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

Andy S Anker1, Ulrik Friis-Jensen1, Frederik L Johansen1

  • 1Department of Chemistry and Nano-Science Center, University of Copenhagen, 2100 Copenhagen Ø, Denmark.

Acta Crystallographica. Section A, Foundations and Advances
|February 29, 2024
PubMed
Summary
This summary is machine-generated.

ClusterFinder is a new automated method that rapidly screens many structures to find starting models for atomic pair distribution function (PDF) refinements, especially for nanoparticles. This approach aids in analyzing complex nanocluster structures.

Keywords:
nanoclustersnanomaterialspair distribution function analysisscreening

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Area of Science:

  • Materials Science
  • Crystallography
  • Computational Chemistry

Background:

  • Determining atomic structures for nanoparticles and nanoclusters using pair distribution function (PDF) refinement is challenging due to the difficulty in finding accurate starting models.
  • Existing methods for generating candidate structures are often time-consuming and less efficient for complex nanoscale materials.

Purpose of the Study:

  • To introduce ClusterFinder, a novel automated high-throughput screening approach for identifying candidate structures for PDF refinements.
  • To overcome the limitations of current methods in finding initial models for PDF analysis of nanoclusters and nanoparticles.

Main Methods:

  • ClusterFinder screens large structural databases (e.g., ICSD) using crystal structures as templates.
  • It identifies atomic clusters that generate a PDF signature matching the target measured PDF.
  • The algorithm processes 10^4 to 10^5 candidate structures within minutes.

Main Results:

  • The algorithm successfully generated rank-ordered lists of candidate clusters for user assessment.
  • ClusterFinder demonstrated strong performance with both simulated and measured PDFs of metal-oxido clusters, including Keggin clusters.
  • It significantly accelerates the process of finding structural models for PDF analysis.

Conclusions:

  • ClusterFinder provides a powerful and efficient solution for identifying structural candidates in PDF modeling studies of nanoparticles and nanoclusters.
  • This automated approach enhances the feasibility and speed of structural determination for nanoscale materials.
  • The method is particularly valuable for complex systems where initial model generation is a bottleneck.