A Global Optimizer for Nanoclusters

摘要

We have developed a method to automatically build the global minimum and other low-energy minima of nanoclusters. This method is implemented in PyAR (https://github.com/anooplab/pyar/tree/development). The global optimization in PyAR involves two parts, generation of several trial geometries and gradient-based local optimization of the trial geometries. The generation of trial geometries uses the Tabu list to store the information of the already used trial geometries and avoids using the similar geometries. In this recursive algorithm, n-sized cluster is built from the geometries of n ? 1 clusters. The overall procedure automatically generates the several unique minimum energy geometries of clusters with size 2 upto n using this evolutionary growth strategy. We have used the above mentioned strategy on some well studied clusters such as Pd, Pt, Au, Al, binary clusters Ru-Pt and Au-Pt, and ternary clustery Ag-Au-Pt. We have analyzed some of the popular parameters used to characterise the clusters, relative energy, singlet-triplet energy difference, binding energy, second order energy difference, and mixing energy and compared with the reported properties.