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Machine Learning-Assisted Analysis of Small Angle X-ray Scattering
RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.ORCID iD: 0000-0001-7877-2121
RISE Research Institutes of Sweden, Bioeconomy and Health, Material and Surface Design.ORCID iD: 0000-0002-9663-7705
RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.ORCID iD: 0000-0001-7879-4371
RISE Research Institutes of Sweden, Bioeconomy and Health, Material and Surface Design.ORCID iD: 0000-0001-5148-8390
2021 (English)In: 2021 Swedish Workshop on Data Science (SweDS), 2021Conference paper, Published paper (Refereed)
Abstract [en]

Small angle X-ray scattering (SAXS) is extensively used in materials science as a way of examining nanostructures. The analysis of experimental SAXS data involves mapping a rather simple data format to a vast amount of structural models. Despite various scientific computing tools to assist the model selection, the activity heavily relies on the SAXS analysts’ experience, which is recognized as an efficiency bottleneck by the community. To cope with this decision-making problem, we develop and evaluate the open-source, Machine Learning-based tool SCAN (SCattering Ai aNalysis) to provide recommendations on model selection. SCAN exploits multiple machine learning algorithms and uses models and a simulation tool implemented in the SasView package for generating a well defined set of datasets. Our evaluation shows that SCAN delivers an overall accuracy of 95%-97%. The XGBoost Classifier has been identified as the most accurate method with a good balance between accuracy and training time. With eleven predefined structural models for common nanostructures and an easy draw-drop function to expand the number and types training models, SCAN can accelerate the SAXS data analysis workflow.

Place, publisher, year, edition, pages
2021.
Keywords [en]
Training, Analytical models, Adaptation models, X-ray scattering, Computational modeling, Scattering, Training data, SAXS, scientific computing, classification, Random Forest, XGBoost
National Category
Physical Chemistry
Identifiers
URN: urn:nbn:se:ri:diva-57437DOI: 10.1109/SweDS53855.2021.9638297OAI: oai:DiVA.org:ri-57437DiVA, id: diva2:1623491
Conference
2021 Swedish Workshop on Data Science (SweDS). 2-3 Dec. 2021
Available from: 2021-12-29 Created: 2021-12-29 Last updated: 2024-01-10Bibliographically approved

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Tomaszewski, PiotrYu, ShunBorg, MarkusRönnols, Jerk

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