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"DIVE" into hydrogen storage materials discovery with AI agents.

Di Zhang1, Xue Jia1, Hung Ba Tran1

  • 1Advanced Institute for Materials Research (WPI-AIMR), Tohoku University Sendai 980-8577 Japan di.zhang.a8@tohoku.ac.jp shin-ichi.orimo.a6@tohoku.ac.jp li.hao.b8@tohoku.ac.jp.

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Summary

Researchers developed a new AI workflow, Descriptive Interpretation of Visual Expression (DIVE), to extract data from scientific papers. This accelerates the discovery of new energy materials like solid-state hydrogen storage.

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Area of Science:

  • Materials Science
  • Artificial Intelligence
  • Chemistry

Background:

  • Autonomous workflows for AI-driven materials discovery are underdeveloped.
  • Extracting experimental data from scientific literature, especially graphical elements, is challenging.
  • Solid-state hydrogen storage materials are crucial for clean energy technologies.

Purpose of the Study:

  • To develop an AI workflow for extracting and organizing experimental data from scientific literature.
  • To improve the accuracy and coverage of data extraction for energy materials.
  • To establish a rapid inverse-design AI workflow for proposing new materials.

Main Methods:

  • Development of the Descriptive Interpretation of Visual Expression (DIVE) multi-agent workflow.
  • Systematic reading and organization of experimental data from graphical elements in scientific literature.
  • Application to solid-state hydrogen storage materials using a curated database of over 30,000 entries from >4000 publications.

Main Results:

  • DIVE significantly improved data extraction accuracy and coverage for solid-state hydrogen storage materials.
  • DIVE achieved 10-15% gains over commercial models and >30% over open-source models in data extraction.
  • An AI workflow was established, capable of proposing new materials within minutes.

Conclusions:

  • The DIVE workflow offers a scalable pathway for accelerated materials discovery.
  • Multimodal AI agents can convert literature-embedded scientific knowledge into actionable innovation.
  • This end-to-end paradigm advances AI in energy materials research and chemistry.