. Prior work shows that ML potentials can be stunningly accurate over a wide swath of chemical and phase space [@Smith2017; @Yao2017ThePhysics; @Kobayashi2017NeuralAlloys; @Sifain2018; @Zhang2019ActiveSimulation; @Zhang2019DP-GEN:Models], but finding sufficiently diverse and reliable training data, to achieve true transferability, remains a challenge. For example, one could readily capture a limited range of physics (e.g. some hand-selected phases in equilibrium) by training to just that specific type of data. However, such success may be misleading, in that the model may fail to generalize beyond the scope of training data. More complex and challenging datasets will push ML models closer to capturing the true range of both equilibrium and *non-equilibrium* physics for a given material.

Active learning (AL) is an iterative dataset construction approach. New data 