  While machine-learned interatomic potentials have become a mainstay for
modeling materials, designing training sets that lead to robust potentials is
challenging. Automated methods, such as active learning and on-the-fly
learning, construct reliable training sets, but these processes can be
resource-intensive. Current training approaches often use density functional
theory (DFT) calculations that have the same cell size as the simulations that
the potential is explicitly trained to model. Here, we demonstrate an
easy-to-implement small-cell training protocol and use it to model the Zr-H
system. This training leads to a potential that accurately predicts known
stable Zr-H phases and reproduces the $\alpha$-$\beta$ pure zirconium phase
transition in molecular dynamics simulations. Compared to traditional active
learning, small-cell training decreased the training time of the
$\alpha$-$\beta$ zirconium phase transition by approximately 20 times. The
potential describes the phase transition with a degree of accuracy similar to
that of the large-cell training method.
