ClusterFinder: a fast tool to find cluster structures from pair distribution function data.

Autor: Anker AS; Department of Chemistry and Nano-Science Center, University of Copenhagen, 2100 Copenhagen Ø, Denmark., Friis-Jensen U; Department of Chemistry and Nano-Science Center, University of Copenhagen, 2100 Copenhagen Ø, Denmark., Johansen FL; Department of Chemistry and Nano-Science Center, University of Copenhagen, 2100 Copenhagen Ø, Denmark., Billinge SJL; Department of Applied Physics and Applied Mathematics Science, Columbia University, New York, NY 10027, USA., Jensen KMØ; Department of Chemistry and Nano-Science Center, University of Copenhagen, 2100 Copenhagen Ø, Denmark.
Jazyk: angličtina
Zdroj: Acta crystallographica. Section A, Foundations and advances [Acta Crystallogr A Found Adv] 2024 Mar 01; Vol. 80 (Pt 2), pp. 213-220. Date of Electronic Publication: 2024 Feb 29.
DOI: 10.1107/S2053273324001116
Abstrakt: A novel automated high-throughput screening approach, ClusterFinder, is reported for finding candidate structures for atomic pair distribution function (PDF) structural refinements. Finding starting models for PDF refinements is notoriously difficult when the PDF originates from nanoclusters or small nanoparticles. The reported ClusterFinder algorithm can screen 10 4 to 10 5 candidate structures from structural databases such as the Inorganic Crystal Structure Database (ICSD) in minutes, using the crystal structures as templates in which it looks for atomic clusters that result in a PDF similar to the target measured PDF. The algorithm returns a rank-ordered list of clusters for further assessment by the user. The algorithm has performed well for simulated and measured PDFs of metal-oxido clusters such as Keggin clusters. This is therefore a powerful approach to finding structural cluster candidates in a modelling campaign for PDFs of nanoparticles and nanoclusters.
(open access.)
Databáze: MEDLINE
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