2023 Volume 131 Issue 10 Pages 762-766
Machine learning potentials (MLPs) are attracting much attention as powerful tools to accurately and efficiently perform atomistic simulations and crystal structure predictions. In this study, we develop a polynomial MLP for the Al–Cu system applicable to the robust global structure search and metastable structure enumeration. We then apply a combination of a global optimization method and the polynomial MLP to the Al–Cu alloy system. As a result of approximately 1010 times energy computations, the globally-stable and metastable structures are enumerated in the Al–Cu system.