Large scale structural optimization of trimetallic Cu–Au–Pt clusters up to 147 atoms

Heterogeneous Trimetallic Nanoparticles as Catalysts

The development and application of trimetallic nanoparticles continues to accelerate rapidly as a result of advances in materials design, synthetic control, and reaction characterization. Following the technological successes of multicomponent materials in automotive exhausts and photovoltaics, synergistic effects are now accessible through the careful preparation of multielement particles, presenting exciting opportunities in the field of catalysis. In this review, we explore the methods currently used in the design, synthesis, analysis, and application of trimetallic nanoparticles across both the experimental and computational realms and provide a critical perspective on the emergent field of trimetallic nanocatalysts. Trimetallic nanoparticles are typically supported on high-surface-area metal oxides for catalytic applications, synthesized via preparative conditions that are comparable to those applied for mono- and bimetallic nanoparticles. However, controlled elemental segregation and subsequent characterization remain challenging because of the heterogeneous nature of the systems. The multielement composition exhibits beneficial synergy for important oxidation, dehydrogenation, and hydrogenation reactions; in some cases, this is realized through higher selectivity, while activity improvements are also observed. However, challenges related to identifying and harnessing influential characteristics for maximum productivity remain. Computation provides support for the experimental endeavors, for example in electrocatalysis, and a clear need is identified for the marriage of simulation, with respect to both combinatorial element screening and optimal reaction design, to experiment in order to maximize productivity from this nascent field. Clear challenges remain with respect to identifying, making, and applying trimetallic catalysts efficiently, but the foundations are now visible, and the outlook is strong for this exciting chemical field.

Application of Optimization Algorithms in Clusters

The structural characterization of clusters or nanoparticles is essential to rationalize their size and composition-dependent properties. As experiments alone could not provide complete picture of cluster structures, so independent theoretical investigations are needed to find out a detail description of the geometric arrangement and corresponding properties of the clusters. The potential energy surfaces (PES) are explored to find several minima with an ultimate goal of locating the global minima (GM) for the clusters. Optimization algorithms, such as genetic algorithm (GA), basin hopping method and its variants, self-consistent basin-to-deformed-basin mapping, heuristic algorithm combined with the surface and interior operators (HA-SIO), fast annealing evolutionary algorithm (FAEA), random tunneling algorithm (RTA), and dynamic lattice searching (DLS) have been developed to solve the geometrical isomers in pure elemental clusters. Various model or empirical potentials (EPs) as Lennard-Jones (LJ), Born-Mayer, Gupta, Sutton-Chen, and Murrell-Mottram potentials are used to describe the bonding in different type of clusters. Due to existence of a large number of homotops in nanoalloys, genetic algorithm, basin-hopping algorithm, modified adaptive immune optimization algorithm (AIOA), evolutionary algorithm (EA), kick method and Knowledge Led Master Code (KLMC) are also used. In this review the optimization algorithms, computational techniques and accuracy of results obtained by using these mechanisms for different types of clusters will be discussed.

Theoretical study of the structures of bimetallic Ag-Au and Cu-Au clusters up to 108 atoms

The stable structures of Ag-Au and Cu-Au clusters with 1 : 1, 1 : 3 and 3 : 1 compositions with up to 108 atoms are obtained using a modified adaptive immune optimization algorithm with Gupta potential. The dominant motifs of Ag-Au and Cu-Au clusters are decahedron and icosahedron, respectively. However, in Ag-rich Ag-Au clusters, more icosahedra are found, and in Cu-rich Cu-Au clusters, there exist several decahedral motifs. Four Leary tetrahedral motifs are predicted. Cu_coreAu_shell configurations are predicted in Cu-Au clusters. In Ag-Au clusters, most Ag atoms are on the surface, but partial ones are located in the inner shell, while Au atoms are interconnected in the middle shell.