Synthesis of covalent organic frameworks for photocatalytic hydrogen peroxide production guided by large language models.

Abstract

The photosynthetic production of hydrogen peroxide (H₂O₂) from water and oxygen presents a sustainable alternative to the energy-intensive anthraquinone process. Covalent organic frameworks (COFs) have emerged as promising photocatalysts for H₂O₂ generation. However, most existing COF photocatalysts yield H₂O₂ at concentrations too low for practical applications, largely due to ongoing challenges in simultaneously optimizing photocatalytic activity and structural stability. Here, we introduce a large language model-driven design strategy for the targeted synthesis of high-performance COF photocatalysts. By analyzing a curated corpus of 355 peer-reviewed articles on COF-based photocatalysis with a language model-driven knowledge extraction pipeline, we extract and structure over 11,000 chemical relationships related to building block identity, linkage robustness, and H₂O₂ yield. Guided by this artificial intelligence-derived knowledge base, we identify 4,4',4″-(1,3,5-triazine-2,4,6-triyl)trianiline and benzo[1,2-b:3,4-b':5,6-b″]trithiophene-2,5,8-tricarbaldehyde as optimal building blocks and thiazole as the preferred linkage motif for constructing a robust, photocatalytic COF. The resulting Thz-COF achieve a high H₂O₂ concentration of 82.3 mM (~0.28 wt%) in aqueous solution (without using sacrificial agents), with a solar-to-chemical energy conversion efficiency of 1.39%.