  We created a computational workflow to analyze the potential energy surface
(PES) of materials using machine-learned interatomic potentials in conjunction
with the minima hopping algorithm. We demonstrate this method by producing a
versatile machine-learned interatomic potential for iron hydride via a neural
network using an iterative training process to explore its energy landscape
under different pressures. To evaluate the accuracy and comprehend the
intricacies of the PES, we conducted comprehensive crystal structure
predictions using our neural network-based potential paired with the minima
hopping approach. The predictions spanned pressures ranging from ambient to 100
GPa. Our results reproduce the experimentally verified global minimum
structures such as \textit{dhcp}, \textit{hcp}, and \textit{fcc}, corroborating
previous findings. Furthermore, our in-depth exploration of the iron hydride
PES at different pressures has revealed complex alterations and stacking faults
in these phases, leading to the identification of several new low-enthalpy
structures. This investigation has not only confirmed the presence of regions
of established FeH configurations but has also highlighted the efficacy of
using data-driven, extensive structure prediction methods to uncover the
multifaceted PES of materials.
