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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:

Machine learning analysis on stability of perovskite solar cells
Çağla Odabaşı, Ramazan Yıldırım
Solar Energy Materials and Solar Cells (2019) Vol. 205, pp. 110284-110284
Closed Access | Times Cited: 85

Showing 1-25 of 85 citing articles:

Machine learning and data mining in manufacturing
Alican Doğan, Derya Birant
Expert Systems with Applications (2020) Vol. 166, pp. 114060-114060
Closed Access | Times Cited: 521
Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm
Tanveer Ahmad, Rafał Madoński, Dongdong Zhang, et al.
Renewable and Sustainable Energy Reviews (2022) Vol. 160, pp. 112128-112128
Closed Access | Times Cited: 352
Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage
Daniel Rangel-Martínez, K.D.P. Nigam, Luis Ricardez‐Sandoval
Process Safety and Environmental Protection (2021) Vol. 174, pp. 414-441
Closed Access | Times Cited: 177
Machine learning for advanced energy materials
Liu Yun, Oladapo Christopher Esan, Zhefei Pan, et al.
Energy and AI (2021) Vol. 3, pp. 100049-100049
Open Access | Times Cited: 151
Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects
Yiming Liu, Xinyu Tan, Jie Liang, et al.
Advanced Functional Materials (2023) Vol. 33, Iss. 17
Closed Access | Times Cited: 80
Materials genome engineering accelerates the research and development of organic and perovskite photovoltaics
Ying Shang, Ziyu Xiong, Kang An, et al.
Materials Genome Engineering Advances (2024) Vol. 2, Iss. 1
Open Access | Times Cited: 18
Critical review of machine learning applications in perovskite solar research
Beyza Yılmaz, Ramazan Yıldırım
Nano Energy (2020) Vol. 80, pp. 105546-105546
Closed Access | Times Cited: 98
Machine learning for halide perovskite materials
Lei Zhang, Mu He, Shaofeng Shao
Nano Energy (2020) Vol. 78, pp. 105380-105380
Closed Access | Times Cited: 92
Recent Advances in Solution‐Processable Organic Photodetectors and Applications in Flexible Electronics
Zhaojue Lan, Min‐Hsuan Lee, Furong Zhu
Advanced Intelligent Systems (2021) Vol. 4, Iss. 3
Open Access | Times Cited: 66
Screening for lead-free inorganic double perovskites with suitable band gaps and high stability using combined machine learning and DFT calculation
Zhengyang Gao, Hanwen Zhang, Guangyang Mao, et al.
Applied Surface Science (2021) Vol. 568, pp. 150916-150916
Closed Access | Times Cited: 64
Paths towards high perovskite solar cells stability using machine learning techniques
M. Mammeri, L. Dehimi, H. Bencherif, et al.
Solar Energy (2022) Vol. 249, pp. 651-660
Closed Access | Times Cited: 58
How Machine Learning Predicts and Explains the Performance of Perovskite Solar Cells
Yiming Liu, Wensheng Yan, Shichuang Han, et al.
Solar RRL (2022) Vol. 6, Iss. 6
Closed Access | Times Cited: 51
Machine learning enabled development of unexplored perovskite solar cells with high efficiency
Wensheng Yan, Yiming Liu, Yue Zang, et al.
Nano Energy (2022) Vol. 99, pp. 107394-107394
Closed Access | Times Cited: 48
Topological feature engineering for machine learning based halide perovskite materials design
D. Vijay Anand, Qiang Xu, JunJie Wee, et al.
npj Computational Materials (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 40
Machine Learning in Perovskite Solar Cells: Recent Developments and Future Perspectives
Nitin Bansal, Snehangshu Mishra, Himanshu Dixit, et al.
Energy Technology (2023) Vol. 11, Iss. 12
Open Access | Times Cited: 34
Machine learning for perovskite solar cell design
Hui Zhan, Min Wang, Xiang Yin, et al.
Computational Materials Science (2023) Vol. 226, pp. 112215-112215
Closed Access | Times Cited: 28
Methods, progresses, and opportunities of materials informatics
Chen Li, Kun Zheng
InfoMat (2023) Vol. 5, Iss. 8
Open Access | Times Cited: 28
Halide Perovskites for Photoelectrochemical Water Splitting and CO2 Reduction: Challenges and Opportunities
Krzysztof Bieńkowski, Renata Solarska, Linh Trinh, et al.
ACS Catalysis (2024) Vol. 14, Iss. 9, pp. 6603-6622
Open Access | Times Cited: 13
Application of artificial intelligence in the materials science, with a special focus on fuel cells and electrolyzers
Mariah Batool, Oluwafemi Joseph Sanumi, Jasna Janković
Energy and AI (2024) Vol. 18, pp. 100424-100424
Open Access | Times Cited: 12
Artificial Intelligence-Based, Wavelet-Aided Prediction of Long-Term Outdoor Performance of Perovskite Solar Cells
Ioannis Kouroudis, Kenedy Tabah Tanko, Masoud Karimipour, et al.
ACS Energy Letters (2024) Vol. 9, Iss. 4, pp. 1581-1586
Open Access | Times Cited: 9
Deep learning for augmented process monitoring of scalable perovskite thin-film fabrication
Felix Laufer, Markus Götz, Ulrich W. Paetzold
Energy & Environmental Science (2025)
Open Access | Times Cited: 1
Assessment of critical materials and cell design factors for high performance lithium-sulfur batteries using machine learning
Aysegul Kilic, Çağla Odabaşı, Ramazan Yıldırım, et al.
Chemical Engineering Journal (2020) Vol. 390, pp. 124117-124117
Closed Access | Times Cited: 50
Enhancing the stability of organic photovoltaics through machine learning
Tudur Wyn David, Helder Scapin Anizelli, T. Jesper Jacobsson, et al.
Nano Energy (2020) Vol. 78, pp. 105342-105342
Open Access | Times Cited: 50
Is machine learning redefining the perovskite solar cells?
Nishi Parikh, Meera Karamta, Neha Yadav, et al.
Journal of Energy Chemistry (2021) Vol. 66, pp. 74-90
Closed Access | Times Cited: 44
Hybrid perovskites thin films morphology identification by adapting multiscale-SinGAN architecture, heat transfer search optimized feature selection and machine learning algorithms
Vinay Vakharia, Milind Shah, Venish Suthar, et al.
Physica Scripta (2022) Vol. 98, Iss. 2, pp. 025203-025203
Closed Access | Times Cited: 35
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