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Updated: Jul 4, 2025
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High Resolution Physical Characterization of Single Metallic Nanoparticles
Published on: June 28, 2019
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POMFinder: identifying polyoxometallate cluster structures from pair distribution function data using explainable
Andy S Anker1, Emil T S Kjær1, Mikkel Juelsholt2
1Department of Chemistry and Nano-Science Center, University of Copenhagen, 2100 Copenhagen Ø, Denmark.
Journal of Applied Crystallography
|February 7, 2024
Summary
POMFinder, a machine learning tool, rapidly identifies suitable polyoxometallate (POM) cluster models for pair distribution function (PDF) analysis. This accelerates materials structure characterization and quantitative parameter extraction.
Area of Science:
- Materials Science
- Computational Chemistry
- Machine Learning
Background:
- Pair distribution function (PDF) analysis is crucial for material structure characterization.
- Identifying suitable atomic models for PDF analysis is a labor-intensive bottleneck.
Purpose of the Study:
- To develop an automated machine learning (ML) approach for rapid screening of candidate structures for PDF analysis.
- To introduce POMFinder, a classifier for identifying polyoxometallate (POM) clusters for PDF data modeling.
Main Methods:
- Utilized a machine learning classifier (POMFinder) to screen a database of POM structures.
- Applied POMFinder to experimental PDF data, including in situ data with fast acquisition.
Main Results:
- POMFinder successfully identified suitable POM candidates from experimental PDF data.
- Demonstrated the tool's effectiveness with fast-acquired in situ data.
- Showcased POMFinder's potential for combined modeling of multiple scattering techniques.
Conclusions:
- POMFinder significantly accelerates the identification of models for PDF analysis.
- The tool enhances the extraction of quantitative structural parameters in materials chemistry.
- POMFinder is open-source, user-friendly, and applicable to researchers without prior ML expertise.
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