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AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org.

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Agentic AI systems using scientific tools significantly improve prediction accuracy for materials science tasks. AGAPI platform demonstrates indispensable tool integration for accurate predictions where data is unavailable.

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Area of Science:

  • Materials Science
  • Artificial Intelligence
  • Computational Chemistry

Background:

  • Agentic AI systems are increasingly connected to external scientific tools.
  • The impact of tool access on prediction accuracy for these systems is not well understood.
  • Existing methods lack comprehensive evaluation of agentic AI in scientific applications.

Purpose of the Study:

  • To present AGAPI (AtomGPT.org API), an open-access platform integrating large language models (LLMs) with scientific tools.
  • To evaluate the impact of tool integration on the prediction accuracy of agentic AI systems.
  • To demonstrate autonomous multi-step workflows for scientific discovery.

Main Methods:

  • Integration of eight open-source LLMs with 18 REST endpoints (28 agent tools, 50 web apps).
  • Utilized materials databases, force fields, tight-binding band structures, X-ray diffraction, and protein structure tools.
  • Employed a three-evaluation residual decomposition on JARVIS-Leaderboard electronic-structure test sets.

Main Results:

  • AGAPI reproduces JARVIS-DFT entries for bulk modulus and bandgap to numerical precision, attributing degradation to functional bias, not agent malfunction.
  • Tool-augmented mean absolute error (MAE) on memorization-resistant test sets was below 0.005 eV, compared to 1.25 to 1.86 eV without tools.
  • Demonstrated autonomous multi-step workflows, including 10-operation defect-engineering pipelines.

Conclusions:

  • Tool access is indispensable for accurate predictions in agentic AI systems when parametric knowledge is unavailable.
  • AGAPI platform effectively separates agent pipeline fidelity from inherited functional bias.
  • The study confirms the critical role of integrated tools in advancing scientific discovery through agentic AI.