This research demonstrates how large language models can be integrated with evolutionary computation to automatically discover optimization algorithms for photonic structure design.

• Introduces a specialized LLaMEA framework with structured prompts for photonic applications
• Successfully optimizes complex photonic systems including Bragg mirrors and solar cell coatings
• Establishes a novel engineering approach that combines AI language models with evolutionary computation
• Demonstrates practical applications in improving optical performance metrics

This research represents a significant advancement in engineering design automation, potentially reducing development time for optical systems while discovering novel, high-performing solutions that human engineers might overlook.

Optimizing Photonic Structures with Large Language Model Driven Algorithm Discovery