This research introduces a powerful closed-loop framework that combines Large Language Models with Conditional Generative Adversarial Networks to overcome data limitations in materials design.

• Integrates LLM-assisted text mining with CGANs to enhance data diversity
• Creates a systematic refinement process for alloy candidates
• Demonstrates superior performance in designing materials with tailored properties
• Provides a scalable approach to overcome traditional constraints in materials engineering

This breakthrough matters for engineering because it accelerates the discovery of new materials with custom specifications, potentially revolutionizing manufacturing processes and enabling novel applications across industries.

Inverse Materials Design by Large Language Model-Assisted Generative Framework