Kavli Affiliate: Kristin A. Persson
| First 5 Authors: Theo Jaffrelot Inizan, Sherry Yang, Aaron Kaplan, Yen-hsu Lin, Jian Yin
| Summary:
Generative models and machine learning promise accelerated material discovery
in MOFs for CO2 capture and water harvesting but face significant challenges
navigating vast chemical spaces while ensuring synthetizability. Here, we
present MOFGen, a system of Agentic AI comprising interconnected agents: a
large language model that proposes novel MOF compositions, a diffusion model
that generates crystal structures, quantum mechanical agents that optimize and
filter candidates, and synthetic-feasibility agents guided by expert rules and
machine learning. Trained on all experimentally reported MOFs and computational
databases, MOFGen generated hundreds of thousands of novel MOF structures and
synthesizable organic linkers. Our methodology was validated through
high-throughput experiments and the successful synthesis of five "AI-dreamt"
MOFs, representing a major step toward automated synthesizable material
discovery.
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