In the field of computational chemistry, locating the energy-lowest configuration of a cluster is of great importance. The geometry optimization and the growth rule of clusters attracted much intrest. In this dissertation, a new method was proposed for solving structural optimization problem of large clusters. The stable structures and growth rule of Al clusters were studied by optimization algorithms. Furthermore, a new algorithm was designed for the optimization of binary-metal clusters. The main works contained in this dissertation include:1. Global optimization of large clusters has been a difficult task, though much effort has been paid and many efficient methods have been proposed. In our works, a rotation operation (RO) was designed to realize the structural transformation from decahedra to icosahedra for the optimization of large clusters. Based on the RO, a development of the previous dynamic lattice searching with constructed core (DLSc), named as DLSc-RO, was presented. With an investigation of the method for the optimization of Lennard-Jones (LJ) clusters, i.e., LJ500, LJ561, LJ600, LJ665-667, LJ670, LJ685, and LJ923, Morse clusters, silver clusters by Gupta potential, and aluminum clusters by NP-B potential, it was found that both the global minima with icosahedral and decahedral motifs can be obtained, and the method was proved to be efficient and universal.2. The growth sequence of aluminum clusters containing up to 310 atoms was studied. The interaction of aluminum atoms is modeled by the NP-B potential fitted by highly accurate electronic structure datum for aluminum clusters and nanoparticles. The putative global minimum structures of Al2-310 clusters are obtained by dynamic lattice searching (DLS) and DLS with constructed cores (DLSc) method. Lower energy structures of Al63 and A164 were found in comparison with the previously reported AI2-65 clusters. In the optimized structures of Al63-310, all clusters were identified as truncated octahedra (TO) except for five decahedral structures at A164, Al72, Al74, Al76, and Al101, four stacking fault face-centered cubic structures at Al91, Al99, Al129, and Al135, and one icosahedron at Al147.The results showed that aluminum clusters adopted TO growth pattern, and the growth was found to be based on six complete TO at Al38, Al79, Al116, Al140, Al201, and Al260.3. The atomic distribution on the surface of truncated octahedral structures in large scale aluminum clusters was studied. Putative global minima for parts of Al270-500 clusters were located by dynamic lattice searching with constructed cores (DLSc) method with the NP-B potential based on embedded-atom model. In the optimized structures, all clusters were identified as truncated octahedra (TO), which were found to be based on the complete TO at Al260, Al314, and Al405. On the other hand, to avoid the influence of edge atoms in these complete TO, an ideal model on the basis of 260- and 405-atom complete TO was designed to further study the detailed growth pattern. Furthermore, it was concluded that the surface growth of TO structures was influenced by factors of edge atoms adjoining two facets and the sizes of (100) and (111) facets.4. A modified adaptive immune optimization algorithm (AIOA) was designed for optimization of Cu-Au and Ag-Au bimetallic clusters with Gupta potential. Compared with homoatom clusters, there are homotopic isomers in bimetallic cluster, so atom exchange operation was presented in the modified AIOA. The efficiency of the algorithm was tested by optimization of CunAu38-n (0≤n≤38). Results showed that all the structures with the putative global minimal energies were successfully located. In the optimization of AgnAu55-n (0≤n≤55) bimetallic clusters, all the structures with the reported minimal energies were obtained, and 36 structures with even lower potential energies were found. On the other hand, with the optimized structures of CunAu55-n, it was shown that all 55-atom Cu-Au bimetallic clusters were Mackay icosahedra except for Au55, which was a face-centered cubic (fcc)-like structure. |