a dataset of about 400 random disordered atomic configurations, generated as in Sec. \[meth:sampling:randomdisorder\]. Next, we begin the AL procedure. Using the current ML potential, we simulate many MD trajectories, each initialized to a random disordered configuration. During these simulations the temperature is varied to accelerate sampling of new configurations (Sec. \[meth:activelearn:tempschedule\]). As these simulations run, an uncertainty quantification metric [@Seung1992QueryCommittee; @Smith2018] is used to determine whether the model is operating as expected. If ML uncertainty exceeds a threshold, the MD trajectory is terminated and the final atomic configuration is placed on a queue for DFT calculation and addition to the training dataset. Periodically, the ML model is retrained to the updated training dataset. This AL loop is iterated until the cost of the MD simulations becomes prohibitively expensive. Specifically, we terminate the procedure when typical MD trajectories reach about [${250}\,\mathrm{ps}$]{} (about $2.5\times 10^5$ timesteps) without uncovering any weaknesses in the ML model. The final active learned dataset contains 6,352 DFT calculations, each containing 55 to 249 atoms, and having varying levels of disorder.

We emphasize that this active learning procedure is fully automated, and receives essentially no human guidance. For example, each MD simulation is initialized to a random 