  The universal mathematical form of machine-learning potentials (MLPs) shifts
the core of development of interatomic potentials to collecting proper training
data. Ideally, the training set should encompass diverse local atomic
environments but the conventional approach is prone to sampling similar
configurations repeatedly, mainly due to the Boltzmann statistics. As such,
practitioners handpick a large pool of distinct configurations manually,
stretching the development period significantly. Herein, we suggest a novel
sampling method optimized for gathering diverse yet relevant configurations
semi-automatically. This is achieved by applying the metadynamics with the
descriptor for the local atomic environment as a collective variable. As a
result, the simulation is automatically steered toward unvisited local
environment space such that each atom experiences diverse chemical environments
without redundancy. We apply the proposed metadynamics sampling to H:Pt(111),
GeTe, and Si systems. Throughout the examples, a small number of metadynamics
trajectories can provide reference structures necessary for training
high-fidelity MLPs. By proposing a semi-automatic sampling method tuned for
MLPs, the present work paves the way to wider applications of MLPs to many
challenging applications.
