A flexible and adaptive grid algorithm for global optimization utilizing basin hopping Monte Carlo.

摘要: Global optimization is an active area of research in atomistic simulations, and many algorithms have been proposed to date. A prominent example basin hopping Monte Carlo, which performs a modified Metropolis Carlo search explore the potential energy surface system interest. These simulations can be very demanding due high-dimensional configurational space. The effective space reduced by utilizing grids for atomic positions, but at cost possibly biasing results if fixed are employed. In this paper, we present flexible grid algorithm global that allows us exploit efficiency without simulation outcome. method general applicable heterogeneous systems, such as interfaces between two materials different crystal structures or large clusters supported surfaces. As benchmark case, demonstrate its performance well-known problem Lennard-Jones containing up 100 particles. Despite simplicity model potential, represent challenging test case since minima some “magic” numbers particles exhibit geometries from those with only slightly size.