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

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 structur

SUBMITTER: Anker AS 

PROVIDER: S-EPMC10840315 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature