Can AI agents automate material discovery?
System of Agentic AI for the Discovery of Metal-Organic Frameworks
April 22, 2025
https://arxiv.org/pdf/2504.14110This research presents MOFGen, a multi-agent AI system for discovering new Metal-Organic Frameworks (MOFs). MOFs are complex materials with applications in carbon capture, water harvesting, and more. Traditionally, MOF discovery is slow and laborious. MOFGen accelerates this process using interconnected AI agents.
Key points for LLM-based multi-agent systems:
- Modular agent design: Specialized agents handle tasks like proposing MOF compositions (using LLMs), generating crystal structures (using diffusion models), optimizing structures (using quantum mechanical calculations and MLFFs), and predicting synthesizability (using expert rules and machine learning). This modularity could inspire designs of other complex multi-agent systems.
- LLM for chemical formula generation: An LLM agent called LinkerGen generates chemically valid formulas for organic linkers within MOFs using in-context learning and chain-of-thought prompting. This demonstrates the practical application of LLMs for scientific tasks.
- Human-in-the-loop validation: The system incorporates human expert feedback to validate and refine predictions, especially for synthesizability. This highlights the importance of human oversight in LLM-driven scientific discovery.
- Integration of diverse AI techniques: MOFGen successfully combines LLMs with diffusion models, quantum mechanical calculations, and machine learning predictors, demonstrating the power of integrating diverse AI techniques to solve challenging scientific problems. This offers a valuable example of collaborative multi-agent design.
- Real-world impact: The system successfully predicted and led to the synthesis of five new MOFs, demonstrating the real-world applicability and potential of this approach. This reinforces the importance of focusing multi-agent system development on practical problem-solving.