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Extracting structur...
Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning
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- Anker, Andy S. (författare)
- University of Copenhagen
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- Kjær, Emil T.S. (författare)
- University of Copenhagen
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- Juelsholt, Mikkel (författare)
- University of Oxford
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- Christiansen, Troels Lindahl (författare)
- University of Copenhagen
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- Skjærvø, Susanne Linn (författare)
- University of Copenhagen
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- Jørgensen, Mads Ry Vogel (författare)
- Lund University,Lunds universitet,MAX IV-laboratoriet,MAX IV Laboratory,Aarhus University
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- Kantor, Innokenty (författare)
- Lund University,Lunds universitet,MAX IV-laboratoriet,MAX IV Laboratory,Technical University of Denmark
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- Sørensen, Daniel Risskov (författare)
- Lund University,Lunds universitet,MAX IV-laboratoriet,MAX IV Laboratory,Aarhus University
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- Billinge, Simon J.L. (författare)
- Brookhaven National Laboratory,Columbia University
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- Selvan, Raghavendra (författare)
- University of Copenhagen
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- Jensen, Kirsten M.Ø. (författare)
- University of Copenhagen
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(creator_code:org_t)
- 2022-10-01
- 2022
- Engelska.
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Ingår i: npj Computational Materials. - : Springer Science and Business Media LLC. - 2057-3960. ; 8:1
- Relaterad länk:
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http://dx.doi.org/10... (free)
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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 approaches have made it possible to test thousands of models for each dataset, but these methods are computationally expensive and analysing the output, i.e. extracting structural information from the resulting fits in a meaningful way, is challenging. Our Machine Learning based Motif Extractor (ML-MotEx) trains an ML algorithm on thousands of fits, and uses SHAP (SHapley Additive exPlanation) values to identify which model features are important for the fit quality. We use the method for 4 different chemical systems, including disordered nanomaterials and clusters. ML-MotEx opens for a type of modelling where each feature in a model is assigned an importance value for the fit quality based on explainable ML.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
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- Av författaren/redakt...
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Anker, Andy S.
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Kjær, Emil T.S.
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Juelsholt, Mikke ...
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Christiansen, Tr ...
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Skjærvø, Susanne ...
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Jørgensen, Mads ...
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visa fler...
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Kantor, Innokent ...
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Sørensen, Daniel ...
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Billinge, Simon ...
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Selvan, Raghaven ...
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Jensen, Kirsten ...
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- Om ämnet
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- NATURVETENSKAP
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NATURVETENSKAP
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och Data och informa ...
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och Bioinformatik
- Artiklar i publikationen
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npj Computationa ...
- Av lärosätet
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Lunds universitet