  We propose a novel active learning scheme for automatically sampling a
minimum number of uncorrelated configurations for fitting the Gaussian
Approximation Potential (GAP). Our active learning scheme consists of an
unsupervised machine learning (ML) scheme coupled to Bayesian optimization
technique that evaluates the GAP model. We apply this scheme to a Hafnium
dioxide (HfO2) dataset generated from a melt-quench ab initio molecular
dynamics (AIMD) protocol. Our results show that the active learning scheme,
with no prior knowledge of the dataset is able to extract a configuration that
reaches the required energy fit tolerance. Further, molecular dynamics (MD)
simulations performed using this active learned GAP model on 6144-atom systems
of amorphous and liquid state elucidate the structural properties of HfO2 with
near ab initio precision and quench rates (i.e. 1.0 K/ps) not accessible via
AIMD. The melt and amorphous x-ray structural factors generated from our
simulation are in good agreement with experiment. Additionally, the calculated
diffusion constants are in good agreement with previous ab initio studies.
