  Machine learning force fields (MLFFs) are an increasingly popular choice for
atomistic simulations due to their high fidelity and improvable nature. Here,
we propose a hybrid small-cell approach that combines attributes of both
offline and active learning to systematically expand a quantum mechanical (QM)
database while constructing MLFFs with increasing model complexity. Our MLFFs
employ the moment tensor potential formalism. During this process, we
quantitatively assessed structural properties, elastic properties, dimer
potential energies, melting temperatures, phase stability, point defect
formation energies, point defect migration energies, free surface energies, and
generalized stacking fault (GSF) energies of Zr as predicted by our MLFFs.
Unsurprisingly, model complexity has a positive correlation with prediction
accuracy. We also find that the MLFFs wee able to predict the properties of
out-of-sample configurations without directly including these specific
configurations in the training dataset. Additionally, we generated 100 MLFFs of
high complexity (1513 parameters each) that reached different local optima
during training. Their predictions cluster around the benchmark DFT values, but
subtle physical features such as the location of local minima on the GSFE
surface are washed out by statistical noise.
