OptiGen: A Physics-Grounded Agentic Framework for Deep-Ultraviolet Laser Diode Design and Optimization

The rapid emergence of agentic AI across scientific domains calls for extending its capabilities to physics-grounded design, where autonomous reasoning must be coupled with physical constraints and optimization. We presented OptiGen, an agentic framework for deep-ultraviolet laser diode (DUV LD) design and optimization. Unlike conventional LLM-based scientific assistants that primarily retrieve and summarize domain knowledge, OptiGen couples simulation-grounded designs with physics-grounded reasoning and constraint-aware optimization within a unified agentic loop. The framework performs query understanding, constraint-aware design retrieval, multi-objective optimization, physics-grounded reasoning, and grounded response generation. We evaluate OptiGen on benchmark queries covering single-objective, multi-objective, constraint-based, and worst-case design tasks using four models, namely o3, GPT-5, GPT-4, and Qwen, to assess the robustness of the framework. The OptiGen (o3) achieves a Precision@5 of 0.588, Recall@5 of 0.600, Mean Reciprocal Rank (MRR) of 0.640, and Normalized Discounted Cumulative Gain (NDCG)@5 of 0.612 for retrieval; best-design accuracy of 0.580 and performance accuracy of 0.659 for optimization; and a physics reasoning score of 0.830, response completeness of 0.884, and hallucination-free rate of 0.709 for generation. It further achieves an intent understanding accuracy of 0.890 and an end-to-end success rate of 0.570 in an average response time of 26.39 s. Across the evaluated models, OptiGen (o3) achieves the strongest overall performance across the major retrieval, optimization, and generation metrics, while OptiGen (Qwen) demonstrates competitive performance on intent understanding and retriever metrics.These results highlight the robustness of OptiGen across different language models demonstrating its potential as an autonomous decision system for specialized optoelectronic engineering problems.