open access publication

Article, 2024

POMFinder: identifying polyoxometallate cluster structures from pair distribution function data using explainable machine learning

Journal of Applied Crystallography, ISSN 0021-8898, 1600-5767, Volume 57, 1, Pages 34-43, 10.1107/s1600576723010014

Contributors

Anker, Andy Sode 0000-0002-7403-6642 [1] Kjær, Emil Thyge Skanning 0000-0002-0298-6016 [1] Juelsholt, Mikkel 0000-0001-6401-8267 [2] Jensen, Kirsten Marie Ørnsbjerg 0000-0003-0291-217X (Corresponding author) [1]

Affiliations

  1. [1] University of Copenhagen
    2. [NORA names: KU University of Copenhagen; University; Denmark; Europe, EU; Nordic; OECD];
  2. [2] University of Oxford
    3. [NORA names: United Kingdom; Europe, Non-EU; OECD]

Abstract

Characterization of a material structure with pair distribution function (PDF) analysis typically involves refining a structure model against an experimental data set, but finding or constructing a suitable atomic model for PDF modelling can be an extremely labour-intensive task, requiring carefully browsing through large numbers of possible models. Presented here is POMFinder, a machine learning (ML) classifier that rapidly screens a database of structures, here polyoxometallate (POM) clusters, to identify candidate structures for PDF data modelling. The approach is shown to identify suitable POMs from experimental data, including in situ data collected with fast acquisition times. This automated approach has significant potential for identifying suitable models for structure refinement to extract quantitative structural parameters in materials chemistry research. POMFinder is open source and user friendly, making it accessible to those without prior ML knowledge. It is also demonstrated that POMFinder offers a promising modelling framework for combined modelling of multiple scattering techniques.

Keywords

ML knowledge, Pair distribution function modeling, acquisition time, analysis, approach, atomic model, automated approach, characterization, chemistry research, cluster structure, clusters, data, data model, data sets, database, database of structures, distribution function, experimental data, experimental data sets, fast acquisition time, framework, function, functional data, knowledge, labor-intensive task, learning, machine, machine learning, material structure, materials, materials chemistry research, model, modeling framework, multiple scattering techniques, pair distribution function, pair distribution function data, pairs, parameters, polyoxometalate, polyoxometalate cluster structure, potential, quantitative structural parameters, refinement, research, scattering techniques, sets, source, structural model, structural parameters, structure, structure refinement, task, technique, time

Funders

  • VINNOVA
  • European Research Council
  • Swedish Research Council
  • Swedish Research Council for Environment Agricultural Sciences and Spatial Planning
  • Helmholtz Association of German Research Centres
  • The Velux Foundations

Data Provider: Digital Science

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