Digital Reticular Chemistry: How Artificial Intelligence is Redefining Covalent Organic Framework Research
Gokhan Onder Aksu1, Seda Keskin1
1Department of Chemical and Biological Engineering, Koc University, Rumelifeneri Yolu, Sariyer, Istanbul 34450, Turkey.
JACS Au
|July 30, 2026
Summary
Artificial intelligence (AI) is underutilized for covalent organic frameworks (COFs) due to data limitations. Integrating AI can accelerate the discovery and design of these versatile porous materials.
Area of Science:
- Materials Science
- Chemistry
- Computer Science
Background:
- Artificial intelligence (AI) is transforming reticular chemistry, particularly for metal-organic frameworks (MOFs).
- Covalent organic frameworks (COFs) are underrepresented in AI-driven research despite their versatile applications.
- Challenges include limited standardized datasets and complexities in COF synthesis and structure.
Purpose of the Study:
- To assess the current application of AI in covalent organic framework (COF) research.
- To identify challenges hindering broader AI adoption in the COF field.
- To highlight future opportunities for AI integration in COF design and discovery.
Main Methods:
- Literature review of AI applications in COF research.
- Analysis of challenges in COF dataset creation and AI implementation.
- Identification of potential AI-driven solutions for COF synthesis and optimization.
Main Results:
- AI is currently used in various COF applications, but its adoption is limited.
- Key challenges include data scarcity, standardization issues, and synthetic complexities.
- Significant opportunities exist for AI to accelerate COF materials design and discovery.
Conclusions:
- AI holds immense potential to revolutionize covalent organic framework (COF) research.
- Overcoming data and synthesis challenges is crucial for unlocking AI's full capabilities in COFs.
- Future integration of AI will accelerate the discovery, design, and optimization of COFs for diverse applications.
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