  Machine learning potential enables molecular dynamics simulations of systems
beyond the capability of traditional force fields. One challenge in developing
machine learning potential is how to construct a data set with low sample
redundancy. This work investigates the method to optimize the training data set
while maintaining the desirable accuracy of the machine learning potential
using the structural similarity algorithm. We construct several subsets ranging
from 200-1500 sample configurations by selecting representative configurations
from a 6183-sample data set using the farthest point sampling method and
examine the ability of the machine learning potential trained from the subsets
to predict energy, atomic forces and structural properties of Li-Si systems.
The simulation results show that the potential developed from 400
configurations can be as accurate as the one developed from the 6183-sample
data set. In addition, our computation results highlight that the
structure-comparison algorithms can not only effectively remove redundant from
training sets, but also achieve an appropriate distribution of samples in
training data sets.
