Determining the optimal structures and clarifying the
corresponding
hierarchical evolution of transition metal clusters are of fundamental
importance for their applications. The global optimization of clusters
containing a large number of atoms, however, is a vastly challenging
task encountered in many fields of physics and chemistry. In this
work, a high-efficiency self-adaptive differential evolution with
neighborhood search (SaNSDE) algorithm, which introduced an optimized
cross-operation and an improved Basin Hopping module, was employed
to search the lowest-energy structures of Co
N
, Pt
N
, and Fe
N
(N = 3–200) clusters. The performance
of the SaNSDE algorithm was first evaluated by comparing our results
with the parallel results collected in the Cambridge Cluster Database
(CCD). Subsequently, different analytical methods were introduced
to investigate the structural and energetic properties of these clusters
systematically, and special attention was paid to elucidating the
structural evolution with cluster size by exploring their overall
shape, atomic arrangement, structural similarity, and growth pattern.
By comparison with those results listed in the CCD, 13 lower-energy
structures of Fe
N
clusters were discovered.
Moreover, our results reveal that the clusters of three metals had
different magic numbers with superior stable structures, most of which
possessed high symmetry. The structural evolution of Co, Pt, and Fe
clusters could be, respectively, considered as predominantly closed-shell
icosahedral, Marks decahedral, and disordered icosahedral-ring growth.
Further, the formation of shell structures was discovered, and the
clusters with hcp-, fcc-, and bcc-like configurations were ascertained.
Nevertheless, the growth of the clusters was not simply atom-to-atom
piling up on a given cluster despite gradual saturation of the coordination
number toward its bulk limit. Our work identifies the general growth
trends for such a wide region of cluster sizes, which would be unbearably
expensive in first-principles calculations, and advances the development
of global optimization algorithms for the structural prediction of
clusters.