ABSTRACT
We present a universal method for the large-scale prediction of the atomic structure of clusters. Our algorithm performs the joint evolutionary search for all clusters in a given area of the compositional space and takes advantage of structural similarities frequently observed in clusters of close compositions. The resulting speedup is up to 50 times compared to current methods. This enables first-principles studies of multicomponent clusters with full coverage of a wide range of compositions. As an example, we report an unprecedented first-principles global optimization of 315 Si nO m clusters with n ≤ 15 and m ≤ 20. The obtained map of Si-O cluster stability shows the existence of both expected (SiO2) n and unexpected (e.g., Si4O18) stable (magic) clusters, which can be important for a variety of applications.
ABSTRACT
The first-principles prediction of stable nanocluster structure is often hampered by the existence of many isomer configurations with energies close to the ground state. This fact attaches additional importance to many-electron effects beyond density functional theory (DFT), because their contributions can change a subtle energy order of competitive structures. To analyze this problem, we consider, as an example, the energetics of silicon nanoclusters passivated by hydrogen Si10H2n (0 ≤ n ≤ 11), where passivation changes the structure from compact to loosely packed and branched. Our calculations performed with DFT, hybrid functionals, and Hartree-Fock methods, as well as by the GW approximation, confirm a considerable sensitivity of isomer energy ordering to many-electron effects.
