and added to the training dataset whenever the estimated uncertainty of the ML model exceeds a threshold. The ML model is periodically retrained to the new data. About 50 such iterations resulted in our final AL dataset. With this data, we trained models using two distinctly different ML architectures: ANI [@Smith2017] and HIP-NN [@Lubbers2018]. Performance of ANI and HIP-NN was comparable, demonstrating that the key enabler of an ML potential is the quality of the training data. Our ANI-Al potential exhibits state-of-the-art accuracy for elemental Al, as demonstrated by benchmarks both in equilibrium (e.g., properties of the stable FCC and melt phases, and map the coexistence temperature as a function of pressure) and highly out-of-equilibrium (e.g., theoretical energy barriers for the transformation between different crystal structures) conditions.

Building the ANI-A potential {#Methods}
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This section presents details of the automated procedure to build ANI-Al, our general purpose machine learning potential for bulk aluminum.

The ANI machine learning model {#meth:animodel}
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ANI is a neural network architecture for modeling interatomic potentials. Our prior work with ANI has largely focused on modeling clusters of organic molecules [@Smith2019ApproachingLearning]. A variety of ANI potentials are available online in the ASE\_ANI github repository [@2017ASE_ANI]. Here 