using the method of Ref. . The inset images in the middle panel of Figure \[fig:almeltfreeze\] show the composition of the system before and after melting, and after refreezing. Compositional information was obtained using the Common Neighbor Analysis as implemented in the OVITO visualization software [@Stukowski2010VisualizationTool].

Every [${2.5}\,\mathrm{ps}$]{} along the trajectory we sampled a frame to perform reference DFT calculations. The error between ANI-Al and DFT is generally small. Over the full trajectory, the MAE for energy and mean force magnitude are just [${0.84}\,\mathrm{meV/atom}$]{} and [${4.1}\,\mathrm{meV/{\textup{\AA}}}$]{}, respectively. The MAE for ANI-Al predicted pressure is [${0.36}\,\mathrm{GPa}$]{}. Interestingly, there is a systematic tendency for ANI-Al to overestimate pressure, especially at negative pressures. This could reflect the fact that the majority of ANI-Al training data was sampled mostly at positive pressures (see Fig. S1 in the SI). Another possible source of error is Pulay stress due to incompleteness of our $3 \times 3 \times 3$ $k$-space basis for the DFT calculations.

Figures S4 and S5 of the SI further validate the ANI-Al model over large a range of temperatures and densities.

Importance of active learning
=============================

The success of ANI-Al hinges on the diversity of the active learned dataset. To demonstrate this, we 