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

Author:

Anker Andy S.ORCID,Kjær Emil T. S.,Juelsholt MikkelORCID,Jensen Kirsten M. Ø.ORCID

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.

Funder

H2020 European Research Council

Villum Fonden

Vetenskapsrådet

VINNOVA

Svenska Forskningsrådet Formas

Publisher

International Union of Crystallography (IUCr)

Subject

General Biochemistry, Genetics and Molecular Biology

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