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Nanostructured Material Design via a Retrieval-Augmented Generation (RAG) Approach: Bridging Laboratory Practice and

Nikita A Krotkov1, Dmitrii A Sbytov1, Anna A Chakhoyan1

  • 1Infochemistry Scientific Center, ITMO University, Lomonosova str. 9, 191002 Saint Petersburg, Russia.

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|September 30, 2025
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Summary

This study introduces an agent-based retrieval-augmented generation (RAG) system using large language models (LLMs) to streamline nanostructured materials research. The system enhances information extraction and analysis for two-photon polymerization (2PP) applications, improving experimental planning.

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Area of Science:

  • Materials Science
  • Computational Chemistry
  • Biomedical Engineering

Background:

  • Designing advanced nanostructured materials for electronics, medicine, and energy is complex.
  • Current research relies heavily on extensive literature review, which is time-consuming and costly.
  • Computational methods are needed to improve efficiency and reduce experimental expenses.

Purpose of the Study:

  • To develop an agent-based retrieval-augmented generation (RAG) system integrated with large language models (LLMs).
  • To automate the extraction and analysis of scientific information on nanostructured materials, particularly those fabricated using two-photon polymerization (2PP).
  • To improve the understanding of nanostructured materials' interactions with cells for biomedical applications.

Main Methods:

  • Implementation of an agent-based RAG system powered by LLMs.
  • Focus on literature databases concerning nanostructured materials produced via 2PP.
  • Integration of mechanisms for dynamic query refinement to ensure accuracy.
  • Emphasis on analyzing cell-material interactions for biomedical relevance.

Main Results:

  • The system achieved high semantic accuracy (cosine similarity: 0.82) and task precision (0.81).
  • Demonstrated significant reduction in misinformation through dynamic query refinement.
  • Provided an intuitive interface for rapid access to relevant scientific data.
  • Enhanced researchers' productivity and accuracy in experimental planning.

Conclusions:

  • The developed RAG-LLM system effectively automates scientific information retrieval and analysis for nanostructured materials.
  • The system shows promise for improving research efficiency and accuracy in fields like biomedicine.
  • Further fine-tuning is recommended to address domain-specific terminology and enhance overall reliability.