This research introduces a novel approach to materials engineering that leverages Large Language Models to precisely control and optimize point defects in MoS2.

• Combines density functional theory (DFT) calculations with LLM capabilities to predict optimal defect configurations
• Enables systematic manipulation of material properties for specific applications
• Demonstrates how AI can accelerate the discovery and development of next-generation materials
• Creates a pathway for tailoring optoelectronic properties in transition metal dichalcogenides

This advancement matters because it significantly reduces the time and resources needed for materials discovery, potentially revolutionizing how we develop materials for high-tech applications like semiconductors, sensors, and energy storage.

Engineering Point Defects in MoS2 for Tailored Material Properties using Large Language Models