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

Yuta Tsuji, Yuta Yoshioka, Mikiya Hori, Kazunari Yoshizawa

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)215-227
Number of pages13
JournalTopics in Catalysis
Volume65
Issue number1-4
DOIs
Publication statusPublished - Feb 2022

All Science Journal Classification (ASJC) codes

  • Catalysis
  • Chemistry(all)

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