  Machine learning potentials (MLPs) developed from extensive datasets
constructed from density functional theory (DFT) calculations have become
increasingly appealing for many researchers. This paper presents a framework of
polynomial-based MLPs, called polynomial MLPs. The systematic development of
accurate and computationally efficient polynomial MLPs for many elemental and
binary alloy systems and their predictive powers for various properties are
also demonstrated. Consequently, many polynomial MLPs are available in a
repository website. The repository will help many scientists perform accurate
and efficient large-scale atomistic simulations and crystal structure searches.
