Citation: Haoran Zhang, Yaxin Jin, Peng Kang, Sheng Zhang. The Convergence and Innovative Application of Artificial Intelligence in Scientific Research: A Case Study of Electrocatalytic Carbon Dioxide Reduction in the Context of the Dual-Carbon Strategy[J]. University Chemistry, ;2025, 40(9): 148-155. doi: 10.12461/PKU.DXHX202412099 shu

The Convergence and Innovative Application of Artificial Intelligence in Scientific Research: A Case Study of Electrocatalytic Carbon Dioxide Reduction in the Context of the Dual-Carbon Strategy

Key Laboratory for Green Chemical Technology of Ministry of Education, Collaborative Innovation Centre of Chemical Science and Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China

Corresponding author: Peng Kang, Sheng Zhang,
Received Date: 20 December 2024
Accepted Date: 15 April 2025

Against the backdrop of rapid advancements in artificial intelligence (AI) technology worldwide, universities face significant challenges in addressing contemporary issues by integrating traditional scientific research with AI. This integration aims to broaden the perspectives of engineering graduate students, stimulate innovative thinking, enhance the efficiency of innovative outputs, and cultivate versatile talents for national and societal needs. This paper, using electrocatalytic carbon dioxide reduction (CO2RR) within the framework of carbon neutrality as a case study, underscores the importance of merging scientific research with AI to augment research output and accuracy. It highlights how AI computing facilitates the screening and prediction of high-performance catalysts, deepens the understanding of complex reaction mechanisms, optimizes electrolytes, and aids in experimental design. Furthermore, it promotes interdisciplinary collaboration and serves as a reference for engineering graduate students embarking on experimental research in universities.
- Artificial intelligence,
- Carbon dioxide electrocatalytic reduction,
- Carbon neutrality,
- Catalyst screening,
- Electrolyte optimization,
- Reaction mechanism research,
- Experimental plan design
1. [1]
2. [2]
3. [3]
  Yan, T.; Chen, X.; Lata, K.; Lin, J.; Li, M.; Fan, Q.; Chi, H.; Meyer, J. T.; Zhang, S.; Ma, X. Chem. Rev. 2023, 123(17), 10530.
4. [4]
  Zhang, S.; Fan, Q.; Xia, R.; Meyer, J. T. Acc. Chem. Res. 2023, 53(1), 255.
5. [5]
  Kuang, S.; Su, Y.; Li, M.; Liu, H.; Chuai, H.; Chen, X.; Hensen, J. M. E.; Meyer, J. T.; Zhang, S.; Ma, X. Proc. Natl. Acad. Sci. USA 2023, 120 (4), e2214175120.
6. [6]
  Wang, Z.; Sun, Z.; Yin, H.; Wei, H.; Peng, Z.; Pang, Y. X.; Jia, G.; Zhao, H.; Pang, C. H.; Yin, Z. eScience 2023, 3 (4), 100136.
7. [7]
  Mok, H. D.; Li, H.; Zhang, G.; Lee, C.; Jiang, K.; Back, S. Nat. Commun. 2023, 14, 7303.
8. [8]
  Wu, Z.; Liu, J.; Mu, B.; Xu, X.; Sheng, W.; Tao, W.; Li, Z. Appl. Surf. Sci. 2024, 648, 159027.
9. [9]
  Li, Z.; Wang, S.; Wei, S. C.; Luke, E. A.; Xin, H. L. J. Mater. Chem. A 2017, 5 (46), 24131.
10. [10]
11. [11]
  Peterson, A. A.; Nørskov, J. K. J. Energy Chem. 2012, 3 (2), 251.
12. [12]
