Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning
(2022) In npj Computational Materials 8(1).- 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 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... (More)
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.
(Less)
- author
- organization
- publishing date
- 2022-12
- type
- Contribution to journal
- publication status
- published
- subject
- in
- npj Computational Materials
- volume
- 8
- issue
- 1
- article number
- 213
- publisher
- Nature Publishing Group
- external identifiers
-
- scopus:85139230506
- ISSN
- 2057-3960
- DOI
- 10.1038/s41524-022-00896-3
- language
- English
- LU publication?
- yes
- id
- 6de49230-cb4b-45d0-8f38-9167098f74c6
- date added to LUP
- 2022-12-19 11:56:32
- date last changed
- 2022-12-19 11:56:32
@article{6de49230-cb4b-45d0-8f38-9167098f74c6, abstract = {{<p>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.</p>}}, author = {{Anker, Andy S. and Kjær, Emil T.S. and Juelsholt, Mikkel and Christiansen, Troels Lindahl and Skjærvø, Susanne Linn and Jørgensen, Mads Ry Vogel and Kantor, Innokenty and Sørensen, Daniel Risskov and Billinge, Simon J.L. and Selvan, Raghavendra and Jensen, Kirsten M.Ø.}}, issn = {{2057-3960}}, language = {{eng}}, number = {{1}}, publisher = {{Nature Publishing Group}}, series = {{npj Computational Materials}}, title = {{Extracting structural motifs from pair distribution function data of nanostructures using explainable machine learning}}, url = {{http://dx.doi.org/10.1038/s41524-022-00896-3}}, doi = {{10.1038/s41524-022-00896-3}}, volume = {{8}}, year = {{2022}}, }