  Machine learning potential enables molecular dynamics simulations of systems
beyond the capability of classical force fields. The traditional approach to
develop structural sets for training machine learning potential typically
generate a great number of redundant configurations, which will result in
unnecessary computational costs. This work investigates the possibility of
reducing redundancy in an initial data set containing 6183 configurations for a
Li-Si machine learning potential. Starting from the initial data set, we
constructed a series of subsets ranging from 25 to 1500 configurations by
combining a structural similarity analysis algorithm and the farthest point
sampling method. Results show that the machine learning potential trained from
a data set containing 400 configurations can achieve an accuracy comparable to
the one developed from the initial data set of 6183 configurations in
describing potential energies, atomic forces, and structural properties of
Li-Si systems. In addition, the redundancy reducing approach also demonstrates
advantages over the classic stochastic method for constructing a concise
training data set for Li-Si systems.
