Globally-stable and metastable crystal structure enumeration using polynomial machine learning potentials in elemental As, Bi, Ga, In, La, P, Sb, Sn, and Te

Abstract: Machine learning potentials (MLPs) have become indispensable for conducting accurate large-scale atomistic simulations and for the efficient prediction of crystal structures. Polynomial MLPs, defined by polynomial rotational invariants, have been systematically developed for a wide range of elemental, alloy, and ionic systems. This study introduces a highly efficient and robust methodology for enumerating globally stable and metastable structures utilizing polynomial MLPs. To develop MLPs that are sufficiently robust for structure enumeration, an iterative process involving random structure searches and subsequent updates to the MLPs is employed. This methodology has been systematically applied to elemental systems such as As, Bi, Ga, In, La, P, Sb, Sn, and Te, where numerous local minima present significant competition with the global minimum in terms of energy. The proposed approach facilitates robust global structure searches and structure enumerations by markedly accelerating the search process and expanding the search space.