  As one of the most robust global optimization methods, simulated annealing
has received considerable attention, with many variations that attempt to
improve the cooling schedule. This paper introduces a variant of simulated
annealing that is useful for optimizing atomistic structures, and makes use of
the statistical mechanical properties of the system, determined on the fly
during optimization, to adaptively control the cooling rate. The adaptive
cooling approach is demonstrated to be more computationally efficient than
classical simulated annealing, when applied to Lennard-Jones clusters. This
increase in efficiency is approximately a factor of two for clusters with
25--40 atoms, and improves with the size of the system.
