(T_\textrm{end} - T_\textrm{start}) +  T_\textrm{mod} \sin^2 (\pi t / t_0) \\$$ The first two terms linearly ramp the background temperature. The initial temperature $T_\textrm{start}$ is randomly sampled from the range [${10}\,\mathrm{K}$]{} to [${1000}\,\mathrm{K}$]{}. The final background temperature $T_\textrm{end}$ is randomly sampled from the range [${10}\,\mathrm{K}$]{} to [${600}\,\mathrm{K}$]{}. The third term in Eq.  superimposes temperature oscillations. The modulation scale $T_\textrm{mod}$ is randomly sampled from the range [${0}\,\mathrm{K}$]{} to [${2000}\,\mathrm{K}$]{}. The oscillation period $t_0$ is randomly sampled from the range [${10}\,\mathrm{ps}$]{} to [${50}\,\mathrm{ps}$]{}.

By spawning MD simulations with many different temperature schedules, we hope to observe a wide variety of nonequilibrium processes. Given that each MD simulation begins from a disordered melt configuration, we hope that the nonequilibrium dynamics will automatically produce: (1) nucleation into various crystal structures (in particular, the ground-state FCC crystal), (2) a variety of defect structures and dynamics (dislocation glide, vacancy diffusion, etc.) and (3) rapid quenches into disordered glass phases. Acquiring snapshots from these types of dynamics will be crucial to the diversity of the training dataset and, thus, to the overall generality of the ANI-Al potential.

Accuracy Benchmarks
===================

Here we present a variety of benchmarks for ANI-Al, our machine learned potential for bulk aluminum. As 