OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!
If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.
Requested Article:
High-throughput screening of CO2 cycloaddition MOF catalyst with an explainable machine learning model
Xuefeng Bai, Yi Li, Ya-Bo Xie, et al.
Green Energy & Environment (2024)
Open Access | Times Cited: 7
Xuefeng Bai, Yi Li, Ya-Bo Xie, et al.
Green Energy & Environment (2024)
Open Access | Times Cited: 7
Showing 7 citing articles:
From Characterization to Discovery: Artificial Intelligence, Machine Learning and High-Throughput Experiments for Heterogeneous Catalyst Design
Jorge Benavides-Hernández, Franck Dumeignil
ACS Catalysis (2024) Vol. 14, Iss. 15, pp. 11749-11779
Closed Access | Times Cited: 24
Jorge Benavides-Hernández, Franck Dumeignil
ACS Catalysis (2024) Vol. 14, Iss. 15, pp. 11749-11779
Closed Access | Times Cited: 24
An atomically economical and environmentally benign approach for the scalable synthesis of rare‐earth metal‐organic framework catalysts
Xin Zhang, Xuefeng Bai, Yi Li, et al.
AIChE Journal (2025)
Closed Access
Xin Zhang, Xuefeng Bai, Yi Li, et al.
AIChE Journal (2025)
Closed Access
Emerging MOF-based antibiotic detection methods in water environments: recent advances, challenges, and prospects
Kexin Zhao, Xiaomei Li, Cuizhu Sun, et al.
Water Cycle (2025)
Open Access
Kexin Zhao, Xiaomei Li, Cuizhu Sun, et al.
Water Cycle (2025)
Open Access
Machine Learning-Assisted Screening of Transition Metal-Doped TMDs for Binding Energy and Charge Transfer Prediction
Pengfei Jia, Qingbin Zeng, Mingxiang Wang, et al.
Materials Today Communications (2025), pp. 112603-112603
Closed Access
Pengfei Jia, Qingbin Zeng, Mingxiang Wang, et al.
Materials Today Communications (2025), pp. 112603-112603
Closed Access
Enhancing arsenate removal through interpretable machine learning guiding the modular design of metal–organic frameworks
Zuhong Lin, Hui Cai, Hongjia Peng, et al.
Chemical Engineering Journal (2024) Vol. 497, pp. 155058-155058
Closed Access | Times Cited: 3
Zuhong Lin, Hui Cai, Hongjia Peng, et al.
Chemical Engineering Journal (2024) Vol. 497, pp. 155058-155058
Closed Access | Times Cited: 3
Machine learning of metal-organic framework design for carbon dioxide capture and utilization
Yang Jeong Park, Sungroh Yoon, Sung Eun Jerng
Journal of CO2 Utilization (2024) Vol. 89, pp. 102941-102941
Open Access | Times Cited: 2
Yang Jeong Park, Sungroh Yoon, Sung Eun Jerng
Journal of CO2 Utilization (2024) Vol. 89, pp. 102941-102941
Open Access | Times Cited: 2
A resorcin[4]arene-based MOF as a Lewis acid catalyst for the CO2 coupling reaction
Duo Wang, Xue-Lu Bai, Jia-Chang Lu, et al.
Inorganic Chemistry Communications (2024), pp. 113341-113341
Closed Access
Duo Wang, Xue-Lu Bai, Jia-Chang Lu, et al.
Inorganic Chemistry Communications (2024), pp. 113341-113341
Closed Access