  Liu, X.; Xiao, J.; Peng, H.; Hong, X.; Chan, K.; Nørskov, J. K. Nat. Commun. 2017, 8 (1), 15438.
13. [13]
  Hu, H.; Shan, Y.; Zhao, Q.; Wang, J.; Wu, L.; Liu, W. J. Energy Chem. 2024, 98, 374.
14. [14]
  Gao, Y. C.; Yao, N.; Chen, X.; Yu, L.; Zhang, R.; Zhang, Q. J. Am. Chem. Soc. 2023, 145 (43), 23764.
15. [15]
  Liu, H.; Liu, J.; Yang, B. Chinese J. Catal. 2022, 43 (11), 2898.
16. [16]
  Fan, Q.; Xiao, T.; Liu, H.; Yan, T.; Lin, J.; Kuang, S.; Chi, H.; Meyer, J. T.; Zhang, S.; Ma, X. ACS Cent. Sci. 2024, 10 (12), 2331.
17. [17]
  He, Y.; Wang, M.; Ji, H.; Cheng, Q.; Liu, S.; Huan, Y.; Qian, T.; Yan. C. Adv. Funct. Mater. 2024, 35 (3), 2413703.
18. [18]
  Lin, J.; Chi, H.; Liu, J.; Fan, Q.; Yan, T.; Kuang, S.; Wang, H.; Li, M.; Yan, Y.; Zhang, T.; et al. AIChE J. 2024, 70 (5), e18382.
19. [19]
  Dmitrieva, P. A.; Fomkina, S. A.; Tracey, T. C.; Romanenko, A. E.; Ayati, A.; Krivoshapkina, V. P.; Krivoshapkina F. E. J. Mater. Chem. A 2024, 12 (45), 31074.
20. [20]
  Gao, Y.; Yuan, Y.; Huang, S.; Yao, N.; Yu, L.; Chen, Y.; Zhang, Q.; Chen, X. Angew. Chem. Int. Ed. 2024, 64 (4), e202416506.
21. [21]
22. [22]
23. [23]
  Kuang, S.; Xiao, T.; Chi, H.; Liu, J.; Mu, C.; Liu, H.; Wang, S.; Yu, Y.; Meyer, J. T.; Zhang, S.; et al. Angew. Chem. Int. Ed. 2024, 63 (9), e202316772.
1. [1]
  Lei Qin , Kai Guo . Application of Generative Artificial Intelligence in the Simulation of Acid-Base Titration Images. University Chemistry, 2025, 40(9): 11-18. doi: 10.12461/PKU.DXHX202408123
2. [2]
  Meirong Cui , Mo Xie , Jie Chao . Design and Reflections on the Integration of Artificial Intelligence in Physical Chemistry Laboratory Courses. University Chemistry, 2025, 40(5): 291-300. doi: 10.12461/PKU.DXHX202412015
3. [3]
  Cheng-an Tao , Jian Huang , Yujiao Li . Exploring the Application of Artificial Intelligence in University Chemistry Laboratory Instruction. University Chemistry, 2025, 40(9): 5-10. doi: 10.12461/PKU.DXHX202408132
4. [4]
  Yan Zhang , Limin Zhou , Xiaoyan Cao , Mutai Bao . Exploring the Application of Artificial Intelligence in Marine-Themed Integrated Physical Chemistry Experiments. University Chemistry, 2025, 40(9): 118-125. doi: 10.12461/PKU.DXHX202503062
5. [5]
  Yuejiao Wang , Quanxing Mao , Junshuo Cui , Xiaogeng Feng , Xiaohong Chang , Zhenning Lou , Ying Xiong . 颜色识别式人工智能融入混合碱滴定实验的应用. University Chemistry, 2026, 41(1): 107-113. doi: 10.12461/PKU.DXHX202506020
6. [6]
  Jizhu Zhou , Xiao Wei , Kaixue Wang . Deep Integration of Artificial Intelligence and Chemical Research: Applying the Fourth Paradigm and Evolving the Role of Chemists. University Chemistry, 2025, 40(12): 119-125. doi: 10.12461/PKU.DXHX202509003
7. [7]
  Zhonghan Xu , Yuejia Li , Kin Shing Chan . 碳中和新旅程. University Chemistry, 2025, 40(6): 167-171. doi: 10.12461/PKU.DXHX202407075
8. [8]
  Tangsheng Guan , Ruizhi Yang , Pingyue Geng , Yuhong Lin , Shui Hu , Xiaojuan Chen , Houjin Li , Yong Shen . Innovative Digital Experiments for Glutathione-Targeted Anticancer Drugs in Redox Systems. University Chemistry, 2026, 41(1): 144-158. doi: 10.12461/PKU.DXHX202505028
