  Accuracy of molecular dynamics simulations depends crucially on the
interatomic potential used to generate forces. The gold standard would be
first-principles quantum mechanics (QM) calculations, but these become
prohibitively expensive at large simulation scales. Machine learning (ML) based
potentials aim for faithful emulation of QM at drastically reduced
computational cost. The accuracy and robustness of an ML potential is primarily
limited by the quality and diversity of the training dataset. Using the
principles of active learning (AL), we present a highly automated approach to
dataset construction. The strategy is to use the ML potential under development
to sample new atomic configurations and, whenever a configuration is reached
for which the ML uncertainty is sufficiently large, collect new QM data. Here,
we seek to push the limits of automation, removing as much expert knowledge
from the AL process as possible. All sampling is performed using MD simulations
starting from an initially disordered configuration, and undergoing
non-equilibrium dynamics as driven by time-varying applied temperatures. We
demonstrate this approach by building an ML potential for aluminum (ANI-Al).
After many AL iterations, ANI-Al teaches itself to predict properties like the
radial distribution function in melt, liquid-solid coexistence curve, and
crystal properties such as defect energies and barriers. To demonstrate
transferability, we perform a 1.3M atom shock simulation, and show that ANI-Al
predictions agree very well with DFT calculations on local atomic environments
sampled from the nonequilibrium dynamics. Interestingly, the configurations
appearing in shock appear to have been well sampled in the AL training dataset,
in a way that we illustrate visually.

Materials Science;Machine Learning;Computational Physics