  When creating training data for machine-learned interatomic potentials
(MLIPs), it is common to create initial structures and evolve them using
molecular dynamics to sample a larger configuration space. We benchmark two
other modalities of evolving structures, contour exploration and dimer-method
searches against molecular dynamics for their ability to produce diverse and
robust training density functional theory data sets for MLIPs. We also discuss
the generation of initial structures which are either from known structures or
from random structures in detail to further formalize the structure-sourcing
processes in the future. The polymorph-rich zirconium-oxygen composition space
is used as a rigorous benchmark system for comparing the performance of MLIPs
trained on structures generated from these structural evolution methods. Using
Behler-Parrinello neural networks as our machine-learned interatomic potential
models, we find that contour exploration and the dimer-method searches are
generally superior to molecular dynamics in terms of spatial descriptor
diversity and statistical accuracy.
