Synthesis of Highly Crystalline Covalent Organic Frameworks Using Large Language Models

Crystallizing covalent organic frameworks (COFs) remain a central challenge in reticular chemistry, as achieving long-range order typically requires extensive trial-and-error optimization over many months or years. Here, we demonstrate that by integrating a deep research agent within ChatGPT, this process can be markedly accelerated, reducing the crystallization timeline to less than one month. Our approach, termed the LLM For Accelerated Synthesis Technique (LFAST), operates through two interlinked cycles. In the first, we formulated a structured, multistep prompt to guide the deep research agent in mining, correlating, and validating synthesis parameters from the relevant chemical literature. This yielded an expanded and refined design space for reaction condition screening. In the second, these conditions were executed by using an automated synthesis platform coupled with high-throughput powder X-ray diffraction analysis. Using a widely reported β-ketoenamine-linked COF, TpPa-SO3H, as a benchmark, LFAST produced frameworks with diffraction peaks corresponding to a 350% increase in crystallinity index (CI) relative to prior reports. The same protocol enabled the synthesis of an unreported β-ketoenamine-linked COF-2000 with an even higher structural order. To ensure reproducibility and data accessibility, we further introduce a standardized metadata format encompassing synthesis and PXRD data sets. This data-driven methodology transforms the way that COFs are crystallized and significantly accelerates the pace of materials discovery.