9. [9]
  Qiang Zhang , Yuanbiao Huang , Rong Cao . Imidazolium-Based Materials for CO₂ Electroreduction. Acta Physico-Chimica Sinica, 2024, 40(4): 2306040-0. doi: 10.3866/PKU.WHXB202306040
10. [10]
  Bing WEI , Jianfan ZHANG , Zhe CHEN . Research progress in fine tuning of bimetallic nanocatalysts for electrocatalytic carbon dioxide reduction. Chinese Journal of Inorganic Chemistry, 2025, 41(3): 425-439. doi: 10.11862/CJIC.20240201
11. [11]
  Xiao Ma , Junjie Wang , Xin Chen , Jingcheng Li , Lihong Zhao , Xueping Sun , Shaojuan Cheng , Fang Wang . Exploring Innovative Approaches to Chemistry Instructional Organization Driven by Artificial Intelligence. University Chemistry, 2025, 40(9): 99-106. doi: 10.12461/PKU.DXHX202410085
12. [12]
  Run Yang , Huajie Pang , Huiping Zang , Ruizhong Zhang , Zhicheng Zhang , Xiyan Li , Libing Zhang . Artificial Intelligence-Enabled DNA Computing: Exploring New Frontiers in Bioinformatics. University Chemistry, 2025, 40(9): 107-117. doi: 10.12461/PKU.DXHX202412135
13. [13]
  Wuyi Feng , Di Zhao . Significance and Measures of Integrating Artificial Intelligence Technology into College Chemistry Teaching. University Chemistry, 2025, 40(9): 156-163. doi: 10.12461/PKU.DXHX202502107
14. [14]
  Lingli Wu , Shengbin Lei . Generative AI-Driven Innovative Chemistry Teaching: Current Status and Future Prospects. University Chemistry, 2025, 40(9): 206-219. doi: 10.12461/PKU.DXHX202503069
15. [15]
  Huixin Dong , Zhenlei Zhou , Wenxin Zou , Juan Jin , Xiguang Liu , Yuzhong Niu , Lili Zhu , Hua Jiang . Exploration and Practice of Ideological and Political Education in Inorganic Chemistry Courses with the Assistance of Artificial Intelligence. University Chemistry, 2026, 41(3): 254-261. doi: 10.12461/PKU.DXHX202505003
16. [16]
  Weigang Zhu , Jianfeng Wang , Qiang Qi , Jing Li , Zhicheng Zhang , Xi Yu . Curriculum Development for Cheminformatics and AI-Driven Chemistry Theory toward an Intelligent Era. University Chemistry, 2025, 40(9): 34-42. doi: 10.12461/PKU.DXHX202412002
17. [17]
  Ping Li , Chao Yin . Teaching Exploration and Practical Innovation of General Education Courses in the Context of Artificial Intelligence. University Chemistry, 2024, 39(10): 402-407. doi: 10.12461/PKU.DXHX202403075
18. [18]
  Yifan Liu , Haonan Peng . AI-Assisted New Era in Chemistry: A Review of the Application and Development of Artificial Intelligence in Chemistry. University Chemistry, 2025, 40(7): 189-199. doi: 10.12461/PKU.DXHX202405182
19. [19]
  Yu Fang . AI-Empowered Education: A Case Study of Self-Directed Learning with ChatGPT-4. University Chemistry, 2025, 40(9): 1-4. doi: 10.12461/PKU.DXHX202502013
20. [20]
  Qifeng Zheng , Aimei Gao , Ruirui Zhao . Exploring a New Undergraduate Training Model for Chemistry Majors through “Science-Education-Innovation Integration” in the AI Era. University Chemistry, 2026, 41(2): 161-167. doi: 10.12461/PKU.DXHX202502056

Get Citation

PDF

XML

Metrics

PDF Downloads(4)
Abstract views(839)
HTML views(75)

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142