for example, is to design a single classical potential that captures transitions between multiple incompatible crystal phases. Over time, the community has identified a need for more flexible functional forms. For example, the embedded atom method (EAM) [@Daw1983SemiempiricalMetals], has lead to generalizations such as modified EAM (MEAM) [@Lee2010TheSimulations] and multistate MEAM [@Baskes2007MultistateMethod]. Such potentials are generally the tool of choice for molecular dynamics simulations of bulk metals but may struggle to generalize beyond the conditions to which they were trained. This shortcoming can be partly attributed to inflexibility of the assumed functional form. In contrast to classical potentials, neural network-based ML potentials may contain $\sim 10^5$ fitting parameters and make almost no assumptions beyond physical symmetries and spatial locality.

Such flexibility potentially allows ML potentials to achieve the accuracy and generality of the reference QM calculations, but with much greater speed. Because of spatial locality, the computational cost to calculate energy and forces scales linearly with system size. The Neurochem implementation [@2017ASE_ANI; @Smith2017] of our ANAKIN-ME (ANI) model, running on a single modern GPU (Nvidia RTX 2080 Ti), calculates all forces for a box of 1,000 atoms in about [${20}\,\mathrm{ms}$]{}. Classical potentials such as MEAM might be 1 to 2 