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.

Machine learning-based interatomic potentials are currently garnering a lot of attention as they strive to achieve the accuracy of electronic structure methods at the computational cost of empirical potentials. Given their generic functional forms, the transferability of these potentials is highly dependent on the quality of the training set, the generation of which can be highly labor-intensive. Good training sets should at once contain a very diverse set of configurations while avoiding redundancies that incur cost without providing benefits. We formalize these requirements in a local entropy-maximization framework and propose an automated sampling scheme to sample from this objective function. We show that this approach generates much more diverse training sets than unbiased sampling and is competitive with hand-crafted training sets.

No abstract