Generating candidates in global optimization algorithms using complementary energy landscapes

Small dust grains are abundant in the interstellar medium. Their properties can be modelled using low-energy atomistic configurations, which can be obtained from global optimization algorithms.

In this paper, we propose a new method that accelerates the global optimization, using an artificial energy landscape in which atoms experience fewer energy barriers. We show that our method improves the overall performance of the global optimization algorithm, and we identify a new global energy minimum structure for the olivine (Mg₂SiO₄)₄ cluster.