LLM Agents Accelerate MOF Design for Gas Separation

A new framework called LEMO Agent, powered by large language model agents, has been introduced to significantly speed up the inverse design process for Metal-Organic Frameworks (MOFs). MOFs are crucial for gas separation, but their vast design possibilities make finding optimal structures challenging. LEMO Agent addresses this by coupling language-based candidate generation with rigorous validation, property prediction using Transformer models, and a structured design memory. The system operates through iterative cycles of generating, validating, evaluating, and remembering, using feedback to guide its search for chemically valid and high-performing MOFs across various linker, metal, and topology choices. Evaluations on methane/nitrogen and carbon dioxide/nitrogen separation tasks show that LEMO Agent enriches the pool of high-performing candidates, improves predicted separation performance, and maintains broad chemical and topological diversity compared to other methods. Selected candidates have even undergone initial wet-lab synthesis and characterization, demonstrating the practical potential of this AI-driven discovery engine.