Citation: Yonghong Tao, Yulin Wen, Kanghui Xiong, Qingji Wang, Jia Ren, Xianjun Lang, Xu Li. Accelerated discovery of descriptors of covalent organic frameworks as photocatalysts via synergistic high-throughput DFT/TD-DFT and machine learning[J]. Chinese Chemical Letters, ;2026, 37(12): 113094. doi: 10.1016/j.cclet.2026.113094 shu

Accelerated discovery of descriptors of covalent organic frameworks as photocatalysts via synergistic high-throughput DFT/TD-DFT and machine learning

Yonghong Tao ^b ,
Yulin Wen ^a ,
Kanghui Xiong ^c ,
Qingji Wang ^{b, *,} ,
Jia Ren ^{b, *,} ,
Xianjun Lang ^{c, *,} ,
Xu Li ^{a, *,}

a.
School of Chemistry and Chemical Engineering, Key Laboratory of Electrochemical Energy Storage and Energy Conversion of Hainan Province, Hainan Normal University, Haikou 571158, China
b.
College of Information and Communication Engineering, Hainan University, Haikou 570228, China
c.
Hubei Key Lab on Organic and Polymeric Optoelectronic Materials, College of Chemistry and Molecular Sciences, Wuhan University, Wuhan 430072, China

wangqingji@hainanu.edu.cn

jiaren@hainanu.edu.cn

xianjunlang@whu.edu.cn

lixu@hainnu.edu.cn

Received Date: 20 January 2026
Revised Date: 29 May 2026
Accepted Date: 17 June 2026
Available Online: 17 June 2026

Figures(5)

Covalent organic frameworks (COFs), with tunable electronic structures, are versatile photocatalysts for sustainable environment and renewable energy. A key challenge remains in the screening of photocatalysts from a vast design space of COFs. Herein, a data-driven strategy that integrates high-throughput density functional theory (DFT)/time-dependent DFT (TD-DFT) calculations with machine learning (ML) accelerates the development of COFs as photocatalysts. Amongst 22 quantum chemical descriptors, the crucial ones, including E_HOMO, E_LUMO, IP, EA, and λ_max, are accurately predicted. Among the eight ML models evaluated, the AdaBoost model achieves the highest predictive accuracy, with an R² value up to 0.99 for λ_max. SHAP analysis further pinpoints E_gap is critical to linking the electronic structure to multiple descriptors. A framework is formulated for high-performance COF photocatalysts: A narrow E_gap for broad light absorption, appropriately aligned energy levels to drive redox reactions, and efficient charge separation and transfer facilitated by asymmetric donor-π-acceptor architecture and extended π-conjugation. This work establishes a strategy for the accelerated discovery of COF photocatalysts through rational molecular design, high-throughput screening, and data-driven analysis.
- Machine learning,
- Density functional theory,
- Covalent organic frameworks,
- Photocatalyst,
- Chemical descriptors
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