A Divide-and-Conquer Approach to Nanoparticle Global Optimisation Using Machine Learning.

Global optimization of the structure of atomic nanoparticles is often hampered by the presence of many funnels on the potential energy surface. While broad funnels are readily encountered and easily exploited by the search, narrow funnels are more difficult to locate and explore, presenting a problem if the global minimum is situated in such a funnel. Here, a divide-and-conquer approach is applied to overcome the issue posed by the multifunnel effect using a machine learning approach, without using knowledge of the potential energy surface.

Calculations of the effect of catalyst size and structure on the electrocatalytic reduction of CO on Cu nanoclusters.

The structure and catalytic properties of Cu nanoclusters of sizes between 55 and 147 atoms were examined to understand if small Cu clusters could provide enhancement over traditional catalysts for the electrocatalysis of CO to CO and carbon-based fuels, such as CH and CHOH, compared to bulk Cu surfaces and large Cu nanoparticles. Clusters studied included Cu, Cu, Cu, Cu, and Cu, the structures of which were determined using global optimisation. The majority of Cu clusters examined were icosahedral, including the perfect closed-shell, partial-shell, elongated and distorted icosahedral clusters.

Comparison of Taboo Search Methods for Atomic Cluster Global Optimization with a Basin-Hopping Algorithm.

The basin-hopping algorithm (BHA) allows for the efficient exploration of atomic cluster potential energy surfaces by random perturbations in configuration space, followed by energy minimizations. Here, the taboo search method is incorporated to prevent the search from revisiting recently visited regions of the search space. Two taboo search modes are implemented, one mode resets the search to random coordinates upon encountering the taboo region, while the other simply rejects any proposed move into the taboo region.