Agent-based multimodal information extraction for nanomaterials
Abstract Automating structured data extraction from scientific literature is a critical challenge with broad implications across domains. We introduce nanoMINER, a multi-agent system combining large language models and multimodal analysis to extract essential information from scientific research art...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-06-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01674-7 |
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| _version_ | 1850108818010144768 |
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| author | R. Odobesku K. Romanova S. Mirzaeva O. Zagorulko R. Sim R. Khakimullin J. Razlivina A. Dmitrenko V. Vinogradov |
| author_facet | R. Odobesku K. Romanova S. Mirzaeva O. Zagorulko R. Sim R. Khakimullin J. Razlivina A. Dmitrenko V. Vinogradov |
| author_sort | R. Odobesku |
| collection | DOAJ |
| description | Abstract Automating structured data extraction from scientific literature is a critical challenge with broad implications across domains. We introduce nanoMINER, a multi-agent system combining large language models and multimodal analysis to extract essential information from scientific research articles on nanomaterials. This system processes documents end-to-end, utilizing tools such as YOLO for visual data extraction and GPT-4o for linking textual and visual information. At its core, the ReAct agent orchestrates specialized agents to ensure comprehensive data extraction. We demonstrate the efficacy of the system by automating the assembly of nanomaterial and nanozyme datasets previously manually curated by domain experts. NanoMINER achieves high precision in extracting nanomaterial properties like chemical formulas, crystal systems, and surface characteristics. For nanozymes, we obtain near-perfect precision (0.98) for kinetic parameters and essential features such as Cmin and Cmax. To benchmark the system performance, we also compare nanoMINER to several baseline LLMs, including the most recent multimodal GPT-4.1, and show consistently higher extraction precision and recall. Our approach is extensible to other domains of materials science and fields like biomedicine, advancing data-driven research methodologies and automated knowledge extraction. |
| format | Article |
| id | doaj-art-bfd235ff404a482f97c081f52c5db5b8 |
| institution | OA Journals |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-bfd235ff404a482f97c081f52c5db5b82025-08-20T02:38:14ZengNature Portfolionpj Computational Materials2057-39602025-06-0111111110.1038/s41524-025-01674-7Agent-based multimodal information extraction for nanomaterialsR. Odobesku0K. Romanova1S. Mirzaeva2O. Zagorulko3R. Sim4R. Khakimullin5J. Razlivina6A. Dmitrenko7V. Vinogradov8AI Talent Hub, ITMO UniversityAI Talent Hub, ITMO UniversityMoscow State UniversityAI Talent Hub, ITMO UniversityAI Talent Hub, ITMO UniversityAI Talent Hub, ITMO UniversityCenter for AI in Chemistry, ITMO UniversityCenter for AI in Chemistry, ITMO UniversityCenter for AI in Chemistry, ITMO UniversityAbstract Automating structured data extraction from scientific literature is a critical challenge with broad implications across domains. We introduce nanoMINER, a multi-agent system combining large language models and multimodal analysis to extract essential information from scientific research articles on nanomaterials. This system processes documents end-to-end, utilizing tools such as YOLO for visual data extraction and GPT-4o for linking textual and visual information. At its core, the ReAct agent orchestrates specialized agents to ensure comprehensive data extraction. We demonstrate the efficacy of the system by automating the assembly of nanomaterial and nanozyme datasets previously manually curated by domain experts. NanoMINER achieves high precision in extracting nanomaterial properties like chemical formulas, crystal systems, and surface characteristics. For nanozymes, we obtain near-perfect precision (0.98) for kinetic parameters and essential features such as Cmin and Cmax. To benchmark the system performance, we also compare nanoMINER to several baseline LLMs, including the most recent multimodal GPT-4.1, and show consistently higher extraction precision and recall. Our approach is extensible to other domains of materials science and fields like biomedicine, advancing data-driven research methodologies and automated knowledge extraction.https://doi.org/10.1038/s41524-025-01674-7 |
| spellingShingle | R. Odobesku K. Romanova S. Mirzaeva O. Zagorulko R. Sim R. Khakimullin J. Razlivina A. Dmitrenko V. Vinogradov Agent-based multimodal information extraction for nanomaterials npj Computational Materials |
| title | Agent-based multimodal information extraction for nanomaterials |
| title_full | Agent-based multimodal information extraction for nanomaterials |
| title_fullStr | Agent-based multimodal information extraction for nanomaterials |
| title_full_unstemmed | Agent-based multimodal information extraction for nanomaterials |
| title_short | Agent-based multimodal information extraction for nanomaterials |
| title_sort | agent based multimodal information extraction for nanomaterials |
| url | https://doi.org/10.1038/s41524-025-01674-7 |
| work_keys_str_mv | AT rodobesku agentbasedmultimodalinformationextractionfornanomaterials AT kromanova agentbasedmultimodalinformationextractionfornanomaterials AT smirzaeva agentbasedmultimodalinformationextractionfornanomaterials AT ozagorulko agentbasedmultimodalinformationextractionfornanomaterials AT rsim agentbasedmultimodalinformationextractionfornanomaterials AT rkhakimullin agentbasedmultimodalinformationextractionfornanomaterials AT jrazlivina agentbasedmultimodalinformationextractionfornanomaterials AT admitrenko agentbasedmultimodalinformationextractionfornanomaterials AT vvinogradov agentbasedmultimodalinformationextractionfornanomaterials |