Global structure searches under varying temperatures and pressures using polynomial machine learning potentials: A case study on silicon

Polynomial machine learning potentials (MLPs) based on polynomial rotational invariants have been systematically developed for various systems and applied to efficiently predict crystal structures. In this study, we propose a robust methodology founded on polynomial MLPs to comprehensively enumerate