with completely disordered atomic configurations and automatically discovers physically relevant crystal phases, including Face Centered Cubic (FCC), Hexagonal Close Packed (HCP), and Body Centered Cubic (BCC), and defects. Our core assumptions are limited to the following: (1) The potential should satisfy physical symmetries (e.g., translation, rotation invariance), (2) The potential is local (no interactions beyond [${7}\,\mathrm{{\textup{\AA}}}$]{}), and (3) The AL sampler is loosely restricted in temperature (up to about [${2000}\,\mathrm{K}$]{}) and pressure (up to about [${50}\,\mathrm{GPa}$]{}). By avoiding any further use of human guidance, we aim to enable true materials discovery without *a priori* knowledge of the phases of interest. Scaled to hundreds of nodes on the Sierra supercomputer, each containing four Tesla V100 GPUs, our AL algorithm required about five days to automatically produce ANI-Al, the general potential for aluminum introduced in this work.

The AL algorithm uses the ML model under construction to drive molecular dynamics sampling for its data collection. All MD trajectories begin from a disordered (high temperature) configuration. By applying time-varying pressures and temperatures, this dynamics generates a variety of interesting atomic configurations. In particular, we can observe nucleation into various poly-crystalline structures, and the formation of crystal defects. Iteratively, new QM data is generated 