disordered configuration, and the machine must *discover* the existence of crystal phases.

Section 1.2 of the SI describes the active learning procedure in more detail.

### Randomized atomic configurations {#meth:sampling:randomdisorder}

We employ randomized atomic configurations to collect an initial dataset of DFT calculations, and to initialize all MD simulations for AL sampling. The procedure to randomize a supercell is as follows:

1.  Randomly sample each of the three linear dimensions of the orthorhombic supercell uniformly from the range 10.5 to [${17.0}\,\mathrm{{\textup{\AA}}}$]{}

2.  Randomly select a target atomic density $\rho$ uniformly from the range 1.80 to [${4.05}\,\mathrm{g/cm^3}$]{}.

3.  Iteratively place atoms randomly in the supercell. If the proposed new atom lies within a distance $r_\textrm{min} = {{1.8}\,\mathrm{{\textup{\AA}}}}$ of an existing atom (i.e., roughly the van der Waals radius), that placement is rejected as unphysical. Placement of atoms is repeated until the target density $\rho$ has been reached.

### Nonequilibrium temperature schedule {#meth:activelearn:tempschedule}

To maximize the diversity in active learning sampling, we perform the MD simulations with a Langevin thermostat using a temperature that varies in time according to a randomized schedule.

Starting at time $t=0$, and running until $t=t_\textrm{max} = {{250}\,\mathrm{fs}}$, the applied temperature is, $$\label{eq:Tsch}
      T(t) = T_\textrm{start} + \frac{t}{t_\textrm{max}} 