Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning

Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning

Abstract Characterization of material structure with X-ray or neutron scattering using e.g. Pair Distribution Function (PDF) analysis most often rely on refining a structure model against an experimental dataset. However, identifying a suitable model is often a bottleneck. Recently, automated approa...

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Bibliografiska uppgifter
Huvudupphovsmän: Andy S. Anker, Emil T. S. Kjær, Mikkel Juelsholt, Troels Lindahl Christiansen, Susanne Linn Skjærvø, Mads Ry Vogel Jørgensen, Innokenty Kantor, Daniel Risskov Sørensen, Simon J. L. Billinge, Raghavendra Selvan, Kirsten M. Ø. Jensen
Materialtyp: Artikel
Språk:English
Publicerad: Nature Portfolio 2022-10-01
Serie:npj Computational Materials
Länkar:https://doi.org/10.1038/s41524-022-00896-3

Internet

https://doi.org/10.1038/s41524-022-00896-3

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