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Cited by 17Covalent Organic Framework ApplicationsMachine Learning in Materials ScienceAdvanced Photocatalysis Techniques

Machine Learning‐Assisted Design of Nitrogen‐Rich Covalent Triazine Frameworks Photocatalysts

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Wu, Mingliang, Song, Zhilong, Cui, Yu, Fu, Zhanzhao, Hong, Kunquan, Li, Qiang, Lyu, Zhiyang, Liu, Wei +1 more·2025·Advanced Functional Materials
DOI 10.1002/adfm.202413453
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Abstract

Covalent triazine frameworks (CTFs), noted for their rich nitrogen content, have attracted significant attention as promising photocatalysts. However, the structural complexity introduced by the diversity of nitrogen atoms in nitrogen‐rich CTFs poses a substantial challenge in discovering high‐performance CTFs. To address this challenge, a machine‐learning approach is developed to rationally design nitrogen‐rich CTFs, which is subsequently validated through experimental methods. A framework is employed based on the special orthogonal group in three dimensions (SO(3))‐invariant graph neural networks to predict photocatalytic properties of CTFs structures. This approach achieves exceptionally high accuracies with R 2 scores exceeding 0.98. From a dataset of 14920 CTFs structures, this framework identifies 45 high‐performance candidates. Guided by these predictions, a novel CTF structure, pyridine‐2,5‐dicarbaldehyde (CTF‐DCPD) is selected and successfully synthesized, which exhibits an ultrahigh hydrogen evolution rate of 17.70 mmol g−1 h−1. This rate significantly surpasses that of the widely studied CTF‐1,4‐dicyanobenzene (CTF‐DCB, 10.41 mmol g−1 h−1). This work provides a new paradigm for machine learning to accelerate materials development, which can be generalized to the development of other functional materials.

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  • The selected CTF-BIP photocatalyst was previously identified and optimized through machine-learning screening in earlier studies.

    “The selected biphenyl-based covalent triazine framework (CTF-BIP) photocatalyst was previously identified and optimized through machine-learning screening in our earlier studies. 56,57”

    From Gradient-structured directional porous floatable aerogels for effective solar-driven hydrogen production and steam generation (2025) · cites this paper as [56]

    The Results and Discussion describes machine-learning screening of CTFs and says the database included biphenyl-based building blocks. But it names CTF-DCPD and CTF-DCB—not CTF-BIP—as the materials selected for synthesis, and does not establish that CTF-BIP was identified or optimized through that screening. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • The CTF-BIP photocatalyst exhibits excellent structural stability under visible-light irradiation.

    “It exhibited excellent structural stability under visible-light irradiation, and its molecular design prevents hydrogen release via selfdecomposition, confirming that the observed hydrogen evolution originates from photocatalytic activity rather than catalyst degradation.”

    From Gradient-structured directional porous floatable aerogels for effective solar-driven hydrogen production and steam generation (2025) · cites this paper as [56]

    The Results and Discussion report good photocatalytic stability for CTF-DCPD over 20 hours of irradiation, not structural stability for CTF-BIP. Searches for CTF-BIP, structural stability under visible light, and self-decomposition found no passage addressing the claimed catalyst or its stability. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • The CTF-BIP molecular design prevents hydrogen release via self-decomposition.

    “and its molecular design prevents hydrogen release via selfdecomposition, confirming that the observed hydrogen evolution originates from photocatalytic activity rather than catalyst degradation.”

    From Gradient-structured directional porous floatable aerogels for effective solar-driven hydrogen production and steam generation (2025) · cites this paper as [56]

    The Results and Discussion section reports photocatalytic hydrogen evolution and stability for CTF-DCPD and CTF-DCB, not CTF-BIP; page numbers are unavailable in our copy. Searches for CTF-BIP, self-decomposition, and catalyst degradation found no passage showing that CTF-BIP’s design prevents hydrogen release through decomposition or ruling out degradation as the source of its hydrogen. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Authors
Wu, M., Song, Z., Cui, Y., Fu, Z., Hong, K., Li, Q., Lyu, Z., Liu, W., & Wang, J.
Affiliations
  • Southeast University (CN)
  • Purple Mountain Laboratories (CN)
Corresponding author
  • Lyu, Zhiyang
  • Liu, Wei
  • Wang, Jinlan
ORCID
Venue
Advanced Functional Materials
Publisher
Wiley
Published
2024-08-19
Volume
35
Issue
3
ISSN
  • 1616-301X (ISSN-L)
  • 1616-3028
DOI
10.1002/adfm.202413453
OpenAlex
W4401686058
Semantic Scholar
19d48b8472ff185a39f77a8206ac0cedd30b43af
Cited by
17
References
81
Citations by year
2026: 6 · 2025: 9 · 2024: 2
FWCI
1.65
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
Advanced Functional Materials (publishedVersion)
Funders
  • National Key Research and Development Program of China (2022YFB3807200)
  • National Natural Science Foundation of China (22033002, 92261112, 22109021, T2321002, 22371041)
  • Basic Research Program of Jiangsu Province (BK20220800, BK20222007)
Topics
  • Physical Sciences › Materials Science › Materials Chemistry › Covalent Organic Framework Applications
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Advanced Photocatalysis Techniques
Keywords
covalent triazine frameworks, nitrogen-rich materials, photocatalysts, machine learning for materials design, hydrogen evolution reaction

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