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…

• KA
  King Abdulaziz City for Science and Technology