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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.

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|February 23, 2026
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Summary
This summary is machine-generated.

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.

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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.