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SasAgent: multi‐agent artificial intelligence system for small‐angle scattering data analysis.

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Title: SasAgent: multi‐agent artificial intelligence system for small‐angle scattering data analysis.
Authors: Ding, Lijie1 (AUTHOR) dingl1@ornl.gov, Do, Changwoo1 (AUTHOR) doc1@ornl.gov
Source: Journal of Applied Crystallography. Apr2026, Vol. 59 Issue 2, p578-585. 8p.
Subjects: Small-angle scattering, Data analysis, Automation, Language models, Multiagent systems, Artificial intelligence
Abstract: We introduce SasAgent, a multi‐agent artificial intelligence (AI) system powered by large language models (LLMs) that automates small‐angle scattering (SAS) data analysis by using tools from the SasView software and enables user interaction via text input. SasAgent features a coordinator agent that interprets user prompts and delegates tasks to three specialized agents for scattering length density (SLD) calculation, synthetic data generation and experimental data fitting. These agents utilize LLM‐friendly tools to execute tasks efficiently. These tools, including the model data tool, retrieval augmented generation documentation tool, BumpsFitting tool and SLD calculator tool, are derived from the SasView Python library. A Gradio‐based interface enhances user accessibility. Through diverse examples, we demonstrate SasAgent's ability to interpret complex prompts, calculate SLDs, generate accurate scattering data and fit experimental datasets using the SasView fitting engine. This work showcases the potential of LLM‐driven AI systems to streamline scientific workflows and enhance automation in SAS research. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Applied Crystallography is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 192849660
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: SasAgent: multi‐agent artificial intelligence system for small‐angle scattering data analysis.
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  Data: <searchLink fieldCode="AR" term="%22Ding%2C+Lijie%22">Ding, Lijie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> dingl1@ornl.gov</i><br /><searchLink fieldCode="AR" term="%22Do%2C+Changwoo%22">Do, Changwoo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> doc1@ornl.gov</i>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Applied+Crystallography%22">Journal of Applied Crystallography</searchLink>. Apr2026, Vol. 59 Issue 2, p578-585. 8p.
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  Data: <searchLink fieldCode="DE" term="%22Small-angle+scattering%22">Small-angle scattering</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Multiagent+systems%22">Multiagent systems</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: We introduce SasAgent, a multi‐agent artificial intelligence (AI) system powered by large language models (LLMs) that automates small‐angle scattering (SAS) data analysis by using tools from the SasView software and enables user interaction via text input. SasAgent features a coordinator agent that interprets user prompts and delegates tasks to three specialized agents for scattering length density (SLD) calculation, synthetic data generation and experimental data fitting. These agents utilize LLM‐friendly tools to execute tasks efficiently. These tools, including the model data tool, retrieval augmented generation documentation tool, BumpsFitting tool and SLD calculator tool, are derived from the SasView Python library. A Gradio‐based interface enhances user accessibility. Through diverse examples, we demonstrate SasAgent's ability to interpret complex prompts, calculate SLDs, generate accurate scattering data and fit experimental datasets using the SasView fitting engine. This work showcases the potential of LLM‐driven AI systems to streamline scientific workflows and enhance automation in SAS research. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Applied Crystallography is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1107/S1600576726001299
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      – Code: eng
        Text: English
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        PageCount: 8
        StartPage: 578
    Subjects:
      – SubjectFull: Small-angle scattering
        Type: general
      – SubjectFull: Data analysis
        Type: general
      – SubjectFull: Automation
        Type: general
      – SubjectFull: Language models
        Type: general
      – SubjectFull: Multiagent systems
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
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      – TitleFull: SasAgent: multi‐agent artificial intelligence system for small‐angle scattering data analysis.
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            NameFull: Ding, Lijie
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            NameFull: Do, Changwoo
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              M: 04
              Text: Apr2026
              Type: published
              Y: 2026
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