Green Closed-Loop AI-Driven Discovery of Sustainable MOFs for Environmental Remediation

While Duan et al. (2025, arXiv) and Desai et al. (2023, Adv. Funct. Mater.) discuss AI and green synthesis separately, this idea proposes an integrated pipeline: use generative AI (e.g., diffusion models, LLMs as in ChatMOF) to propose new MOF candidates, but crucially, embed green chemistry rules and life cycle assessment (LCA) metrics into the generation and evaluation steps. The pipeline would automatically downselect only those MOFs whose precursors, synthesis methods, and predicted performance meet strict sustainability criteria (renewable feedstocks, minimal waste, recyclability, etc.). This creates a feedback loop where environmental impact is not an afterthought, but a core design criterion at every stage, pushing beyond the "performance-first" mentality typical in current literature. The result could be a new generation of MOFs optimized not just for function, but for true sustainability in real-world applications.

References:

1. ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models. Y. Kang, Jihan Kim (2024). Nature Communications.
2. The Rise of Generative AI for Metal-Organic Framework Design and Synthesis. Chenru Duan, Aditya Nandy, Shyam Chand Pal, Xin Yang, Wenhao Gao, Yuanqi Du, Hendrik Krass, Yeonghun Kang, Varinia Bernales, Zuyang Ye, Tristan Pyle, Ray Yang, Zeqi Gu, Philipp Schwaller, Shengqian Ma, Shijing Sun, Alán Aspuru-Guzik, S. M. Moosavi, Robert B. Wexler, Zhiling Zheng (2025). arXiv.org.
3. Green Synthesis of Reticular Materials. Aamod V. Desai, E. Lizundia, Andrea Laybourn, Daniel N. Rainer, A. R. Armstrong, Russell E. Morris, Stefan Wuttke, Romy Ettlinger (2023). Advanced Functional Materials.