Synthesis of Highly Crystalline Covalent Organic Frameworks Using Large Language Models
Kaiyu Wang1,2,3, Daehyun Daniel Ahn1,2,3, Nakul Rampal1,2,3
1Department of Chemistry, University of California, Berkeley, California 94720, United States.
Journal of the American Chemical Society
|February 23, 2026
Summary
Accelerating covalent organic framework (COF) crystallization is now possible using an AI-driven approach. This method, the LLM For Accelerated Synthesis Technique (LFAST), reduces synthesis time from years to under a month.
Area of Science:
- Materials Science
- Chemistry
- Artificial Intelligence
Background:
- Crystallizing covalent organic frameworks (COFs) is crucial for reticular chemistry but often requires extensive optimization.
- Achieving long-range order in COFs typically involves lengthy trial-and-error processes spanning months or years.
Purpose of the Study:
- To significantly accelerate the crystallization process of covalent organic frameworks.
- To reduce the time required for COF synthesis and improve structural order.
Main Methods:
- Integration of a deep research agent within ChatGPT to form the LLM For Accelerated Synthesis Technique (LFAST).
- Utilizing a structured, multistep prompt to mine and validate synthesis parameters from chemical literature.
- Employing an automated synthesis platform with high-throughput powder X-ray diffraction (PXRD) for condition execution and analysis.
Main Results:
- Reduced COF crystallization timeline to less than one month.
- Achieved a 350% increase in crystallinity index (CI) for the benchmark COF, TpPa-SO3H.
- Successfully synthesized an unreported COF-2000 with enhanced structural order.
Conclusions:
- The LFAST methodology dramatically accelerates COF crystallization and improves material quality.
- Introduced a standardized metadata format for enhanced reproducibility and data accessibility.
- This data-driven approach transforms COF synthesis and speeds up materials discovery.
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