ArticleActa crystallographica. Section A, Foundations and advances2020
Cluster-mining: an approach for determining core structures of metallic nanoparticles from atomic pair distribution function data.
Article in Acta crystallographica. Section A, Foundations and advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Tracking Out-Of-Equilibrium Growth of Bimetallic Nanoalloys Using Deep Learning and X-ray Scattering.ACS nano · 2026Article
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- Positive Thinking: Countercation Effects in Colloidal Syntheses of Gold Nanoparticles.Nano letters · 2025Article
- Structure of a DNA-Stabilized AgAngewandte Chemie (International ed. in English) · 2025Article
- Monitoring the Structural Changes in Iridium Nanoparticles during Oxygen Evolution Electrocatalysis withJournal of the American Chemical Society · 2024Article
- MLstructureMining: a machine learning tool for structure identification from X-ray pair distribution functions.Digital discovery · 2024Article
- ClusterFinder: a fast tool to find cluster structures from pair distribution function data.Acta crystallographica. Section A, Foundations and advances · 2024Article
- Article
- Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry.Chemical science · 2023Review
- Towards scanning nanostructure X-ray microscopy.Journal of applied crystallography · 2023Article
- DeepStruc: towards structure solution from pair distribution function data using deep generative models.Digital discovery · 2023Article
- Fatty acid capped, metal oxo clusters as the smallest conceivable nanocrystal prototypes.Chemical science · 2023Article
- Structural Analysis of Molecular Materials Using the Pair Distribution Function.Chemical reviews · 2022Review
- Surfactant-free syntheses and pair distribution function analysis of osmium nanoparticles.Beilstein journal of nanotechnology · 2022Article
- Advances in the electron diffraction characterization of atomic clusters and nanoparticles.Nanoscale advances · 2021Review
- There's no place like real-space: elucidating size-dependent atomic structure of nanomaterials using pair distribution function analysis.Nanoscale advances · 2020Review
- sasPDF: pair distribution function analysis of nanoparticle assemblies from small-angle scattering data.Journal of applied crystallography · 2020Article
- Structure-mining: screening structure models by automated fitting to the atomic pair distribution function over large numbers of models.Acta crystallographica. Section A, Foundations and advances · 2020Article
- Structure analysis of supported disordered molybdenum oxides using pair distribution function analysis and automated cluster modelling.Journal of applied crystallography · 2020Article
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9 authors.
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Abstract
A novel approach for finding and evaluating structural models of small metallic nanoparticles is presented. Rather than fitting a single model with many degrees of freedom, libraries of clusters from multiple structural motifs are built algorithmically and individually refined against experimental pair distribution functions. Each cluster fit is highly constrained. The approach, called cluster-mining, returns all candidate structure models that are consistent with the data as measured by a goodness of fit. It is highly automated, easy to use, and yields models that are more physically realistic and result in better agreement to the data than models based on cubic close-packed crystallographic cores, often reported in the literature for metallic nanoparticles.
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