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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:

Performance analysis of perovskite solar cells in 2013–2018 using machine-learning tools
Çağla Odabaşı, Ramazan Yıldırım
Nano Energy (2018) Vol. 56, pp. 770-791
Closed Access | Times Cited: 116

Showing 1-25 of 116 citing articles:

Consensus statement for stability assessment and reporting for perovskite photovoltaics based on ISOS procedures
Mark Khenkin, Eugene A. Katz, Antonio Abate, et al.
Nature Energy (2020) Vol. 5, Iss. 1, pp. 35-49
Open Access | Times Cited: 1183
Minimizing non-radiative recombination losses in perovskite solar cells
Deying Luo, Rui Su, Wei Zhang, et al.
Nature Reviews Materials (2019) Vol. 5, Iss. 1, pp. 44-60
Closed Access | Times Cited: 1005
Machine learning for perovskite materials design and discovery
Qiuling Tao, Pengcheng Xu, Minjie Li, et al.
npj Computational Materials (2021) Vol. 7, Iss. 1
Open Access | Times Cited: 319
Machine learning for a sustainable energy future
Zhenpeng Yao, Yanwei Lum, Andrew Johnston, et al.
Nature Reviews Materials (2022) Vol. 8, Iss. 3, pp. 202-215
Open Access | Times Cited: 223
Predictions and Strategies Learned from Machine Learning to Develop High‐Performing Perovskite Solar Cells
Jinxin Li, Basudev Pradhan, Surya Gaur, et al.
Advanced Energy Materials (2019) Vol. 9, Iss. 46
Closed Access | Times Cited: 153
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: 81
AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS)
Honghao Chen, Yingzhe Zheng, Jiali Li, et al.
ACS Nano (2023) Vol. 17, Iss. 11, pp. 9763-9792
Closed Access | Times Cited: 48
Advancing perovskite solar cell commercialization: Bridging materials, vacuum deposition, and AI-assisted automation
Zhihao Xu, Sang‐Hyun Chin, Bo‐In Park, et al.
Next Materials (2024) Vol. 3, pp. 100103-100103
Open Access | Times Cited: 18
Advancements in Perovskites for Solar Cell Commercialization: A Review
Tejas Dhanalaxmi Raju, Vignesh Murugadoss, Kiran A. Nirmal, et al.
Advanced Powder Materials (2025), pp. 100275-100275
Open Access | Times Cited: 2
Machine-Learning-Accelerated Perovskite Crystallization
Jeffrey Kirman, Andrew Johnston, D.A. Kuntz, et al.
Matter (2020) Vol. 2, Iss. 4, pp. 938-947
Open Access | Times Cited: 122
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 knowledge discovery for heterogeneous catalysis using machine learning
M. Erdem Günay, Ramazan Yıldırım
Catalysis Reviews (2020) Vol. 63, Iss. 1, pp. 120-164
Closed Access | Times Cited: 86
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
Exploring the critical factors of algal biomass and lipid production for renewable fuel production by machine learning
Ahmet Coşgun, M. Erdem Günay, Ramazan Yıldırım
Renewable Energy (2020) Vol. 163, pp. 1299-1317
Closed Access | Times Cited: 81
Applying machine learning to boost the development of high-performance membrane electrode assembly for proton exchange membrane fuel cells
Rui Ding, Yiqin Ding, Hongyu Zhang, et al.
Journal of Materials Chemistry A (2021) Vol. 9, Iss. 11, pp. 6841-6850
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
Review of technology specific degradation in crystalline silicon, cadmium telluride, copper indium gallium selenide, dye sensitised, organic and perovskite solar cells in photovoltaic modules: Understanding how reliability improvements in mature technologies can enhance emerging technologies
Jeff Kettle, Mohammadreza Aghaei, Shahzada Ahmad, et al.
Progress in Photovoltaics Research and Applications (2022) Vol. 30, Iss. 12, pp. 1365-1392
Open Access | Times Cited: 55
Strategies for high‐performance perovskite solar cells from materials, film engineering to carrier dynamics and photon management
Huilin Li, Chong Chen, Hangyu Hu, et al.
InfoMat (2022) Vol. 4, Iss. 7
Open Access | Times Cited: 46
Machine‐Learning Modeling for Ultra‐Stable High‐Efficiency Perovskite Solar Cells
Yingjie Hu, Xiaobing Hu, Lu Zhang, et al.
Advanced Energy Materials (2022) Vol. 12, Iss. 41
Closed Access | Times Cited: 42
Accelerating perovskite materials discovery and correlated energy applications through artificial intelligence
Jiechun Liang, Tingting Wu, Ziwei Wang, et al.
Energy Materials (2022) Vol. 2, Iss. 3, pp. 200016-200016
Open Access | Times Cited: 39
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
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
The Future of Material Scientists in an Age of Artificial Intelligence
Ayman Maqsood, Chen Chen, T. Jesper Jacobsson
Advanced Science (2024) Vol. 11, Iss. 19
Open Access | Times Cited: 12
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
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