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MCP-enabled LLM for meta-optics inverse design: leveraging differentiable solver without LLM expertise.

Yi Huang1, Bowen Zheng1, Yunxi Dong1

  • 1Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, USA.

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Summary

We developed a framework using large language models (LLMs) and a Model Context Protocol (MCP) to simplify metasurface inverse design. This approach makes advanced computational tools accessible to researchers without programming expertise, improving design quality and efficiency.

Keywords:
TorchRDITautomatic differentiationinverse designlarge language model (LLM)model context protocol (MCP)

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Area of Science:

  • Optics and Photonics
  • Computational Science
  • Artificial Intelligence

Background:

  • Automatic differentiation (AD) is crucial for metasurface inverse design but demands significant expertise.
  • Current methods require extensive theoretical and programming knowledge, limiting accessibility for many researchers.

Purpose of the Study:

  • To present a Model Context Protocol (MCP) assisted framework enabling inverse design using large language models (LLMs) and differentiable solvers.
  • To simplify access to complex computational tools for researchers lacking programming expertise.

Main Methods:

  • Developed an LLM-assisted framework that dynamically accesses verified code templates and documentation via dedicated servers.
  • The LLM autonomously generates inverse design codes without predefined coordination rules.
  • Evaluated the framework on a Huygens meta-atom design task using the TorchRDIT solver.

Main Results:

  • Both natural language and structured prompting achieved high success rates in inverse design.
  • Structured prompting significantly outperformed natural language in design quality, workflow efficiency, computational cost, and error reduction.
  • A minimalist server design with 5 APIs demonstrated the framework's accessibility and generalizability.

Conclusions:

  • The MCP-assisted framework successfully democratizes metasurface inverse design by integrating LLMs with differentiable solvers.
  • This approach significantly enhances design quality and workflow efficiency while reducing computational costs and errors.
  • The framework offers a generalizable solution for integrating sophisticated computational tools into various scientific tasks.