Exploring Metal Cluster Catalysts Using Swarm Intelligence: Start with Hydrogen Adsorption

The catalytic function of metal nanoclusters has attracted much attention because of their specific activity and selectivity. The structures of metal clusters are very diverse, especially when adsorbates are adsorbed on them. This is an obstacle when approaching metal nanocluster catalysts with computational chemistry. In this manuscript, a prescription for this problem is presented. With metal nanoclusters catalyzing reactions involving hydrogen in mind, a comprehensive, systematic, and efficient search for stable structures of metal nanoclusters with an adsorbed hydrogen atom is presented. This can be achieved through a good use of a supercomputer while using the particle swarm optimization algorithm and density functional theory together. In this attempt, three metallic elements, Fe, Ni, and Cu, are selected. When clustered, what kind of structure these elements form and how their affinity for hydrogen changes are detailed. Eventually, a path is presented to explore clusters that are actually useful as catalysts, using surface calculations as a reference.

Automated summary

The manuscript presents a comprehensive and efficient method for systematically searching for stable structures of metal nanoclusters catalyzing reactions involving hydrogen. By utilizing a supercomputer, the particle swarm optimization algorithm, and density functional theory, the study focuses on metallic elements Fe, Ni, and Cu to explore their structures and hydrogen affinity changes. The goal is to identify clusters that are useful as catalysts through surface calculations as a reference.