An Agentic Framework for Autonomous Metamaterial Modeling and Inverse Design (2506.06935v1)

Abstract: Recent significant advances in integrating multiple LLM systems have enabled Agentic Frameworks capable of performing complex tasks autonomously, including novel scientific research. We develop and demonstrate such a framework specifically for the inverse design of photonic metamaterials. When queried with a desired optical spectrum, the Agent autonomously proposes and develops a forward deep learning model, accesses external tools via APIs for tasks like simulation and optimization, utilizes memory, and generates a final design via a deep inverse method. The framework's effectiveness is demonstrated in its ability to automate, reason, plan, and adapt. Notably, the Agentic Framework possesses internal reflection and decision flexibility, permitting highly varied and potentially novel outputs.

• The paper introduces an autonomous framework using specialized LLM agents to manage both forward modeling with deep neural networks and inverse design of photonic metamaterials.
• It achieves a validation Mean Squared Error of 1.3×10⁻³ in forward modeling and 2.53×10⁻³ in inverse design, aligning closely with human benchmarks.
• The approach minimizes expert intervention and streamlines complex workflows, potentially accelerating research and broader applications in computational photonics.

An Agentic Framework for Autonomous Metamaterial Modeling and Inverse Design

The paper introduces a sophisticated Agentic Framework designed to facilitate the autonomous modeling and inverse design of photonic metamaterials. This framework leverages advanced capabilities of LLMs integrated through agent-based architecture, aiming to automate a complex workflow typically requiring substantial expertise and manual intervention.

Overview and Methodology

The research demonstrates the utility of Agentic Frameworks—a concept involving the division of tasks into autonomous agents, each equipped with specialized LLMs—to achieve independent automation of metamaterial design processes. This framework is crafted not only to develop accurate forward models using deep neural networks (DNNs) but also to perform inverse design tasks autonomously. The system operates through several specialized agents:

1. Planner: Constructs the overall workflow based on user input, navigating the complex subtasks involved.
2. Input Verifier: Ensures all necessary inputs are valid and accessible.
3. Forward Modeler: Responsible for constructing DNN-based surrogate models. This agent iteratively decides between generating more data and optimizing model architecture to reduce prediction error.
4. Inverse Designer: Utilizes neural adjoint methods to craft metamaterial geometries meeting designated spectral targets.

Experimental Results

The capability of the Agentic system has been validated using an all-dielectric metamaterial (ADM), focusing on both forward modeling and inverse design:

• Forward Modeling: The system demonstrates competitive prediction accuracy compared to human-designed models on fixed datasets. The agent-designed transformer architecture achieved a validation Mean Squared Error (MSE) of $1.3 \times 10^{-3}$. When tasked with reaching a target MSE of $2 \times 10^{-3}$, the agents systematically generated data and optimized model architectures to achieve the desired performance, illustrating robust autonomous decision-making processes.
• Inverse Design: Employing the neural adjoint method, the framework realized resimulation MSE of $2.53 \times 10^{-3}$, effectively aligning with human benchmarks and underscoring the efficacy of the autonomous inverse design approach.

Implications and Future Directions

The proposed Agentic Framework has significant implications for expediting metamaterial research by reducing dependence on expert knowledge and minimizing time-consuming manual processes. The system's ability to autonomously achieve modeling and design tasks marks a notable advancement in computational photonics, potentially setting the foundation for more generalized applications across various material domains.

Future research could explore expanding the framework to encompass additional constraints pertinent to physical realizability and manufacturability, thus enhancing the practical utility of automated design processes. Moreover, improving self-improvement capabilities could refine agent performance over successive iterations, aligning with open-ended evolution approaches to continually elevate outcomes.

Conclusion

The paper contributes a sophisticated agentic architecture, demonstrating autonomous metamaterial modeling and design through advanced DNN and LLM integration. The framework validates its design methodology on benchmark systems, exhibiting notable similarity to human experts in modeling precision and inverse design accuracy. This paper lays groundwork for extended applications in computational science, leveraging automation to streamline and democratize complex design workflows.