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OpenAlex Citation Counts

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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:

Accelerated exploration of efficient ternary solar cells with PTB7:PC71BM:SMPV1 using machine-learning methods
Chaorong Guo, Zhennan Li, Kuo Wang, et al.
Physical Chemistry Chemical Physics (2022) Vol. 24, Iss. 37, pp. 22538-22545
Closed Access | Times Cited: 12

Showing 12 citing articles:

Filter‐Free Narrowband Photomultiplication‐Type Planar Heterojunction Organic Photodetectors
Zijin Zhao, Chunyu Xu, Yao Ma, et al.
Advanced Functional Materials (2022) Vol. 33, Iss. 9
Closed Access | Times Cited: 58
Predicting power conversion efficiency of binary organic solar cells based on Y6 acceptor by machine learning
Qiming Zhao, Yuqing Shan, Chongchen Xiang, et al.
Journal of Energy Chemistry (2023) Vol. 82, pp. 139-147
Closed Access | Times Cited: 27
A machine learning prediction model for quantitative analyzing the influence of non-radiative voltage loss on non-fullerene organic solar cells
Di Huang, Kuo Wang, Zhennan Li, et al.
Chemical Engineering Journal (2023) Vol. 475, pp. 145958-145958
Closed Access | Times Cited: 23
Design of experiments with the support of machine learning for process parameter optimization of all‐small‐molecule organic solar cells
Kuo Wang, Jiaojiao Liang, Zhennan Li, et al.
FlexMat. (2024) Vol. 1, Iss. 3, pp. 234-247
Open Access | Times Cited: 7
Machine learning assisted identification of the matched energy level of materials for high open circuit voltage in binary organic solar cells
Kuo Wang, Chaorong Guo, Zhennan Li, et al.
Molecular Systems Design & Engineering (2023) Vol. 8, Iss. 6, pp. 799-809
Closed Access | Times Cited: 14
Machine learning-assisted structural parameters screening of Sb@C composites for high cycle capacity in sodium-ion battery
Yufei Wang, Haixin Zhou, Kuo Wang, et al.
Journal of Alloys and Compounds (2025), pp. 179424-179424
Closed Access
Machine learning-assisted screening of effective passivation materials for P–I–N type perovskite solar cells
Di Huang, Chaorong Guo, Zhennan Li, et al.
Journal of Materials Chemistry C (2023) Vol. 11, Iss. 28, pp. 9602-9610
Closed Access | Times Cited: 9
Probing the Effect of Photovoltaic Material on Voc in Ternary Polymer Solar Cells with Non-Fullerene Acceptors by Machine Learning
Di Huang, Zhennan Li, Kuo Wang, et al.
Polymers (2023) Vol. 15, Iss. 13, pp. 2954-2954
Open Access | Times Cited: 8
Predicting the multiple parameters of organic acceptors through machine learning using RDkit descriptors: An easy and fast pipeline
Khadijah Mohammedsaleh Katubi, Muhammad Saqib, Tayyaba Mubashir, et al.
International Journal of Quantum Chemistry (2023) Vol. 123, Iss. 23
Open Access | Times Cited: 5
Exploring the impact of fabrication parameters in organic solar cells with PM6:Y6 using machine learning
Xiaojie Zhao, Min Lei, Kuo Wang, et al.
AIP Advances (2024) Vol. 14, Iss. 6
Open Access | Times Cited: 1
A present scenario of the computational approaches for ternary organic solar cells
Oscar Eraso, Daniela Bolaños, Nikolas Echeverri, et al.
Journal of Renewable and Sustainable Energy (2023) Vol. 15, Iss. 6
Open Access | Times Cited: 3
A machine learning model with crude estimation of property strategy for performance prediction of perovskite solar cells based on process optimization
Dan Li, Ernie Che Mid, Shafriza Nisha Basah, et al.
APL Materials (2024) Vol. 12, Iss. 12
Open Access
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