Interpretable Inverse Design of Metal-Organic Frameworks with Large Language Model Agents
Researchers at KAIST developed a system using LLM agents for the interpretable inverse design of Metal-Organic Frameworks. By employing a two-agent closed-loop system to generate and translate chemical hypotheses, the method enables efficient, low-cost discovery of new materials while explaining the scientific logic behind successful designs.