A Global Optimization Scheme for Bimetallic Nanoparticles

Abstract

The nature of atomic clusters has increasingly attracted the attention of researchers over the last few decades. One of the primary reasons for the rise in interest is that the physical and chemical properties of atomic metal clusters are different from their corresponding bulk metals. To understand cluster properties, knowledge of the relevant structures are needed. However, structural information is very difficult to acquire from experiments. In this thesis, a theoretical approach is used instead. In particular, a Basin-Hopping global optimization algorithm is implemented where the atomic interactions are calculated from first principles by use of the Density Functional Theory (DFT). The Basin-Hopping code is written with the Python programming language and the Siesta and Dmol softwares are used to run the DFT calculations. Structures of bimetallic structures are the primary focus, as they are known as highly-effective hydrogenation catalysts. In addition, the monometallic cluster structures of Sn and Ru are explored. Sn clusters in a range from 2 to 20 atoms with higher stability than previously reported structures were obtained. The optimized Ru clusters match with previous reported results. The electronic, magnetic, and chemical properties of ground state clusters are discussed. The new method was, furthermore, used to study structures of a range of bimetallic systems including Ru-Sn, Ru-Pd, Pd-Au, and other similar structures.